Reviewerly/exHarmony huggingface dataset:
Examining the Metaverse Technology Acceptance Among the Real-Estate Industry in the United Arab Emirates: A Case Study

Metaverse technology in business is a relatively new yet well-understood phenomenon. It has gained much popularity in real-estate organizations where clients rely on physical visits and direct observation to make favorable decisions. This research has also focused on the metaverse technology in the real estate sector in the United Arab Emirates. The researchers employed the case study method and proposed the explanatory framework supported by the Theory of Planned Behavior (TPB). Results showed that three factors, i.e., System Quality, Digital Information Flow, and Service Quality are significant factors that affect their Intention to Use Metaverse Technology for real-estate purposes. Additionally, despite the mediation of Behavioral Intention on the relationships between System Quality, Digital Information Flow, and Intention to Use Metaverse Technology was insignificant, the relevant indirect effect on the relationship between service quality and Intention to use Metaverse for real-estate purposes was significant. Thus, this study concluded that Metaverse technology is revolutionizing the real estate sector by facilitating faster and more secure transactions, more efficient communication, and the capacity to create immersive virtual experiences. It offers new real estate market possibilities and creative solutions for complex issues. It is still in its early stages, but the potential for innovation and transformation is immense.
A Lightweight NUet Model for Document Image Restoration

The automatic analysis of document images has important applications. However, the quality of document images often cannot meet the requirements, as device aging, improper operation or environmental factors can cause document images to contain noise and blur. Existing document image restoration techniques have high computational complexity. To address this issue, firstly, we proposed three new convolution modules: BSCM model, spindle shape module and hourglass shape module, which are all combining the spatial separable convolutions with the depthwise separable convolution. Then, we propose a lightweight document image restoration model (NUnet), which combines U-shape as backbone network and apply the proposed three new convolution modules to build the network. Finally, we evaluated the proposed model in Kaggle and GOPRO dataset. Experiments have confirmed that the proposed network model has a small number of parameters, and the visual restoration results are superior to other models.
A Rank Constrained Approach for Graph Learning from Spatio-Temporal Signals

In the task of graph learning, the presence of noise poses a significant challenge to accurately estimate the underlying graph structure. Current approaches often rely on nuclear norm regularization to exploit the low-rank properties of noiseless spatio-temporal signals. However, their performance depends on empirically estimated parameters and also suffers from slow convergence. In this paper, we propose a novel graph learning algorithm from spatio-temporal signals that establishes an upper bound on the rank of the signal matrix, which can be readily estimated. The alternating optimization strategy is employed to tackle the resulting problem. The symmetric adjacency matrix can be updated by solving an quadratic program, while the signal matrix can be effectively achieved by an alternating direction method of multipliers (ADMM). Experimental results show that our algorithm outperforms existing methods in terms of estimation accuracy while maintaining a fast convergence speed.
Federated Learning for Sleep Stage Classification on Edge Devices via a Model-Agnostic Meta-Learning-Based Pre-Trained Model

Sleep stage classification using physiological data obtained from consumer wearable devices has gained significant attention. However, data privacy regulations prevent the collection of individual data for training the models. Federated learning (FL) enables collaborative training across distributed networks of edge devices without sharing private data. This paper presents a federated learning approach that leverages computational resources of Internet of Thing (IoT) edge devices for sleep stage classification. Given the limited computational power and resources on edge devices, we evaluate the impact of model initialization in FL using pre-trained weights of model-agnostic meta-learning (MAML) models. We evaluate the performance of on-device sleep stage classification by comparing local models to different FL settings initialized with random weights (FL-Random) and pre-trained weights (FL-ML). FL-ML enables faster model convergence, yielding better classification performance (up to 22.6%) in a small number of training rounds, compared to local models. Starting from FL-ML reduces the training time required to reach a target accuracy and allows for the training of more accurate models, compared to initializing FL from random weights or using local models. Our study highlights the feasibility of FL initialized with pre-trained weights in real-world IoT settings for sleep stage classification on edge devices.
Predictive Coding Using Local Decoded Images with Different Degrees of Blurring

When the same object, both within a single picture and between pictures in a video sequence, exhibits varying degrees of blurring, coding efficiency is likely to be reduced due to a decrease in the accuracy of intra- and inter-picture predictions. To improve the coding efficiency in this case, we propose a method to perform such predictions using sharpened and blurred images in addition to conventional prediction using a local decoded image. These sharpened and blurred images are generated by enhancing and suppressing the high-frequency power of each coding unit in a local decoded image. The optimal prediction result is then selected from the local decoded image and its sharpened and blurred images for each coding unit using a rate-distortion optimization process. In experiments, the Bjøntegaard delta rate, a measure of coding efficiency improvement, was obtained by using ultra-high-definition video in which the degree of blurring varies within and between pictures. The analysis showed that the proposed method improved the coding efficiency for some image sequences.
Approximating Temporal Networks from Aggregated Network Data

Temporal network data is typically provided in the form of temporal edge lists, where pairs of connected nodes are associated with their interaction timestamps, enabling construction of network snapshots in accumulative or interval manners. For data privacy and/or data storage purposes, data can be instead available only in an aggregated form with the first- and last interaction times and the frequency. In this paper we address the problem of reconstructing temporal networks from aggregated network data. We propose an approach to derive approximate interaction times through linear interpolation over edge time-spans. Our experiments show the feasibility of constructing approximate temporal networks, where the errors are minimal for longer intervals and/or intervals closer to the boundaries of the system lifespan.
Metaheuristic Approach to Artificial Neural Network Optimization

The performance of Artificial Neural Networks (ANNs) depends on the tuning quality of the parameters, such as weights, learning rate, hyperparameters, number of layers and number of neurons. Typical tuning approaches include gradient descent based methods, which are applicable for smooth functions and perform at their best for convex problems. However, ANNs are non-convex and they may benefit from non-smooth functions. Thus, finding new innovative techniques to overcome these limitations is a priority for researchers in the field. Metaheuristic (MH) algorithms can be an alternate option to estimate the optimal parameters of ANNs. Since most MH algorithms have a high capability in exploration or exploitation of non-convex problems, developing customized MH algorithms will be a fruitful area for parameter search of ANNs. In this work, our focus is on developing a Variable Neighborhood Search (VNS) approach that overcomes the limitations of conventional methods in the parameter search of ANNs.
A comparative study of two calibration methodologies for a laser stripe sensor

A laser stripe sensor has two kinds of calibration methodologies. One is based on the homography model between the laser stripe plane and the image plane, which is called the one-step calibration methodology. The other is based on the simple triangular methodology, which is named as the two-step calibration methodology. However, the geometrical meaning of each element in the one-step calibration methodology isn’t clear as that is the two-step calibration methodology. A novel mathematical derivation is presented to reveal the geometrical meaning of each parameter in the one-step calibration methodology, and then the comparative study of the one-step calibration methodology and the two-step calibration methodology is completed and the intrinsic relationship is derived. What is more, an improved one-step calibration methodology is proposed with 7 independent parameters rather than 11 independent parameters. Experiments are conducted to verify the accuracy of their comparison and robust of the proposed calibration methodology.
Leveraging Pre-trained Language Models for Arabic Machine Reading Comprehension with Unanswerable questions

Machine Reading Comprehension (MRC) is a cru-cial task in natural language processing that enables machines to extract the minimal text span from a reading passage that answers a given question. Its importance is underscored in elevating the machine's capabilities to understand and process human language, which is crucial for various applications, from digital assistants to information retrieval systems. The availability of annotated datasets like SQuAD and the improvements in modeling techniques have recently driven significant progress in MRC. However, a critical limitation of traditional MRC approaches, particularly for non-English languages, is the as-sumption that every question has a corresponding answer in the reading passage. This assumption does not align with real-world scenarios where some questions are unanswerable using the given text, although some answers appear plausible. In this study, we evaluate the performance of several transformer-based models for Arabic MRC tasks that include both answerable and unan-swerable questions. These models are fine-tuned using an Arabic-translated version of the SQuADv2.0 dataset. Our investigation features a detailed analysis of the results, categorizing them by question types and tools. Notably, AraBERTv2.0-base stands out as the most effective model, achieving an exact match score of 84.20% and an F1-score of 82.5 %. The results also highlight AraBERTv2.0-base's distinct strength in accurately responding to answerable questions, which sets it apart from other models that are better at identifying unanswerable questions.
Load Curve Based on Modeling a Photovoltaic System, Load and Weather Data - a Case Study in Brazil

This study presents a comprehensive case study centered around the modeling of a load curve for a public education institution in Brazil that harnesses energy from distributed sources, specifically photovoltaic panels. Actual demand data, sourced from an energy analyzer, and weather data collected through an automated weather station, were integrated into the photovoltaic generation model. The model's efficacy was meticulously assessed, juxtaposing the actual and simulated photovoltaic generation data, which showcased a remarkable alignment with a percentage error of merely 6.88%. Furthermore, the study delved into weekly load curve simulations spanning the spring and summer seasons. These simulations yielded a compelling percentage error of 8.01%. These outcomes underscore the potential of the proposed load curve model, not only in strategic solar energy production planning but also in the strategic optimization of energy costs. This work not only underscores the feasibility of accurate load curve prediction but also illuminates the capacity to explore solar energy production potential, thereby opening avenues for future research in revenue generation and energy credit evaluation.
Performance Analysis of Comparison Operations on Homomorphically Encrypted Sensitive Data

Homomorphic encryption (HE) is one of the solutions to securely analyze and utilize sensitive client data such as purchase history and sensor data accumulated on cloud servers. HE permits the computation of encrypted data, such as addition and multiplication, without decryption. However, not all applications can run with HE because of restrictions on certain types of calculations. To perform matching or comparison operations in encrypted form, the plaintext must be encoded before encryption to enhance data usability. Depending on the encoding method, the execution time of the HE operations and ciphertext size may increase. Therefore, it is important to select an encoding method based on the purpose of the computations and data features. In this study, we performed comparison op-erations on homomorphically encrypted data using two different encoding methods: binary encoding and bitmask encoding. We demonstrate how computations can be executed using the TFHE scheme, which supports logical operations over encrypted data, and we evaluate the performance of the comparison operations using two different encoding methods.
A Telephone Consultation Support System for Pediatric Emergency Medical Care

Many Japanese parents live by themselves, and when their children have health problems they often don't have anyone to consult. Each local government in Japan has a pediatric emergency medical telephone consultation service. We developed a system to support the responders of this service. Our system consists of an interface and a chat engine: AI-Brid (Kimura Information Technology, Japan) as the interface, and Watson (IBM) for the chat engine. AI-Brid is a hybrid AI search system that combines one-question-and-one-answer and document search, each of which can be performed in the same search window. The administrator trains Watson with possible questions, answers, and document data in advance through AI-Brid. AI-Brid stores the ID, the assumed question, and the answer, and Watson stores the analysis result of the entered assumed question and the ID. The one-question-and-one-answer type includes case examples based on records that contain the questions and the responses to them, while the document search type includes basic knowledge for each case. We have completed construction of a prototype system that is currently in trial use in a real environment. Our ultimate goals are to improve the quality and to unify the responses.
Design and Analysis of an Anonymous and Fair Trading Scheme for Electronic Resources with Blind Adaptor Signature

With the development of the blockchain technology, blockchain is being widely used in finance, education, healthcare and other important fields, and its decentralized nature also brings new ideas for solving the problems of credit risk, uncertainty of transactions and imperfect regulatory system that exist in third-party e-commerce platforms. For the privacy security problem of electronic resource transactions in blockchain, this paper proposes an anonymous and fair transaction scheme for electronic resource, which is based on a new blind adaptor signature technology, the blind adaptor signature combines the atomicity of the adaptor signature and the blindness of the blind signature, which is applied to the payment channel, and the transactions are carried out in the payment channel, so that the establishment of the payment channel does not require a deposit, and the transactions are settled instantly and with fairness, and at the same time improve the privacy security, has a good application prospect in the field of electronic resource transaction.
Blockchain-Based Decentralized Product Lifecycle Management: A Framework for Production Network

The current global trade wars, rising logistics costs, and the impact of the Covid-19 pandemic have compelled companies to re-evaluate their production strategies. An increasingly popular approach to address these issues is decentralized production, which involves manufacturing products near to the customers to reduce logistics and warehousing expenses, improve lead times, and decrease carbon emissions. However, the decentralized nature of the production network necessitates a secure communication platform for stakeholders to access authentic data in real-time. In this regard, blockchain technology is identified as a promising solution due to its characteristics, which make it suitable for product data management across its lifecycle, especially in a decentralized production network. Therefore, this work proposes a conceptual framework for a blockchain-based product lifecycle management (PLM) system to ensure data provenance, security, authenticity, and trusted traceability in a decentralized production network. Moreover, this study highlights that there is currently no such practical model available for decentralized production scenario. Consequently, we argue that successful deployment of a blockchain-based network would require a collaborative approach that involves close interaction and cooperation among all stakeholders in the network.
Generating MPI Scene Representation with Self-Attention Network

Multi-plane Image (MPI) plays a crucial role in enabling efficient and unambiguous scene representation for view synthesis. Existing methods often suffer from artifacts and truncation issues in rendered views due to limited understanding of depth plane dependencies and semantics. We propose a Transformer-based algorithm for the MPI scene representation generation. Leveraging the self-attention, our approach captures global context, effectively modeling relationships between planes. We introduce a rendering method with stricter constraints for improved accuracy in predicting occluded regions. Experimental results demonstrate our method’s superiority, with a significant 9.86% improvement in Structural Similarity Index (SSIM) for synthesizing novel views. Perceptual image patch similarity (LPIPS) decreases remarkably by 53.54%, enhancing perceptual quality, while peak signal-to-noise ratio (PSNR) improves by 34.98%. These results validate our method’s effectiveness and high-quality MPI scene representation.
Low-light image enhancement based on U-Net and fusion strategy

Normal light images are a prerequisite for many visual tasks, making single image enhancement crucial in computer vision. While physical model-based approaches have demonstrated promising results in this domain at an early stage, they often produce undesired anomalies in their outputs, making it difficult to apply them universally across all scenarios. Conversely, learning-based methods yield more authentic outcomes. However, the enhancement capabilities of machine learning algorithms are limited by the lack of pairs of normal light images, and many algorithms focus on a single enhancement task, ignoring the role of high-level tasks in guiding enhancement. This paper introduces a low-light image enhancement algorithm rooted in a fusion strategy that consists of two subtasks, visibility restoration and realism improvement tasks, and combines the advantages of physical modeling and learning methods. In the visibility restoration stage, the fusion of features from upstream and downstream tasks enables our network to take full advantage of the guidance of the upstream task to the downstream task to recover results closer to nature; in the fidelity enhancement stage, the Retinex enhancer and the refinement enhancement network are introduced to enhance the fidelity of the image. Finally, the results of the two subtasks are fused to obtain the output. Experiments substantiate that the algorithm excels in terms of visual effects and objective metrics across both synthetic data and real-life scenes.
DFairNAS: A Dataflow Fairness Approach to Training NAS Neural Networks

Neural Architecture Search (NAS) is a technology that finds the best neural architecture from a large number of candidates. Recent researchers have proposed one-shot NAS for reducing search costs. One-shot NAS organizes a search space into a single large-scale network (i.e., supernet) and trains it as an estimator of the neural network candidates (i.e., subnetworks). However, one-shot NAS still faces an unfairness training challenge—the supernet can still have poor estimation performance, as it may be unfairly trained, especially when the search space is irregular. To tackle with this challenge, we propose DFairNAS, a dataflow fairness approach to fairly training supernets in general search spaces. DFairNAS trains supernets while keeping dataflow fairness; it improves the estimation performance of a supernet by organizing its operators into layers and requires all of the dataflows between two adjacent layers to be fairly trained. We evaluate DFairNAS against existing one-shot NAS approaches on the CIFAR datasets. The evaluation results show that DFairNAS outperforms FAIRNAS (a state-of-the-art operator fairness-based NAS approach) by 63.19% and 73.99% in the supernets’ estimation performance in regular and irregular search spaces, respectively; it also outperforms FAIRNAS by 70.3% on F-MATRIC, a novel metric for measuring training fairness. Furthermore, DFairNAS can find architectures with high accuracy without incurring heavy training costs.
Measurement based on geometry and feature shape by weighted multi-view stereo

Improving measurement accuracy has always been a hot topic in the field of computer vision. Due to the ability to provide redundant information from multiple perspectives, multi-camera systems provide higher measurement accuracy and stronger measurement robustness. With the differences in structural parameters, imaging quality, and object imaging shape of different cameras, the contribution of each camera to 3D point reconstruction should also be different. However, traditional methods treat the contribution of each camera to 3D point reconstruction as the same, which reduces measurement accuracy. In this paper, we comprehensively consider camera calibration error, key point extraction error caused by feature shape, geometry error of the vision system. On this basis, the camera weights are calculated appropriately. A five-camera system is established for accuracy verification. Compared with the traditional least squares method, the diameter measurement accuracy can be improved by 6.330% and the center distance accuracy can be improved by 7.328% with reasonable weighting.
Enhancing Local Information and Long-Distance Dependencies for Finer Monocular Depth Estimation

Recent studies have highlighted the remarkable effectiveness of Vision Transformer (ViT) on the monocular depth estimation (MDE) task. Although ViT can model the long-distance dependence between pixels and handle irregular sampling through the self-attention mechanism, it lacks inductive bias characteristics and cannot effectively capture local and global spatial information, resulting in errors in the estimated depth map at the boundaries and contours. To address this, our paper introduces a hybrid architecture that combines the strengths of ViT and Convolutional Neural Network (CNN) to fully enhance local information and long-distance dependencies. Specifically, we propose a novel patch-attention mechanism (PA), which aggregates features along vertical and horizontal directions, respectively. Through extensive experiments on various datasets, including KITTI, DIW, DIODE, and Sintel, our architecture achieves more efficient and representative patches before embedding, leading to significant performance improvements of up to 17.6% compared to the state-of-the-art benchmarks in the MDE task.