Reviewerly/exHarmony huggingface dataset:
A Bibliometric Analysis of Augmented Reality in Language Learning

As an emerging technology, augmented reality has been increasingly entering the educational field. Previous studies of AR are strongly associated with scientific education but lack the mention of language learning. This study aimed to analyze the use of AR tools in language learning contexts using the bibliometric tools VOSviewer and CitNetExplorer using both qualitative and quantitative research methods. This study analyzed the top ten authors, sources, countries, and organizations using VOSviewer and established citation networks using CitNetExplorer. The study found that both teachers and students showed a positive attitude toward AR tools in language learning. Games, 3D images, and videos are the main ways to instantiate virtual elements into the real world, and the most widely used tool in AR-assisted learning was HP Reveal. Moreover, the study also found that AR tools could enhance language learning by presenting an immersive learning context, increasing motivation, providing interaction, and reducing anxiety. Future research will contribute to how AR tools influence students’ learning performance and teachers’ teaching activities.
Web3 toolkits: A user innovation theory of crypto development

The development of Web3 — a stack of decentralised technologies underpinned by blockchains — isn't simply a technical or financing challenge, it is also a problem of innovation and entrepreneurial discovery. In this paper we apply the lens of user innovation toolkit theory to the development of Web3. Toolkits are an organizational design solution to an innovation problem with sticky and local information. Our aim is to explore how toolkits theory applies to Web3 innovation, proposing that Web3 innovation is being organized through toolkits (e.g., blockchains, token standards, DAO frameworks) that enable efficient organization of sticky information to facilitate innovation. The contribution of this paper is the first application of toolkits theory to Web3, reframing its development as a problem of entrepreneurship and innovation in the context of distributed information. We provide implications for the role of developers as user innovators, the economic problem of emergent toolkit stacking, and the design of toolkit business models.
Evaluating the adoption of cybersecurity and its influence on organizational performance

Cyberattacks negatively impact the performance of enterprises all around the globe. While organizations invest more in cybersecurity to avoid cyberattacks, studies on the factors affecting their overall cybersecurity adoption and awareness are sparse. In this paper, by integrating the diffusion of innovation theory (DOI), technology acceptance model (TAM), and technology-organization-environment (TOE) with the balanced scorecard approach, we propose a comprehensive set of factors that influence cybersecurity adoption and assess the effects of these factors on organizational performance. Data are collected through a survey of IT experts in small and medium-sized enterprises (SMEs) in the United Kingdom, with 147 valid responses. Structural equation modeling based on a statistical package for the social sciences (SPSS) was used to assess the model. The findings identify and confirm the importance of eight factors affecting SMEs' cybersecurity adoption. Moreover, cybersecurity technology adoption is found to positively impacts organizational performance. The proposed framework depicts variables influencing cybersecurity technology adoption and assesses their importance. The outcomes of this study provide a basis for future research and can be adopted by IT and cybersecurity managers to identify the most appropriate cybersecurity technologies that positively impact their company's performance.
A Method for Face Image Inpainting Based on Autoencoder and Generative Adversarial Network

Face image inpainting has great value in the fields of computer vision and digital image processing. In this paper, we propose a face image inpainting method based on autoencoder and Generative Adversarial Network (GAN). The neural network for image inpainting consists of two parts, a generator and a discriminator. The autoencoder is used twice in the discriminator part, after the final inpainted image is generated by local discriminator and global discriminator. The final loss function is obtained by combining Generative Adversarial Loss and Mean Squared Error (MSE) Loss [20]. We improve and implement an image inpainting model with two evaluation metrics, namely, Peak Signal-to-noise Ratio (PSNR) and Structural similarity index measure (SSIM) [27], respectively. The proposed model for image inpainting is much more suitable for face image inpainting.
VP-Net: Voxels as Points for 3-D Object Detection

3D object detection with LiDAR point clouds is a challenging problem which requires 3D scene understanding, yet this task is critical to autonomous driving. Existing voxel-based 3D object detectors are becoming increasingly popular but have several shortcomings. For example, during voxelization, features of distant sparse point clouds are largely discarded, which leads to the missing detection of objects. Additionally, the correlation of points between voxels and the importance of different voxels within a region are not well learned. Therefore, we present a robust network (VP-Net) that views voxels as points to accurately detect 3D objects in LiDAR point clouds and can capture objects’ internal relationships. 3D CNN processing shows the output features of VP-Net as key points. The relationship between key points is then constructed into local graphs to enhance object feature extraction via a self-attention mechanism. Finally, the Euclidean distance between the extracted features guides our model’s weight reassignment for strengthening the importance of neighbor points, thereby enhancing the internal feature aggregation of objects. Experiments on KITTI and nuScenes 3D object detection benchmarks demonstrate the efficiency of enhancing inter-voxel validity within object features and show that the proposed VP-Net can achieve state-of-the-art performance.
Modified BB84 quantum key distribution protocol robust to source imperfections

The Bennett-Brassard 1984 (BB84) protocol is the most widely implemented quantum key distribution (QKD) scheme. However, despite enormous theoretical and experimental efforts in the past decades, the security of this protocol with imperfect sources has not yet been rigorously established. In this paper, we address this shortcoming and prove the security of the BB84 protocol in the presence of multiple source imperfections, including state preparation flaws and side channels, such as Trojan-horse attacks, mode dependencies and classical correlations between the emitted pulses. To do so, we consider a modified BB84 protocol that exploits the basis mismatched events, which are often discarded in standard security analyses of this scheme; and employ the reference technique, a powerful mathematical tool to accommodate source imperfections in the security analysis of QKD. Moreover, we compare the achievable secret-key rate of the modified BB84 protocol with that of the three-state loss-tolerant protocol, and show that the addition of a fourth state, while redundant in ideal conditions, significantly improves the estimation of the leaked information in the presence of source imperfections, resulting in a better performance. This paper demonstrates the relevance of the BB84 protocol in guaranteeing implementation security, taking us a step further towards closing the existing gap between theory and practice of QKD.
Proxy smart contracts for zero trust architecture implementation in Decentralised Oracle Networks based applications

Implementation of blockchain is quickly finding popularity in several real-world applications. As a result, in order to execute and process smart contracts, blockchains frequently necessitate integrating them with real-world data from various sources or employing third-party services. Through hybrid smart contracts, Decentralized Oracle Networks (DONs) make it possible for on-chain code to communicate with off-chain infrastructure and services, assisting in the realisation of this vision. Although the blockchain itself is a secure ecosystem, its reliance on external services and real-world data exposes it to security risks. These dangers reveal themselves in improper smart contract execution as a result of malevolent actors manipulating data from outside sources. Therefore, it is necessary to design a Zero Trust Architecture to provide end-to-end security for DON-based applications. The idea of Proxy Smart Contracts (PSC), which offers a one-level indirection to the execution of the actual smart contracts, is introduced in this article. Prior to the actual smart contract execution, the PSC hypothetically runs the smart contract logic and displays the result state to the interested parties for their approval. Thus, inconsistent end states can be identified and the smart contract's actual execution can be stopped. Solidity is used in the initial implementation to show that the suggested strategy is workable. The current work is significant since it presents a potential remedy to overcome this crucial gap that can limit widespread acceptance of dApps-based use-cases in the real world.
Learning to rank and predict: Multi-task learning for ad hoc retrieval and query performance prediction

The ad hoc retrieval task aims at ranking relevant documents to a user query such that the most relevant documents are ranked higher compared to less relevant ones. Given the performance of the ad hoc retrieval task can vary across a range of queries, researchers have extensively explored the interrelated task of query performance prediction, which aims at estimating the quality of the search results for a user query without having access to relevance judgments. Traditionally and to-date, the two tasks have been explored as separate tasks where ad hoc retrieval and query performance prediction have been performed in isolation. In this paper, we propose to learn joint tasks that would perform ad hoc retrieval and at the same time predict the quality of the produced rankings. More specifically, we propose a multi-task learning approach, called Multi-task Query Performance Prediction Framework (M-QPPF), which learns document ranking and query performance prediction tasks simultaneously. In M-QPPF, we adopt a shared BERT layer, which is fine-tuned to learn representations for query-document pairs in the embedding space such that the representations effectively encode the cross-interaction between the query and documents. In addition, we include additional yet separate layers to capture task-specific characteristics. We perform comprehensive experiments against state-of-the-art methods in the query performance prediction and ranking tasks over large-scale datasets including the MS MARCO and TREC DL datasets. The improvements measure up to 18.8% on MS MARCO dataset with a Pearson correlation of 0.604, which is superior to the performance of any state-of-the-art baseline in the query performance prediction task.
Attribute-based searchable encrypted scheme with edge computing for Industrial Internet of Things

Storing the data generated in the Industrial Internet of Things has become a method to solve data redundancy. Still, data security and privacy protection have become an issue that cannot ignore. Attribute-based searchable encryption can encrypt and retrieve data without exposing users' privacy. In this paper, we introduce an attribute-based searchable scheme combining online/offline encryption and outsourcing decryption technology, which uses a reusable ciphertext pool to reduce the computation in the encryption stage, and uses an edge server to reduce the cost of resource-constrained users through outsourcing decryption. To improve the solution's security, the specified server can authenticate the cloud server in the searchable location. Finally, our scheme is proven to satisfy CCA2-secure and is efficient in performance and safety analysis. In a word, the algorithm is suitable for the actual Industrial Internet of Things environment.
Bandit-based data poisoning attack against federated learning for autonomous driving models

In Internet of Things (IoT) applications, federated learning is commonly used for distributedly training models in a privacy-preserving manner. Recently, federated learning is broadly applied to autonomous driving for training intelligent decision models without disseminating local data remotely. Although federated learning provides a safer training manner for protecting data privacy in autonomous driving, the model training process is still vulnerable to poisoning attacks from vehicle client ends. It is beneficial to study poisoning attacks for enhancing the robustness of the training process to generate reliable decisions for safe driving. Until now, a few researches on poisoning attacks against classification models under federated learning scenarios have been proposed. However, those poisoning attacks against classification tasks cannot be directly applied to regression tasks in a federated learning framework, especially autonomous driving tasks such as steering angle control and brake control. The biggest challenge is that the output of non-linear regression models in federated learning is a dynamic sequential value decided by an online updated non-linear function. Thus, minor attacks can affect the overall non-linear function inference outputs leading to a failed stealthy attack. Based on existing challenges, this paper proposes an ATTack against Federated Learning based Autonomous Vehicle framework(ATT_FLAV) to evaluate and enhance the robustness of the federated learning-based autonomous driving models and take the steering angle control task as a representative non-linear regression task to illustrate the methodology. In the proposed framework, a bandit-based AttackRegion-UCB (AR-UCB) algorithm is designed for dynamic data poisoning attacks against the non-linear regression model. This is a black-box attack strategy that chooses the target attack label region dynamically in each federated learning round based on historical attack experiences. Compared with the attack performance of baseline poisoning attacks and the robustness under defense schemas, the proposed poisoning attack strategy can achieve superior attack performance via continuous data poisoning attacks against the federated learning framework.
En–DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis

Liver cancer ranks as the sixth most prevalent cancer among all cancers globally. Computed tomography (CT) scanning is a non-invasive analytic imaging sensory system that provides greater insight into human structures than traditional X-rays, which are typically used to make the diagnosis. Often, the final product of a CT scan is a three-dimensional image constructed from a series of interlaced two-dimensional slices. Remember that not all slices deliver useful information for tumor detection. Recently, CT scan images of the liver and its tumors have been segmented using deep learning techniques. The primary goal of this study is to develop a deep learning-based system for automatically segmenting the liver and its tumors from CT scan pictures, and also reduce the amount of time and labor required by speeding up the process of diagnosing liver cancer. At its core, an Encoder-Decoder Network (En-DeNet) uses a deep neural network built on UNet to serve as an encoder, and a pre-trained EfficientNet to serve as a decoder. In order to improve liver segmentation, we developed specialized preprocessing techniques, such as the production of multichannel pictures, de-noising, contrast enhancement, ensemble, and the union of model predictions. Then, we proposed the Gradational modular network (GraMNet), which is a unique and estimated efficient deep learning technique. In GraMNet, smaller networks called SubNets are used to construct larger and more robust networks using a variety of alternative configurations. Only one new SubNet modules is updated for learning at each level. This helps in the optimization of the network and minimizes the amount of computational resources needed for training. The segmentation and classification performance of this study is compared to the Liver Tumor Segmentation Benchmark (LiTS) and 3D Image Rebuilding for Comparison of Algorithms Database (3DIRCADb01). By breaking down the components of deep learning, a state-of-the-art level of performance can be attained in the scenarios used in the evaluation. In comparison to more conventional deep learning architectures, the GraMNets generated here have a low computational difficulty. When associated with the benchmark study methods, the straight forward GraMNet is trained faster, consumes less memory, and processes images more rapidly.
Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques

Accurate solar radiation nowcasting models are critical for the integration of the increasing solar energy in power systems. This work explored the benefits obtained by the blending of four all-sky-imagers (ASI)-based models, two satellite-images-based models and a data-driven model. Two blending approaches (general and horizon) and two blending models (linear and random forest (RF)) were evaluated. The relative contribution of the different forecasting models in the blended-models-derived benefits was also explored. The study was conducted in Southern Spain; blending models provide one-minute resolution 90 min-ahead GHI and DNI forecasts. The results show that the general approach and the RF blending model present higher performance and provide enhanced forecasts. The improvement in rRMSE values obtained by model blending was up to 30% for GHI (40% for DNI), depending on the forecasting horizon. The greatest improvement was found at lead times between 15 and 30 min, and was negligible beyond 50 min. The results also show that blending models using only the data-driven model and the two satellite-images-based models (one using high resolution images and the other using low resolution images) perform similarly to blending models that used the ASI-based forecasts. Therefore, it was concluded that suitable model blending might prevent the use of expensive (and highly demanding, in terms of maintenance) ASI-based systems for point nowcasting.
Investigating the Relationship between Users’ Behavioral Intentions and Learning Effects of VR System for Sustainable Tourism Development

Macao is a tourist city. It is home to the Ruins of Saint Paul’s, a unique 100-year-old landmark, which is still standing with manual maintenance, even after three fires and reconstruction events. Therefore, the continuous preservation of its culture, heritage education, and construction are important issues for Macao. With the development of digital technology in recent years, users can quickly search historical sites and save two-dimensional and three-dimensional images and videos through smartphones. These methods also enhance the communication power of culture. Virtual browsing on a smartphone requires computing power and storage space; yet, virtual reality devices are not widely used. Therefore, augmented reality and virtual reality are rarely used simultaneously for three-dimensional interactive guided tours and operation experiences on the same theme. However, by quickly creating virtual reality scenarios and preserving historical sites on mobile devices, 4DAGE’s 4DKanKan technology can provide augmented reality and metaverse virtual reality experiences. 4DKanKan can also integrate mobile guides and navigation software to connect mobile devices and assist in cultural inheritance and conduct sustainable education. This research linked this technology to the web by incorporating augmented reality and virtual reality technology to make designs and discussed the influences among service design, behavioral intentions, and learning effects. We collated and analyzed relevant data and text materials through systematic testing, observation, operation processes, and semi-structured interviews. The PLS multigroup structural model was used to explore and analyze the degree of influence and explanatory power of system quality, information quality, behavioral intention, and learning effects among themselves. The results of this study show that most users accepted the proposed innovative mode of operation and found it to be interesting and fun. Augmented reality is not limited by space or time; however, virtual reality devices taking too long to operate, switching too frequently, and having too many functional interfaces can cause operational problems. This study identified and modified the influencing factors and problems of the proposed system, with the aim of continuing to expand the applications of 4DKanKan to other cultural attractions or museums in the future. In addition, the research results can provide a reference for the sustainable development of related cultural sites.
Fast Helmet and License Plate Detection Based on Lightweight YOLOv5

The integrated fast detection technology for electric bikes, riders, helmets, and license plates is of great significance for maintaining traffic safety. YOLOv5 is one of the most advanced single-stage object detection algorithms. However, it is difficult to deploy on embedded systems, such as unmanned aerial vehicles (UAV), with limited memory and computing resources because of high computational load and high memory requirements. In this paper, a lightweight YOLOv5 model (SG-YOLOv5) is proposed for the fast detection of the helmet and license plate of electric bikes, by introducing two mechanisms to improve the original YOLOv5. Firstly, the YOLOv5s backbone network and the Neck part are lightened by combining the two lightweight networks, ShuffleNetv2 and GhostNet, included. Secondly, by adopting an Add-based feature fusion method, the number of parameters and the floating-point operations (FLOPs) are effectively reduced. On this basis, a scene-based non-truth suppression method is proposed to eliminate the interference of pedestrian heads and license plates on parked vehicles, and then the license plates of the riders without helmets can be located through the inclusion relation of the target boxes and can be extracted. To verify the performance of the SG-YOLOv5, the experiments are conducted on a homemade RHNP dataset, which contains four categories: rider, helmet, no-helmet, and license plate. The results show that, the SG-YOLOv5 has the same mean average precision (mAP0.5) as the original; the number of model parameters, the FLOPs, and the model file size are reduced by 90.8%, 80.5%, and 88.8%, respectively. Additionally, the number of frames per second (FPS) is 2.7 times higher than that of the original. Therefore, the proposed SG-YOLOv5 can effectively achieve the purpose of lightweight and improve the detection speed while maintaining great detection accuracy.