Reviewerly/exHarmony huggingface dataset:
A comparative evaluation of behavioral security motives: Protection, intrinsic, and identity motivations

This study examines the contrast between three motivation theories (identity, protection, and intrinsic) and their impact on two forms of security behaviors (in-role and extra-role). The information security literature has informed much of our understanding of how individuals are motivated to perform security behaviors, with a predominant focus on the Protection Motivation and Intrinsic Motivation theories. We draw from recent insights from the Identity Theory and its explanation of behaviors in the workplace to compare motivations toward in-role and extra-role security behaviors. Whereas identity is influential in positively motivating in-role and extra-role behaviors, intrinsic motivation does not influence in-role behaviors, and protection motivation reduces extra-role behaviors. These results provide insights on the role that identity plays as a capable motivation with implications for research and practice.
Dual-channel graph contrastive learning for self-supervised graph-level representation learning

Self-supervised graph-level representation learning aims to learn discriminative representations for subgraphs or entire graphs without human-curated labels. Recently, graph contrastive learning (GCL) methods have revolutionized this field and achieved state-of-the-art results in various downstream tasks. Nonetheless, current GCL models are mostly based on simple node-level information aggregation operations and fail to reveal various substructures from input graphs. Moreover, to perform graph-graph contrastive training, they often involve well-designed graph augmentation, which is expensive and requires extensive expert efforts. Here, we propose a novel GCL framework, namely DualGCL, for self-supervised graph-level representation learning. For fine-grained local information incorporation, we first present an adaptive hierarchical aggregation process with a differentiable Transformer-based aggregator. Then, to efficiently learn graph-level discriminative representations, we introduce a dual-channel contrastive learning process in a multi-granularity and augmentation-free contrasting mode. When tested empirically on six popular graph classification benchmarks, our DualGCL achieves better or comparable performance than various strong baselines.
State-Independent Nonadiabatic Geometric Quantum Gates

Quantum computation has demonstrated advantages over classical computation for special hard problems, where a set of universal quantum gates is essential. Geometric phases, which have built-in resilience to local noise, have been used to construct quantum gates with excellent performance. However, this advantage has been smeared in previous schemes. Here, we propose a state-independent nonadiabatic geometric quantum-gate scheme that is able to realize a more fully geometric gate than previous approaches, allowing for the cancelation of dynamical phases accumulated by an arbitrary state. Numerical simulations demonstrate that our scheme has significantly stronger gate robustness than the previous geometric and dynamical ones. Meanwhile, we give a detailed physical implementation of our scheme with the Rydberg atom system based on the Rydberg blockade effect, specifically for multiqubit control-phase gates, which exceeds the fault-tolerance threshold of multiqubit quantum gates within the considered error range. Therefore, our scheme provides a promising way for fault-tolerant quantum computation in atomic systems.
Named Entity Recognition Model Based on Feature Fusion

Named entity recognition can deeply explore semantic features and enhance the ability of vector representation of text data. This paper proposes a named entity recognition method based on multi-head attention to aim at the problem of fuzzy lexical boundary in Chinese named entity recognition. Firstly, Word2vec is used to extract word vectors, HMM is used to extract boundary vectors, ALBERT is used to extract character vectors, the Feedforward-attention mechanism is used to fuse the three vectors, and then the fused vectors representation is used to remove features by BiLSTM. Then multi-head attention is used to mine the potential word information in the text features. Finally, the text label classification results are output after the conditional random field screening. Through the verification of WeiboNER, MSRA, and CLUENER2020 datasets, the results show that the proposed algorithm can effectively improve the performance of named entity recognition.
One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving

The core obstacle towards a large-scale deployment of autonomous vehicles currently lies in the long tail of rare events. These are extremely challenging since they do not occur often in the utilized training data for deep neural networks. To tackle this problem, we propose the generation of additional synthetic training data, covering a wide variety of corner case scenarios. As ontologies can represent human expert knowledge while enabling computational processing, we use them to describe scenarios. Our proposed master ontology is capable to model scenarios from all common corner case categories found in the literature. From this one master ontology, arbitrary scenario-describing ontologies can be derived. In an automated fashion, these can be converted into the OpenSCENARIO format and subsequently executed in simulation. This way, also challenging test and evaluation scenarios can be generated.
Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation

Existing spectral reconstruction methods learn discrete mappings from spectrally downsampled measurements (e.g., RGB images) to a specific number of spectral bands. However, they generally neglect the continuous nature of the spectral signature and only reconstruct specific spectral bands due to the intrinsic limitation of discrete mappings. In this paper, we propose a novel continuous spectral reconstruction network with implicit neural representation, which enables spectral reconstruction of arbitrary band numbers for the first time. Specifically, our method takes an RGB image and a set of wavelengths as inputs to reconstruct the spectral image with arbitrary bands, where the RGB image provides the context of the scene and the wavelengths provide the target spectral coordinates. To exploit the spectral-spatial correlation in implicit neural representation, we devise a spectral profile interpolation module and a neural attention mapping module, which exploit and aggregate the spatial-spectral correlation of the spectral image in multiple dimensions. Extensive experiments demonstrate that our method not only outperforms existing discrete spectral reconstruction methods but also enables spectral reconstruction of arbitrary and even extreme band numbers beyond the training samples.
Detecting Phishing Accounts on Ethereum Based on Transaction Records and EGAT

In recent years, the losses caused by scams on Ethereum have reached a level that cannot be ignored. As one of the most rampant crimes, phishing scams have caused a huge economic loss to blockchain platforms and users. Under these circumstances, to address the threat to the financial security of blockchain, an Edge Aggregated Graph Attention Network (EGAT) based on the static subgraph representation of the transaction network is proposed. This study intends to detect Ethereum phishing accounts through the classification of transaction network subgraphs with the following procedures. Firstly, the accounts are used as nodes and the flow of transaction funds is used as directed edges to construct the transaction network graph. Secondly, the transaction record data of phishing accounts in the publicly available Ethereum are analyzed and statistical features of Value, Gas, and Timestamp values are manually constructed as node and edge features of the graph. Finally, the features are extracted and classified using the EGAT network. According to the experimental results, the Recall of the proposed method from the article is 99.3% on the dataset of phishing accounts. As demonstrated, the EGAT is more efficient and accurate compared with Graph2Vec and DeepWalk, and the graph structure features can express semantics better than manual features and simple transaction networks, which effectively improves the performance of phishing account detection.
A novel DL-based algorithm integrating medical knowledge graph and doctor modeling for Q&amp;A pair matching in OHP

Using AI technology to automatically match Q&A pairs on online health platforms (OHP) can improve the efficiency of doctor-patient interaction. However, previous methods often neglected to fully exploit rich information contained in OHP, especially the medical expertise that could be leveraged through medical text modeling. Therefore, this paper proposes a model named MKGA-DM-NN, which first uses the named entities of the medical knowledge graph (KG) to identify the intention of the problem, and then uses graph embedding technology to learn the representation of entities and entity relationships in the KG. The proposed model also employs the relationship between entities in KG to optimize the hybrid attention mechanism. In addition, doctors' historical Q&A records on OHP are used to learn modeling doctors' expertise to improve the accuracy of Q&A matching. This method is helpful to bridge the semantic gap of text and improve the accuracy and interpretability of medical Q&A matching. Through experiments on a real dataset from a Chinese well-known OHP, our model has been verified to be superior to the baseline models. The accuracy of our model is 4.4% higher than the best baseline model. The cost-sensitive error of our model is 13.53% lower than that of the best baseline model. The ablation experiment shows that the accuracy rate can be significantly improved by 8.72% by adding the doctor modeling module, and the cost-sensitive error can be significantly reduced by 17.27% by adding the medical KG module.
Label modification and bootstrapping for zero-shot cross-lingual hate speech detection

The goal of hate speech detection is to filter negative online content aiming at certain groups of people. Due to the easy accessibility and multilinguality of social media platforms, it is crucial to protect everyone which requires building hate speech detection systems for a wide range of languages. However, the available labeled hate speech datasets are limited, making it difficult to build systems for many languages. In this paper we focus on cross-lingual transfer learning to support hate speech detection in low-resource languages, while highlighting label issues across application scenarios, such as inconsistent label sets of corpora or differing hate speech definitions, which hinder the application of such methods. We leverage cross-lingual word embeddings to train our neural network systems on the source language and apply them to the target language, which lacks labeled examples, and show that good performance can be achieved. We then incorporate unlabeled target language data for further model improvements by bootstrapping labels using an ensemble of different model architectures. Furthermore, we investigate the issue of label imbalance in hate speech datasets, since the high ratio of non-hate examples compared to hate examples often leads to low model performance. We test simple data undersampling and oversampling techniques and show their effectiveness.
An Experimentally Verified Attack on 820-Round Trivium

The cube attack is one of the most important cryptanalytic techniques against Trivium. As the method of recovering superpolies becomes more and more effective, another problem of cube attacks, i.e., how to select cubes that can effectively attack, is attracting more and more attention. In this paper, we present a novel framework to search for valuable cubes whose superpolies have an independent secret variable each, i.e., a linear variable not appearing in any nonlinear term. To control online complexity, valuable cubes are selected from very few large cubes. New ideas are given on the large cube construction and the subcube sieve. As illustrations, we apply the new algorithm to the stream cipher Trivium. For 815-round Trivium, the complexity of full key-recovery attack is $$2^{47.32}$$ . For 820-round Trivium, the complexity of full key-recovery attack is $$2^{53.17}$$ . Strong experimental evidence shows that the full key-recovery attacks on 815- and 820-round Trivium could be completed within six hours and two weeks on a PC with two RTX3090 GPUs, respectively.
DeFi Survival Analysis: Insights into Risks and User Behaviors

We propose a decentralized finance (DeFi) survival analysis approach for discovering and characterizing user behavior and risks in lending protocols. We demonstrate how to gather and prepare DeFi transaction data for survival analysis. We demonstrate our approach using transactions in AAVE, one of the largest lending protocols. We develop a DeFi survival analysis pipeline which first prepares transaction data for survival analysis through the selection of different index events (or transactions) and associated outcome events. Then we apply survival analysis statistical and visualization methods such as median survival times, Kaplan–Meier survival curves, and Cox hazard regression to gain insights into usage patterns and risks within the protocol. We show how by varying the index and outcome events, we can utilize DeFi survival analysis to answer three different questions. What do users do after a deposit? How long until borrows are first repaid or liquidated? How does coin type influence liquidation risk? The proposed DeFi survival analysis can easily be generalized to other DeFi lending protocols. By defining appropriate index and outcome events, DeFi survival analysis can be applied to any cryptocurrency protocol with transactions.
Improved YOLOv5s model for key components detection of power transmission lines

High-voltage transmission lines are located far from the road, resulting in inconvenient inspection work and rising maintenance costs. Intelligent inspection of power transmission lines has become increasingly important. However, subsequent intelligent inspection relies on accurately detecting various key components. Due to the low detection accuracy of key components in transmission line image inspection, this paper proposed an improved object detection model based on the YOLOv5s (You Only Look Once Version 5 Small) model to improve the detection accuracy of key components of transmission lines. According to the characteristics of the power grid inspection image, we first modify the distance measurement in the k-means clustering to improve the anchor matching of the YOLOv5s model. Then, we add the convolutional block attention module (CBAM) attention mechanism to the backbone network to improve accuracy. Finally, we apply the focal loss function to reduce the impact of class imbalance. Our improved method's mAP (mean average precision) reached 98.1%, the precision reached 97.5%, the recall reached 94.4% and the detection rate reached 84.8 FPS (frames per second). The experimental results show that our improved model improves the detection accuracy and has advantages over other models in performance.
Teacher-reported instructional quality in the context of technology-enhanced teaching: The role of teachers’ digital competence-related beliefs in empowering learners

Teachers' competence-related beliefs are crucial in determining teachers' professional competence and instructional quality. However, in the context of technology-enhanced teaching, studies regarding competence-related beliefs, specifically competence dimensions and their relations to instructional quality, are limited. Against this backdrop, the purpose of the present study is twofold: The first is to test the factorial structure of a measure that assesses teachers' competence-related beliefs in the dimension of empowering learners with its subdimensions of differentiation and actively engaging learners based on the European Framework for the Digital Competence of Educators (DigCompEdu). The second is to examine the relations between competence-related beliefs and teachers' reported use of digital technologies to enhance classroom management, cognitive activation, and supportive climate. Based on data from 145 teachers (73.1% female), both the factorial structure and the relations were analyzed using various approaches of structural equation modeling. The results supported the bifactor confirmatory factor analysis model with a general factor defined as empowering learners as well as two subdimensions representing differentiation and actively engaging learners. Teachers' competence-related beliefs regarding differentiation and empowering learners are positively related to the teacher-reported use of technologies to enhance instructional quality. Understanding the structure of teachers' competence-related beliefs about empowering learners and their relations to instructional quality in technology-enhanced teaching is relevant for teacher education to address learners' diverse learning needs, as well as to promote active and creative engagement.
Cross-scale cascade transformer for multimodal human action recognition

Human action recognition can benefit from multimodal information to address the classification problem under complex situations. However, existing works either use score fusion or perform simple feature integration methods to combine multiple heterogeneous modalities which failed to effectively utilize multimodal complementary information. In this paper, we proposed a Cross-Scale Cascade Multimodal Fusion Transformer (CSCMFT) to perform interaction and fusion among modalities of multi-scale features, thus obtaining a multimodal complementary representation for RGB-D-based human action recognition. Cross-Modal Cross-Scale Mixer (CCM) is the basic component in CSCMFT, which captures cross-modal relations and propagates the fused information across scales. Furthermore, our CSCMFT can still achieve significant improvements when applied to different multimodal combinations, indicating its generality and scalability. Experimental results show that CSCMFT fully exploits complementary semantic information between RGB and depth maps and outperforms state-of-the-art RGB-D-based methods on NTU RGB+D 60 & 120 and PKU-MMD datasets.
A Novel Intelligent Detection Algorithm of Aids to Navigation Based on Improved YOLOv4

Aiming at the problem of high-precision detection of AtoN (Aids to Navigation, AtoN) in the complex inland river environment, in the absence of sufficient AtoN image types to train classifiers, this paper proposes an automatic AtoN detection algorithm Aids-to-Navigation-YOLOv4 (AN-YOLOv4) based on improved YOLOv4 (You Only Look Once, Yolo). Firstly, aiming at the problem of an insufficient number of existing AtoN datasets, the Deep Convolutional Generative Adversarial Networks (DCGAN) is used to expand and enhance the AtoN image dataset. Then, aiming at the problem of small target recognition accuracy, the image pyramid is used to multi-scale zoom the dataset. Finally, the K-means clustering algorithm is used to correct the candidate box of AN-YOLOv4. The test on the test dataset shows that the improvement effect of AN-YOLOv4 is obvious. The accuracy rate of small targets is 92%, and the average accuracy (mAP) of eight different types of AtoN is 92%, which is 14% and 13% higher than the original YOLOv4, respectively. This research has important theoretical significance and reference value for the intelligent perception of the navigation environment under the intelligent shipping system.
Photovoltaic Power Prediction Based on VMD-BRNN-TSP

Overfitting often occurs in neural network training, and neural networks with higher generalization ability are less prone to this phenomenon. Aiming at the problem that the generalization ability of photovoltaic (PV) power prediction model is insufficient, a PV power time-sharing prediction (TSP) model combining variational mode decomposition (VMD) and Bayesian regularization neural network (BRNN) is proposed. Firstly, the meteorological sequences related to the output power are selected by mutual information (MI) analysis. Secondly, VMD processing is performed on the filtered sequences, which is aimed at reducing the non-stationarity of the data; then, normalized cross-correlation (NCC) and signal-to-noise ratio (SNR) between the components obtained by signal decomposition and the original data are calculated, after which the key influencing factors are screened out to eliminate the correlation and redundancy of the data. Finally, the filtered meteorological sequences are divided into two datasets based on whether the irradiance of the day is zero or not. Meanwhile, the predictions are performed using BRNN for each of the two datasets. Then, the results are reordered in chronological order, and the prediction of PV power is realized conclusively. It was experimentally verified that the mean absolute value error (MAE) of the method proposed in this paper is 0.1281, which is reduced by 40.28% compared with the back propagation neural network (BPNN) model on the same dataset, the mean squared error (MSE) is 0.0962, and the coefficient of determination (R2) is 0.9907. Other error indicators also confirm that VMD is of much significance and TSP is contributive.
Mixed Reality in Undergraduate Mental Health Education: A Systematic Review

The landscape of Extended Reality (ER), which includes Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) is rapidly changing. However, despite the promising results from many randomized controlled trials (RCTs) developed on healthcare environments, there is a lack of standardization, specifically to determine their effectiveness in academic settings. To our best knowledge, this is the first systematic review addressing the efficacy of MR to enhance learning and skills acquisition in undergraduate mental health education. The purposes of this study were to review the scientific literature of those studies involving MR and undergraduate mental health education, to answer the two following questions: (1) Is MR useful to enhance the acquisition of knowledge and skill training in undergraduate mental health education, and (2) Which are the advantages and disadvantages that should be addressed to successfully develop MR in undergraduate mental health education? We conducted a systematic review of the peer-reviewed literature from EBSCO, Ovid, PubMed, and Scopus y WOS (Web of Science), following the PRISMA statements and using “mixed reality + education”, “mixed reality + student”, “mixed reality + undergraduate”, and “mixed reality + mental health”, as keywords. Those studies published between 2012 to present, in English or Spanish language, were reviewed. A total of 2608 records were retrieved, and only 6 publications met the inclusion criteria, and were finally included. MR training used was varied. There were no studies providing specific outcomes regarding the student’s acquired knowledge (theoretical concepts) after using MR. Several strengths and weaknesses of using MR with students were discussed. The results will be useful to develop innovative MR strategies to improve undergraduate mental health education, due to the lack of studies focused on this topic.