Reviewerly/exHarmony huggingface dataset:
Evolution of the online rating platform data structures and its implications for recommender systems

Online rating platform represents the new trend of online cultural and commercial goods consumption. The user rating data on such platforms are foods for recommender system algorithms. Understanding the evolution pattern and its underlying mechanism is the key to understand the structures of input data for recommender systems. Prior research on input data analysis for recommender systems is quite limited, with a notable exception in 2018 [6]. In this paper, we take advantage of Poisson Process to analyze the evolution mechanism of the input data structures. We discover that homogeneous Poisson Process could not capture the mechanism of user rating behavior on online rating platforms, and inhomogeneous Poisson Process is compatible with the formation process.
Multi-modal graph contrastive encoding for neural machine translation

As an important extension of conventional text-only neural machine translation (NMT), multi-modal neural machine translation (MNMT) aims to translate input source sentences paired with images into the target language. Although a lot of MNMT models have been proposed to perform multi-modal semantic fusion, they do not consider fine-grained semantic correspondences between semantic units of different modalities (i.e., words and visual objects), which can be exploited to refine multi-modal representation learning via fine-grained semantic interactions. To address this issue, we propose a graph-based multi-modal fusion encoder for NMT. Concretely, we first employ a unified multi-modal graph to represent the input sentence and image, in which the multi-modal semantic units are considered as the nodes in the graph, connected by two kinds of edges with different semantic relationships. Then, we stack multiple graph-based multi-modal fusion layers that iteratively conduct intra- and inter-modal interactions to learn node representations. Finally, via an attention mechanism, we induce a multi-modal context from the top node representations for the decoder. Particularly, we introduce a progressive contrastive learning strategy based on the multi-modal graph to refine the training of our proposed model, where hard negative samples are introduced gradually. To evaluate our model, we conduct experiments on commonly-used datasets. Experimental results and analysis show that our MNMT model obtains significant improvements over competitive baselines, achieving state-of-the-art performance on the Multi30K dataset.
Including insider threats into risk management through Bayesian threat graph networks

Cybersecurity incidents do represent a serious danger for companies. In fact, the number of cyber crimes is exponentially growing in a scenario where the global COVID-19 pandemic determined several conditions that have negatively affected companies' cyber-security posture. The adoption of risk management processes can help reduce security threats and mitigate both financial and reputation losses. In computer systems, it is crucial to relate security risks to the system infrastructure. Bayesian attack graph models can help reach such a goal. The approach is very effective as it allows to define the attack paths an attacker would perform against a specific network infrastructure. In this way, it is possible to construct a truthful representation of a company's security risks that cannot be obtained with other approaches. Still, Bayesian risk management approaches are usually based on advanced threats. Namely, those threats relate to vulnerabilities that can only be exploited by a skilled attacker. Although several works enrich the expressiveness of the proposed Bayesian model, current proposals do not provide insights into how it is possible to estimate security control costs, asset values, and threat probabilities for other types of threats. Furthermore, they do not take insider threats into consideration. This work shows that a risk management framework based on Bayesian Attack Graphs can be adapted to include a variety of threats, including those related to the insiders. We first extend an interesting work based on Bayesian Decision Networks to cover a broader range of threats. Then, we formalize several concepts, such as security control coverage and risk strategy, and show that our model can easily integrate insider threats when specific properties are defined. Finally, in order to address insider threats, we enrich the model with security controls that differ from the standard ones, such as technical IT training sessions and employee satisfaction surveys.
Kryptosafe: managing and trading data sets using blockchain and IPFS

Trading data sets is not easy. The owner of valuable data, once they are sold the first time, cannot be sure that they will not be copied and resold. On the other hand, the buyer, cannot be sure that the seller will not sell the same data to a competitor. The advent of blockchain technology, or DLT, can mitigate, or event solve these issues, because it can certify the data ownership, and act as a broker between seller and buyer. In this paper we present Kryptosafe, a system developed following sound software engineering practices, aimed to manage the trade of data sets taking advantage of the unique features of immutability and trustfulness of Ethereum blockchain, and of IPFS distributed DBMS. Kryptosafe allows data sellers to sell a whole encrypted data set or to show potential buyers a subset of it, allowing full access only after the sale is finalized. Using ERC721 and ERC1155 tokens, it also manages one-time sales, when the data set ownership is simply transferred to the buyer, or multiple sales of the same data set to different buyers.
Automatic Generation of a Blockchain-based Drug Supply Chain Management System

Blockchain technology can improve supply chain management and delivery systems for medical drugs, in several ways. It can improve trust, data integrity, transparency, certify the ownership of drug lots, and track all the phases of the life-cycle of a drug in a non-alterable way. This paper presents a blockchain-based methodology for automatically developing a DApp for drug supply chain management, starting from a description of the specific supply chain involved. It make use of the Python Django framework and is performed by building configurable SCs. Additionally, we describe a simple case study to show how our approach works.
Defending Against Adversarial Fingerprint Attacks Based on Deep Image Prior

Recently, deep learning-based biometric authentication systems, especially fingerprint authentication, have been used widely in real-world. However, these systems are vulnerable to adversarial attack which prevents deep learning model from distinguishing input data properly. To solve these problems, various defense methods have been proposed, especially utilizing denoising mechanism, but they provided limited defense performance. In this study, we proposed a newdefense method against adversarial fingerprint attacks. To ensure defense performance, we have introduced Deep Image Prior mechanism which has superior performance in image reconstruction without prior training and a large amount of dataset. The proposed method aims to remove adversarial perturbations of input fingerprint image and reconstruct it close to original fingerprint image by adapting Deep Image Prior. Our method has achieved robust defense performance against various types of adversarial fingerprint attacks across different datasets, encompassing variations in sensors, shapes, and materials of fingerprint images. Furthermore, our method has demonstrated that it is superior to other image reconstruction methods.
Nighttime Image Dehazing by Render

Nighttime image dehazing presents unique challenges due to the unevenly distributed haze caused by the color change of artificial light sources. This results in multiple interferences, including atmospheric light, glow, and direct light, which make the complex scattering haze interference difficult to accurately distinguish and remove. Additionally, obtaining pairs of high-definition data for fog removal at night is a difficult task. These challenges make nighttime image dehazing a particularly challenging problem to solve. To address these challenges, we introduced the haze scattering formula to more accurately express the haze in three-dimensional space. We also proposed a novel data synthesis method using the latest CG textures and lumen lighting technology to build scenes where various hazes can be seen clearly through ray tracing. We converted the complex 3D scattering relationship transformation into a 2D image dataset to better learn the mapping from 3D haze to 2D haze. Additionally, we improved the existing neural network and established a night haze intensity evaluation label based on the idea of optical PSF. This allowed us to adjust the haze intensity of the rendered dataset according to the intensity of the real haze image and improve the accuracy of dehazing. Our experiments showed that our data construction and network improvement achieved better visual effects, objective indicators, and calculation speed.
A Two-Stage Track-before-Detect Method for Non-Cooperative Bistatic Radar Based on Deep Learning

Compared with traditional active detection radar, non-cooperative bistatic radar has a series of advantages, such as a low cost and low detectability. However, in real-life scenarios, it is limited by the non-cooperation of the radiation source and the bistatic geometric model, resulting in a low target signal-to-noise ratio (SNR) and unstable detection between frames in the radar scanning cycle. The traditional detect-before-track (DBT) method fails to exploit adequately the target information and is incapable of achieving consistent and effective tracking. Therefore, in this paper, we propose a two-stage track-before-detect (TBD) method based on deep learning. This method employs a low-threshold detection network to identify the target initially, followed by utilizing the model method to ascertain potential tracks. Subsequently, a diverse range of network structures are employed to extract and integrate position information, innovation score, and target structural information from the track in order to obtain the target track. Experimental results demonstrate the method’s ability to achieve multi-target tracking in highly cluttered environments, where the higher the number of frames processed, the better the target tracking effect. Moreover, the method exhibits real-time processing capabilities. Hence, this method provides an effective solution for target tracking in non-cooperative bistatic radar systems.
Exploring the determinants affecting the usage of blockchain-based remittance services: an empirical study on the banking sector

ABSTRACTThe industrial revolution 4.0 has pushed the banking sector to bring transparency in their financial transactions, especially in sending and receiving remittances. This study identifies the factors that affect the user's behaviour in the adaptability of blockchain technology (BT). The unified theory of acceptance and use of technology (UTAUT) with two additional factors comprising trust and transparency is used in the current study. The data of 240 respondents from the banks of Pakistan were tested through the PLS-SEM and fsQCA. The study results show that performance expectancy, effort expectancy, trust in BT, transparency in BT and the facilitating condition positively affect, while social influence negatively affects the behavioural intention towards BT. This study extends the application of the UTAUT model with two dimensions (e.g., trust and transparency) to identify the factors that predict bankers' intentions and their subsequent effect on the overall use of blockchain-based remittance services in Pakistani banks. In addition, we extend this study by combining symmetric (i.e. PLS-SEM) and asymmetric (i.e. fsQCA) techniques to generate significant implications for banks that provide blockchain-based remittance services. The asymmetric findings also provided bankers with two models (recipes) that have a greater potential for predicting bankers' use of blockchain-based remittance services.KEYWORDS: Blockchain technologyperformance expectancyeffort expectancysocial influencetrusttransparency Disclosure statementNo potential conflict of interest was reported by the author(s).
An Efficient Object Detection Algorithm Based on Improved YOLOv5 for High-Spatial-Resolution Remote Sensing Images

The field of remote sensing information processing places significant research emphasis on object detection (OD) in high-spatial-resolution remote sensing images (HSRIs). The OD task in HSRIs poses additional challenges compared to conventional natural images. These challenges include variations in object scales, complex backgrounds, dense arrangement, and uncertain orientations. These factors contribute to the increased difficulty of OD in HSRIs as compared to conventional images. To tackle the aforementioned challenges, this paper introduces an innovative OD algorithm that builds upon enhancements made to the YOLOv5 framework. The incorporation of RepConv, Transformer Encoder, and BiFPN modules into the original YOLOv5 network leads to improved detection accuracy, particularly for objects of varying scales. The C3GAM module is designed by introducing the GAM attention mechanism to address the interference caused by complex background regions. To achieve precise localization of densely arranged objects, the SIoU loss function is integrated into YOLOv5. The circular smooth label method is used to detect objects with uncertain directions. The effectiveness of the suggested algorithm is confirmed through its application to two commonly utilized datasets, specifically HRSC2016 and UCAS-AOD. The average detection accuracies achieved on these datasets are 90.29% and 90.06% respectively, surpassing the performance of other compared OD algorithms for HSRIs.
Tracing Knowledge Diffusion Trajectories in Scholarly Bitcoin Research: Co-Word and Main Path Analyses

In the burgeoning field of bitcoin research, a cohesive understanding of how knowledge and insights have evolved over time is lacking. This study aims to address this gap through an exploration of 4123 academic articles pertaining to bitcoin. Utilizing co-word analysis and main path analysis (MPA), it uncovers key themes and seminal works that have substantially influenced the field’s progression. The identified clusters, including safe haven, internet of things (IoT), proof of work (PoW), market efficiency, sentiment analysis, digital currency, and privacy, shed light on the multifaceted discourse surrounding bitcoin. The MPA, incorporating both forward and backward local paths, traces an evolving narrative, starting from an in-depth exploration of bitcoin’s structure, anonymity, and contrasts against traditional financial assets. It tracks the shift in focus to broader market dynamics, volatility, speculative nature, and reactions to economic policy fluctuations. The analysis underscores the transformation of bitcoin research, from its beginnings as a decentralized, privacy-oriented currency to its role in global economics and green financing, revealing a complex narrative of an innovative financial instrument to a multifaceted entity. Implications drawn from this analysis include the need for further research on the potential integration of bitcoin within emerging technologies like AI and cybersecurity, the implications of bitcoin’s interplay with traditional financial systems, and the environmental impacts of bitcoin and blockchain utilization. Overall, the current study not only enhances our understanding of the bitcoin field but also charts its dynamic evolution and stimulates further academic inquiry.
A Dynamic-Routing Algorithm Based on a Virtual Quantum Key Distribution Network

Quantum key distribution (QKD) is an encrypted communication technique based on the principles of quantum mechanics that ensures communication security by exploiting the properties of quantum states. Currently, the transmission efficiency of the QKD system is low. Trusted relay technology is used to solve this problem and achieve long-distance transmission. However, trusted relaying alone cannot decrypt the issues of poor link stability and the low utilization of key resources. To further optimize the system performance, we propose a dynamic routing algorithm. One of the improvement schemes includes the following: firstly, an adjustable-size quantum key pool (QKP) is designed, which can dynamically adjust the size of the refreshing pool according to the actual demand. Secondly, the utilization of key resources is improved by using the residual quantum key model to dynamically obtain the remaining key amount in the QKP and set the key amount threshold. We calculate the link-blocking probability and track the blocking intensity and blocking entry by combining the Poisson process, thus realizing the evaluation of the link stability. Finally, the number of remaining keys in the QKP and the link-blocking probability combine with the random wandering model as the basis of the route selection for the QKD dynamic routing algorithm to achieve efficient key path selection. We validated the algorithm by comparing it with other algorithms on the Mininet simulation platform, and the algorithm proved to have a better performance in terms of congestion avoidance, delay reduction, and improved QKD efficiency. This scheme provides a novel and efficient way to solve the problems in existing QKD systems. It effectively improves the transmission efficiency and strengthens the system’s security by dynamically obtaining the critical volume, accurately evaluating the link state, and selecting the optimal critical path.
Remote Sensing Image Target Detection Method Based on Refined Feature Extraction

To address the challenges posed by the large scale and dense distribution of small targets in remote sensing images, as well as the issues of missed detection and false detection, this paper proposes a one-stage target detection algorithm, DCN-YOLO, based on refined feature extraction techniques. First, we introduce DCNv2 and a residual structure to reconstruct a new backbone network, which enhances the extraction of shallow feature information and improves the network’s accuracy. Then, a novel feature fusion module is employed in the neck network to adaptively adjust the fusion weight for integrating texture information from shallow features with deep semantic information. This targeted approach effectively suppresses noise caused by extracting shallow features and enhances the representation of key features. Moreover, the normalized Gaussian Wasserstein distance loss, replacing Intersection over Union (IoU), is used as the regression loss function in the model, to enhance the detection capability of multi-scale targets. Finally, comparing our evaluations against recent advanced methods such as YOLOv7 and YOLOv6 demonstrates the effectiveness of the proposed approach, which achieves an average accuracy of 20.1% for small targets on the DOTAv1.0 dataset and 29.0% on the DIOR dataset.
An Improved Density Peak Clustering Algorithm Based on Chebyshev Inequality and Differential Privacy

This study aims to improve the quality of the clustering results of the density peak clustering (DPC) algorithm and address the privacy protection problem in the clustering analysis process. To achieve this, a DPC algorithm based on Chebyshev inequality and differential privacy (DP-CDPC) is proposed. Firstly, the distance matrix is calculated using cosine distance instead of Euclidean distance when dealing with high-dimensional datasets, and the truncation distance is automatically calculated using the dichotomy method. Secondly, to solve the difficulty in selecting suitable clustering centers in the DPC algorithm, statistical constraints are constructed from the perspective of the decision graph using Chebyshev inequality, and the selection of clustering centers is achieved by adjusting the constraint parameters. Finally, to address the privacy leakage problem in the cluster analysis, the Laplace mechanism is applied to introduce noise to the local density in the process of cluster analysis, enabling the privacy protection of the algorithm. The experimental results demonstrate that the DP-CDPC algorithm can effectively select the clustering centers, improve the quality of clustering results, and provide good privacy protection performance.
Multilabel Genre Prediction Using Deep-Learning Frameworks

In this study, transfer learning has been used to overcome multilabel classification tasks. As a case study, movie genre classification by using posters has been chosen. Six state-of-the-art pretrained models, VGG16, ResNet, DenseNet, Inception, MobileNet, and ConvNeXt, have been employed for this experiment. The movie posters have been obtained from Internet Movie Database (IMDB). The dataset has been divided using an iterative stratification technique. A sequence of dense layers has been added on top of each model and these models have been trained and fine-tuned. All the results of the models compared considered accuracy, loss, Hamming loss, F1-score, precision, and AUC metrics. When the metrics used were evaluated, the most successful result regarding accuracy has been obtained from the modified DenseNet architecture at 90%. Also, the ConvNeXt, which is the newest model among all, performed quite satisfactorily, reaching over 90% accuracy. This study uses an iterative stratification method to split an unbalanced dataset which provides more reliable results than the classical splitting method which is the common method in the literature. Also, the feature extraction capabilities of the six pretrained models have been compared. The outcome of this study shows promising results regarding multilabel classification. As for future work, it is planned to enhance this study by using natural language processing and ensemble methods.
Research on Learning Concentration Recognition with Multi-Modal Features in Virtual Reality Environments

Learning concentration, as a crucial factor influencing learning outcomes, provides the basis for learners’ self-regulation and teachers’ instructional adjustments and intervention decisions. However, the current research on learning concentration recognition lacks the integration of cognitive, emotional, and behavioral features, and the integration of interaction and vision data for recognition requires further exploration. The way data are collected in a head-mounted display differs from that in a traditional classroom or online learning. Therefore, it is vital to explore a recognition method for learning concentration based on multi-modal features in VR environments. This study proposes a multi-modal feature integration-based learning concentration recognition method in VR environments. It combines interaction and vision data, including measurements of interactive tests, text, clickstream, pupil facial expressions, and eye gaze data, to measure learners’ concentration in VR environments in terms of cognitive, emotional, and behavioral representation. The experimental results demonstrate that the proposed method, which integrates interaction and vision data to comprehensively represent the cognitive, emotional, and behavioral dimensions of learning concentration, outperforms single-dimensional and single-type recognition results in terms of accuracy. Additionally, it was found that learners with higher concentration levels achieve better learning outcomes, and learners’ perceived sense of immersion is an important factor influencing their concentration.