Reviewerly/exHarmony huggingface dataset:
Mini‐VLAT: A Short and Effective Measure of Visualization Literacy

Abstract The visualization community regards visualization literacy as a necessary skill. Yet, despite the recent increase in research into visualization literacy by the education and visualization communities, we lack practical and time‐effective instruments for the widespread measurements of people's comprehension and interpretation of visual designs. We present Mini‐VLAT, a brief but practical visualization literacy test. The Mini‐VLAT is a 12‐item short form of the 53‐item Visualization Literacy Assessment Test (VLAT). The Mini‐VLAT is reliable (coefficient omega = 0.72) and strongly correlates with the VLAT. Five visualization experts validated the Mini‐VLAT items, yielding an average content validity ratio (CVR) of 0.6. We further validate Mini‐VLAT by demonstrating a strong positive correlation between study participants' Mini‐VLAT scores and their aptitude for learning an unfamiliar visualization using a Parallel Coordinate Plot test. Overall, the Mini‐VLAT items showed a similar pattern of validity and reliability as the 53‐item VLAT. The results show that Mini‐VLAT is a psychometrically sound and practical short measure of visualization literacy.
Performance analysis of a novel hybrid deep learning approach in classification of quality-related English text

Text classification technique is advancing rapidly alongside AI technology, showing signs of maturity. Moreover, there are always many unrestricted constraints that text classification must deal with in practical settings. English text is indeed a significant component of textual data and a significant source of data for persons seeking data from other countries. This study enhances the text classification method currently in use using text classification depending upon English quality. By using an illustration of English quality-related text classification systems, the concept as well as execution of text classification systems is demonstrated, concluding the study on text classification algorithms. The main task of this article is to categorize, and analyze huge volumes of information in English text using technique of integrating qualitative. Therefore, the fundamental components of superior English compositions are attained using Lion Optimization Algorithm (LOA). Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) is utilized to classify the obtained texts. If there is a lot of training input, the typical English text classification method can easily display flaws like ambiguous characteristic elements. In light of such issues, the research suggests a quality-related English text classification approach based on convolutional neural network in order to enhance the precision and adaptability of English text classification.
Model Framework for Consumer Protection and Crypto-Exchanges Regulation

Cross-border insolvency of crypto-exchanges, cyber-risks, transnational character of activities with cryptocurrencies, and financial frauds on the Internet are among the key threats for individuals who use Bitcoin as an investment. Moreover, crypto-exchanges impose consumer agreements containing provisions limiting their liability for hacker attacks and other clauses promoting inequality in relations with investors. All the named obstacles highlight the vulnerability of unsophisticated individuals investing in digital assets and have pointed out the necessity to adopt an internationally recognized model of rules for crypto-exchanges, otherwise, it will be impossible to effectively protect the rights of investors engaged in the activities of intermediaries exchanging and keeping decentralized cryptocurrencies. The purpose of the study is to elaborate on the fundamentals for constructing an international legal framework protecting consumers from risks arising from the activities of crypto-exchanges dealing with decentralized cryptocurrencies. Based on the methodology of comparative legal study, this paper examines the judicial practice of various countries and the legislation of jurisdictions popular among crypto-exchanges. The research explores the nature of Bitcoin, describes the types of crypto-exchanges and discusses the main approaches to crypto-exchanges regulation. It argues that an international framework on crypto-exchanges should be based on understanding Bitcoin as a commodity which is situated in the place of crypto-exchange incorporation, licensing of crypto-exchanges, and self-regulation.
An Evolutionary Game Theory-Based Method to Mitigate Block Withholding Attack in Blockchain System

Consensus algorithms are the essential components of blockchain systems. They guarantee the blockchain’s fault tolerance and security. The Proof of Work (PoW) consensus algorithm is one of the most widely used consensus algorithms in blockchain systems, using computational puzzles to enable mining pools to compete for block rewards. However, this excessive competition for computational power will bring security threats to blockchain systems. A block withholding (BWH) attack is one of the most critical security threats blockchain systems face. A BWH attack obtains the reward of illegal block extraction by replacing full proof with partial mining proof. However, the current research on the BWH game could be more extensive, considering the problem from the perspective of a static game, and it needs an optimal strategy that dynamically reflects the mining pool for multiple games. Therefore, to solve the above problems, this paper uses the method of the evolutionary game to design a time-varying dynamic game model through the degree of system supervision and punishment. Based on establishing the game model, we use the method of replicating dynamic equations to analyze and find the optimal strategy for mining pool profits under different BWH attacks. The experimental results demonstrate that the mining pools will choose honest mining for the best profit over time under severe punishment and high supervision. On the contrary, if the blockchain system is supervised with a low penalty, the mining pools will eventually choose to launch BWH attacks against each other to obtain the optimal mining reward. These experimental results also prove the validity and correctness of our model and solution.
SiamCAF: Complementary Attention Fusion-Based Siamese Network for RGBT Tracking

The tracking community is increasingly focused on RGBT tracking, which leverages the complementary strengths of corresponding visible light and thermal infrared images. The most well-known RGBT trackers, however, are unable to balance performance and speed at the same time for UAV tracking. In this paper, an innovative RGBT Siamese tracker named SiamCAF is proposed, which utilizes multi-modal features with a beyond-real-time running speed. Specifically, we used a dual-modal Siamese subnetwork to extract features. In addition, to extract similar features and reduce the modality differences for fusing features efficiently, we designed the Complementary Coupling Feature fusion module (CCF). Simultaneously, the Residual Channel Attention Enhanced module (RCAE) was designed to enhance the extracted features and representational power. Furthermore, the Maximum Fusion Prediction module (MFP) was constructed to boost performance in the response map fusion stage. Finally, comprehensive experiments on three real RGBT tracking datasets and one visible–thermal UAV tracking dataset showed that SiamCAF outperforms other tracking methods, with a remarkable tracking speed of over 105 frames per second.
An Intelligent Hybrid Neural Collaborative Filtering Approach for True Recommendations

Recommendation services become a critical and hot research topic for researchers.A recommendation agent that automatically suggests products to users according to their tastes or preferences instead of wandering in a huge corpus for a product.Social data such as Reviews play an important role in the recommendation of products.Improvement was achieved by neural network methods for capturing user and product information from a short text.However, such approaches do not fairly and efficiently incorporate users' preferences and product characteristics.We are proposing the novel Hybrid Neural Collaborative Filtering (HNCF) model that combines deep learning capabilities and deep interaction modelling for recommender systems with a rating matrix.To overcome the cold start problem, we use the new overall rating by aggregating the multivariate rating MR (votes, likes, stars and sentiment scores of reviews) from different external data sources because different sites have different rating scores about the same product.The propose novel model consists of four major modules HUAPA-DCF+NSC+MR (Hierarchical User Attention and Hierarchical Product Attention, Deep Collaborative Filtering, Neural Sentiment Classifier, and multivariant rating) to solve the addressed problems.Initially, the HUAPA module is based on BiLSTM's hierarchical user attention (HUA) and hierarchical product attention (HPA) to embed the user preferences and product characteristics respectively.Further, these are combined nonlinear representations and fed as input to the interaction module.Secondly, the deep collaborative filtering module is implemented to find the explicit interaction between user and product.Thirdly, NSC module will extract the user's semantic about products by incorporating the user's preferences and product characteristics.Finally, the module uses explicit information (multivariant rating) to the maximum extent for final classification.Experimental results demonstration that our novel model is outperforming than state-of-the-art at IMDb, Yelp2013 and Yelp2014 datasets for the true recommendation of top-n products using HNCF (HUAPA+DCF+NSC+MR) to increase the accuracy, confidence, and trust on recommendation services.
Creating Alert Messages Based on Wild Animal Activity Detection Using Hybrid Deep Neural Networks

The issue of animal attacks is increasingly concerning for rural populations and forestry workers. To track the movement of wild animals, surveillance cameras and drones are often employed. However, an efficient model is required to detect the animal type, monitor its locomotion and provide its location information. Alert messages can then be sent to ensure the safety of people and foresters. While computer vision and machine learning-based approaches are frequently used for animal detection, they are often expensive and complex, making it difficult to achieve satisfactory results. This paper presents a Hybrid Visual Geometry Group (VGG)-19+ Bidirectional Long Short-Term Memory (Bi-LSTM) network to detect animals and generate alerts based on their activity. These alerts are sent to the local forest office as a Short Message Service (SMS) to allow for immediate response. The proposed model exhibits great improvements in model performance, with an average classification accuracy of 98%, a mean Average Precision (mAP) of 77.2%, and a Frame Per Second (FPS) of 170. The model was tested both qualitatively and quantitatively using 40,000 images from three different benchmark datasets with 25 classes and achieved a mean accuracy and precision of above 98%. This model is a reliable solution for providing accurate animal-based information and protecting human lives.
A Systematic Literature Review on Blockchain-Based Systems for Academic Certificate Verification

In the past few years, there has been significant progress made in the area of blockchain.The use of blockchain technology has the potential to revolutionize the educational system by providing individuals with innovative and cost-effective ways to learn, as well as by altering the way teachers and students work together.Additionally, blockchain technology can be utilized for the issuing of unchangeable digital certificates, and it can enhance the present limitations of the existing certificate verification systems by making them quicker, more reliable, and independent of the central authority.The application of blockchain in the context of education has generated significant scientific interest in this field.Nonetheless, research endeavors on the adoption of blockchain in the verification of academic credentials are still in the development phase.In order to shed more light on the field, in this paper we focus on extensively reviewing the body of knowledge on blockchain-based systems for academic certificate verification.Hence, the purpose of this survey is to compile all relevant research into a systematic literature review, highlighting the key contributions from various researchers throughout the years with a focus on the past, present, and future.In this work, we have identified 34 relevant studies out of 1744 papers that were published between 2018 and 2022 by employing the PRISMA framework.We distinguished six major themes covered by the research articles analyzed and also identified research gaps that need to be addressed and explored by the research community.Based on the findings of this review, we provide some recommendations for future research directions and practical applications that can assist researchers, policymakers, and practitioners in the field.
Singular spectrum analysis of two-photon interference from distinct quantum emitters

Two-photon interference underlies the functioning of many quantum photonics devices. It also serves as the prominent tool for testing the indistinguishability of distinct photons. However, as their time-spectral profile becomes more involved, extracting relevant parameters, foremost the central frequency difference, may become difficult. In a parametric approach, these arise from the need for an exhaustive model combined with limited count statistics. Here we discuss a solution to curtail these effects on the evaluation of frequency separation relying on a semiparametric method. The time trace of the quantum interference pattern of two photons from two independent solid-state emitters is preprocessed by means of singular spectral analysis before inspecting its spectral content. This approach allows one to single out the relevant oscillations from both the envelope and the noise, without resorting to fitting. This opens the way for robust and efficient on-line monitoring of quantum emitters.
Cross‐project defect prediction method based on genetic algorithm feature selection

Abstract With the continuous development of Internet technology, the role of software in life is increasing, and software defect prediction (SDP) is a key means to ensure software reliability. SDP is to predict the modules that may have defects in advance based on the historical data of software projects, and its purpose is to maximize the use of testing resources. However, in the actual development process, the project that needs to be predicted is often a new project for which there is little or no historical data. Therefore, how to use the massive data of other related projects to build a cross‐project software defect prediction (CPDP) model has received extensive attention from scholars. However, due to the differences in data distribution and class imbalance between different projects, the performance of CPDP is greatly affected. Therefore, on the basis of CPDP, this article proposes a feature selection method based on genetic algorithm (genetic algorithm feature selection, GAFS). GAFS mainly includes two stages: feature selection and ensemble training. In the feature selection stage, this article proposes a global search adaptive feature selection method based on genetic algorithm, which uses the integrated training results of candidate feature subsets on target data to migrate the optimal feature subset. In the ensemble training phase, the EasyEnsemble method is used to alleviate the class imbalance problem, multiple naive Bayesian classifiers are constructed, and then the final model is constructed through ensemble learning. In this article, F1‐score and MCC are used as the test indicator, and comparative experiments are carried out on AEEEM and Promise. The results show that compared with the five comparison methods, GAFS can improve the average F1‐score and MCC much more. For example, GAFS can improve the average F1‐score value by 38.9%, 31.6%, 35.1%, 22.0%, and 31.6%, respectively. In most cases, it can effectively improve the performance of the model and achieve better prediction results.
Deep reinforcement learning‐based routing and resource assignment in quantum key distribution‐secured optical networks

Abstract In quantum key distribution‐secured optical networks (QKD‐ONs), constrained network resources limit the success probability of QKD lightpath requests (QLRs). Thus, the selection of an appropriate route and the efficient utilisation of network resources for establishment of QLRs are the essential and challenging problems. This work addresses the routing and resource assignment (RRA) problem in the quantum signal channel of QKD‐ONs. The RRA problem of QKD‐ONs is a complex decision making problem, where appropriate solutions depend on understanding the networking environment. Motivated by the recent advances in deep reinforcement learning (DRL) for complex problems and also because of its capability to learn directly from experiences, DRL is exploited to solve the RRA problem and a DRL‐based RRA scheme is proposed. The proposed scheme learns the optimal policy to select an appropriate route and assigns suitable network resources for establishment of QLRs by using deep neural networks. The performance of the proposed scheme is compared with the deep‐Q network (DQN) method and two baseline schemes, namely, first‐fit (FF) and random‐fit (RF) for two different networks, namely The National Science Foundation Network (NSFNET) and UBN24. Simulation results indicate that the proposed scheme reduces blocking by 7.19%, 10.11%, and 33.50% for NSFNET and 2.47%, 3.20%, and 19.60% for UBN24 and improves resource utilisation up to 3.40%, 4.33%, and 7.18% for NSFNET and 1.34%, 1.96%, and 6.44% for UBN24 as compared with DQN, FF, and RF, respectively.
A WAV2VEC2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition

Despite recent advancements in deep learning technologies, Child Speech Recognition remains a challenging task. Current Automatic Speech Recognition (ASR) models require substantial amounts of annotated data for training, which is scarce. In this work, we explore using the ASR model, wav2vec2, with different pretraining and finetuning configurations for self-supervised learning (SSL) toward improving automatic child speech recognition. The pretrained wav2vec2 models were finetuned using different amounts of child speech training data, adult speech data, and a combination of both, to discover the optimum amount of data required to finetune the model for the task of child ASR. Our trained model achieves the best Word Error Rate (WER) of 7.42 on the MyST child speech dataset, 2.91 on the PFSTAR dataset and 12.77 on the CMU KIDS dataset using cleaned variants of each dataset. Our models outperformed the unmodified wav2vec2 BASE 960 on child speech using as little as 10 hours of child speech data in finetuning. The analysis of different types of training data and their effect on inference is provided by using a combination of custom datasets in pretraining, finetuning and inference. These ‘cleaned’ datasets are provided for use by other researchers to provide comparisons with our results.
Multi-Stream General and Graph-Based Deep Neural Networks for Skeleton-Based Sign Language Recognition

Sign language recognition (SLR) aims to bridge speech-impaired and general communities by recognizing signs from given videos. However, due to the complex background, light illumination, and subject structures in videos, researchers still face challenges in developing effective SLR systems. Many researchers have recently sought to develop skeleton-based sign language recognition systems to overcome the subject and background variation in hand gesture sign videos. However, skeleton-based SLR is still under exploration, mainly due to a lack of information and hand key point annotations. More recently, researchers have included body and face information along with hand gesture information for SLR; however, the obtained performance accuracy and generalizability properties remain unsatisfactory. In this paper, we propose a multi-stream graph-based deep neural network (SL-GDN) for a skeleton-based SLR system in order to overcome the above-mentioned problems. The main purpose of the proposed SL-GDN approach is to improve the generalizability and performance accuracy of the SLR system while maintaining a low computational cost based on the human body pose in the form of 2D landmark locations. We first construct a skeleton graph based on 27 whole-body key points selected among 67 key points to address the high computational cost problem. Then, we utilize the multi-stream SL-GDN to extract features from the whole-body skeleton graph considering four streams. Finally, we concatenate the four different features and apply a classification module to refine the features and recognize corresponding sign classes. Our data-driven graph construction method increases the system’s flexibility and brings high generalizability, allowing it to adapt to varied data. We use two large-scale benchmark SLR data sets to evaluate the proposed model: The Turkish Sign Language data set (AUTSL) and Chinese Sign Language (CSL). The reported performance accuracy results demonstrate the outstanding ability of the proposed model, and we believe that it will be considered a great innovation in the SLR domain.
Blockchain-Enabled and Multisignature-Powered Verifiable Model for Securing Federated Learning Systems

The Internet of Things (IoT) is revolutionizing numerous industrial applications by employing smart devices in manufacturing and industrial processes. Industries based on IoT generate extensive data, typically analyzed using various machine learning (ML) models. Federated learning (FL) is an emerging, privacy-preserving ML method where clients train models locally and develop a global model based on the aggregation of local models, without sharing the local data set with a third party. However, FL methods struggle to achieve trustworthiness and incorporate accountable ML principles. Blockchain technologies are being developed across different industries to enhance trust and security. This article proposes a blockchain-enabled, verifiable model for securing FL within IoT systems. Our proposed framework combines a trusted execution platform (TEE) to secure each client’s local model training process, and multisignature-powered global model verification to ensure ML model verifiability. We conducted several experiments with different data sets to assess our proposed framework. The experiments demonstrated the high efficiency and scalability of the proposed framework.
Multi-User Nonlinear Optical Cryptosystem Based on Polar Decomposition and Fractional Vortex Speckle Patterns

In this paper, we propose a new multiuser nonlinear optical cryptosystem using fractional-order vortex speckle (FOVS) patterns as security keys. In conventional optical cryptosystems, mostly random phase masks are used as the security keys which are prone to various attacks such as brute force attack. In the current study, the FOVSs are generated optically by the scattering of the fractional-order vortex beam, known for azimuthal phase and helical wavefronts, through a ground glass diffuser. FOVSs have a remarkable property that makes them almost impossible to replicate. In the input plane, the amplitude image is first phase encoded and then modulated with the FOVS phase mask to obtain the complex image. This complex image is further processed to obtain the encrypted image using the proposed method. Two private security keys are obtained through polar decomposition which enables the multi-user capability in the cryptosystem. The robustness of the proposed method is tested against existing attacks such as the contamination attack and known-plaintext attack. Numerical simulations confirm the validity and feasibility of the proposed method.