Reviewerly/exHarmony huggingface dataset:
Energy-time entanglement from a resonantly driven quantum-dot three-level system

We investigate the degree of energy-time entanglement in a dressed quantum-dot three-level system via Franson interferometry. Our work demonstrates a maximum visibility of (73 $\ifmmode\pm\else\textpm\fi{}$ 2)%, surpassing the limit of violation of Bell's inequality.
The effect of activation functions on accuracy, convergence speed, and misclassification confidence in CNN text classification: a comprehensive exploration

Abstract Convolutional neural networks (CNNs) have become a useful tool for a wide range of applications such as text classification. However, CNNs are not always sufficiently accurate to be useful in certain applications. The selection of activation functions within CNN architecture can affect the efficacy of the CNN. However, there is limited research regarding which activation functions are best for CNN text classification. This study tested sixteen activation functions across three text classification datasets and six CNN structures, to determine the effects of activation function on accuracy, iterations to convergence, and Positive Confidence Difference (PCD). PCD is a novel metric introduced to compare how activation functions affected a network’s classification confidence. Tables were presented to compare the performance of the activation functions across the different CNN architectures and datasets. Top performing activation functions across the different tests included the symmetrical multi-state activation function, sigmoid, penalised hyperbolic tangent, and generalised swish. An activation function’s PCD was the most consistent evaluation metric during activation function assessment, implying a close relationship between activation functions and network confidence that has yet to be explored.
Factors Affecting Employees Use and Acceptance of Remote Working During the COVID-19 Pandemic: Evidence From the Jordanian Insurance Sector

Remote working during the COVID-19 pandemic comes as an "enforced experiment," where companies and individuals have turned to work from home to preserve business continuity. Drawing on a theoretical framework that integrates the Theory of Reasoned Action (TRA), Social Capital Theory (SCT), and Technology Acceptance Model (TAM), this research uses a sample of 134 survey responses to assess the factors affecting the acceptance and use of remote work during the COVID-19 pandemic among workers of the insurance industry in Jordan. The results suggest that social trust, perceived usefulness, and perceived ease of use can help elevate employee's acceptance and use of remote work, whereas social norms have no significant effect. Considering these results, we further discuss implications and recommendations for the insurance sector.
Binary Code Vulnerability Detection Based on Multi-Level Feature Fusion

The existence of software vulnerabilities will cause serious network attacks and information leakage problems. Timely and accurate detection of vulnerabilities in software has become a research focus on the security field. Most existing work only considers instruction-level features, which to some extent overlooks certain syntax and semantic information in the assembly code segments, affecting the accuracy of the detection model. In this paper, we propose a binary code vulnerability detection model based on multi-level feature fusion. The model considers both word-level features and instruction-level features. In order to solve the problem that traditional text embedding methods cannot handle polysemy, this paper uses the Embeddings from Language Models (ELMo) model to obtain dynamic word vectors containing word semantics and other information. Considering the grammatical structure in the assembly code segment, the model randomly embeds the normalized assembly code segment to represent it. Then the model uses bidirectional Gated Recurrent Unit (GRU) to extract word-level sequence features and instruction-level sequence features respectively. Then, the weighted feature fusion method is used to study the impact of different sequence features on the model performance. During model training, adding standard deviation regularization to constrain model parameters can prevent the occurrence of overfitting problems. To evaluate our proposed method, we conduct experiments on two datasets. Our method achieves an F1-score of 98.9 percent on the Juliet Test Suite dataset and a F1-score of 87.7 percent on the NDSS18 (Whole) dataset. The experimental results show that the model can improve the accuracy of binary code vulnerability detection.
Interactive pose and shape editing with simple sketches from different viewing angles

The challenge of generating and creating humanoid 3D models is nowadays omnipresent in animation films, video games, and many other applications. To enhance and speed up this costly process, we propose an interactive method to infer 3D human poses and shapes from simple free-hand sketches. In contrast to previous work, our sketches are easy and fast to draw even for inexperienced users. Reconstructing the human 3D pose and shape from a single sketch is difficult due to the perspective projection resulting in ambiguous representations. We approach this challenge by adding a novel multi-view sketch interface for human pose estimation. Our simple sketch style allows for fast and intuitive, iterative refinement of not only the pose but also the shape using simple 2D interactions. The pose is inferred from a pre-trained neural network that is trained on a self-generated data set of sketches, derived by sampling 3D STAR models and rendering them in a sketch style. We conduct a user study to evaluate the accuracy, intuitiveness, and usability of our system in a real-world scenario. With our novel multi-view input system, we can effectively resolve ambiguities inherent to single sketch approaches while still delivering a fast and intuitive user experience.
Reverse gamma correction based GARCH model for underwater image dehazing and detail exposure

Underwater imaging poses significant challenges as water alters the behavior of light in comparison to air or vacuum. Therefore, it is crucial to effectively utilize the unique characteristics of unclear edges in hazy underwater images to achieve high-performance results in real-time applications. In this paper, we exploit such features as edges and visual perception in underwater haze images. To achieve this, we estimate the true transmission of the image by enhancing the visibility of discontinuous edges using the reverse gamma correction based on the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. The parameters of the GARCH model are defined by the local and global pixel dynamics in adjacent neighborhoods, which improves the color orientation of the image while preserving image details. Additionally, we perform deseasoning to separate pixels while maintaining the natural contours of interactions between them. By considering the volatility of the RGB color channels, we predict the variance of the pixels based on the difference of the deseasoned pixels, which improves pixel intensity and allows for scene depth estimation. While estimating gamma correction and global ambient light, we deseasonalize image pixels based on their colors, enhancing the color of the final dehazed images. Using the greedy algorithm with a Convolutional Neural Network (CNN), our proposed method outperforms commonly used state-of-the-art methods.
BlazePose-Based Action Recognition with Feature Selection Using Stochastic Fractal Search Guided Whale Optimization

The BlazePose, which models human body skeletons as spatiotemporal graphs, has achieved fantastic performance in skeleton-based action identification. A Spatial-Temporal Graph Convolutional Network can then be used to forecast the actions. This architecture performance can be improved by simply replacing the skeleton input data with a different set of joints that provide more information about the activity of interest. On the other hand, existing approaches require the user to manually set the graph's topology and then fix it across all input layers and samples. This research shows how to use Stochastic Fractal Search - Guided Whale Optimization Algorithm in conjunction with the BlazePose skeletal data to construct a novel implementation of this topology for action recognition. We utilized the NTU-RGB+D and the Kinetics datasets as benchmarks in our experiments.
Higher-Order Boolean Masking Does Not Prevent Side-Channel Attacks on LWE/LWR-based PKE/KEMs

Public-key cryptographic schemes currently in use depend on the intractability of certain mathematical problems such as integer factorization or the discrete logarithm. However, Shor’s algorithm can solve these problems in polynomial time if large-scale quantum computers become available. This will compromise the security of today’s public-key cryptosystems. To address this issue, new public-key cryptographic primitives are being developed. One of them is Saber whose security relies on the Learning With Rounding (LWR) problem that is believed to be hard for quantum computers. The resistance of unprotected and first-order masked implementations of Saber to side-channel attacks has been already investigated. In this paper, we demonstrate the first successful message and secret key recovery attacks on the second- and third-order masked implementations of Saber in ARM Cortex-M4 CPU by deep learning-based power analysis. Our experimental results show that currently available software implementations of Saber need better protection.
Remarks on Gibbs Permutation Matrices for Ternary Bent Functions

As in the binary case, ternary bent functions are a very small portion of the set of all ternary functions for a given number of variables. For example, for n = 2, there are 486 ternary bent functions out of 19683 ternary functions, which is 2, 47%, and this number reduces exponentially with the increase of n. However, finding, or alternatively, constructing them is a challenging task. A possible approach is based upon the manipulation of known ternary bent functions to construct other ternary bent functions. In this paper, we define Gibbs permutation matrices derived from the Gibbs derivative with respect to the Vilenkin-Chrestenson transform and propose their usage in constructing bent functions. The method can be extended to p-valued bent functions, where p is a prime larger than 3.
Neighborhood Rough Residual Network–Based Outlier Detection Method in IoT-Enabled Maritime Transportation Systems

Outlier detection can identify anomalies in large-scale data. To provide reliability and security for Internet of Things (IoT)-enabled maritime transportation systems (MTSs), in this paper we propose an outlier detection method based on the neighborhood rough residual network (NRRN). We calculate the neighborhood approximation accuracy and neighborhood conditional entropy to obtain the neighborhood combined entropy describing the discrimination ability of the condition attribute subset to the information system. We then delete the redundant attributes according to the attribute combination importance derived from the neighborhood combined entropy. The data after attribute reduction are used to train the convolutional neural network, and the residual network (ResNet50) is used to avoid the degradation of model performance caused by the increase in the number of network layers. The proposed method is compared with mainstream outlier detection algorithms on a fishing vessel operation dataset. Experiments show that the proposed method can greatly improve the accuracy of outlier detection while taking into account interpretability and computational efficiency, thereby ensuring the data integrity of IoT-enabled MTSs.
Generation of <i>n</i>-Dimensional Hyperchaotic Maps Using Gershgorin-Type Theorem and its Application

High-dimensional (HD) chaotic map has wide applications in various research fields such as neural networks and secure communication. Designing HD chaotic maps with expected dynamics and robust hyperchaotic behaviors is an interesting but challenging topic. In this article, we propose an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -dimensional hyperchaotic map <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(n\text{D}$ </tex-math></inline-formula> -HCM) generation method on the basis of the Gershgorin-type theorem. First, the general form of the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n\text{D}$ </tex-math></inline-formula> -HCM is built using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> parametric polynomials. Then, the entity and coefficient parameter matrices are configured according to the Gershorin-type theorem. Theoretical analysis shows that the generated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n\text{D}$ </tex-math></inline-formula> -HCM has <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> positive Lyapunov exponents and thus can show robust hyperchaotic behaviors. Two examples of hyperchaotic map with specified equations are provided and their properties are analyzed to show the availability of the proposed method. Performance evaluations display that our <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n\text{D}$ </tex-math></inline-formula> -HCM possesses abundant properties and complex behaviors, and it can outperform some representative HD chaotic maps. Moreover, to show the application of our <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n\text{D}$ </tex-math></inline-formula> -HCM, we apply it to a secure communication scheme and the experimental results exhibit that it shows much better performance than these representative HD chaotic maps in resisting transmission noise.
Detecting Inaccurate Sensors on a Large-Scale Sensor Network Using Centralized and Localized Graph Neural Networks

This article conducts an empirical study on detecting faulty sensors in a large-scale sensor network containing approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\,000$ </tex-math></inline-formula> sensors distributed over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$36\,000 \text {km}^{2}$ </tex-math></inline-formula> . First, we discuss the practical challenge of this task. We compare rule-based models, traditional machine learning models, deep learning models without graph neural networks (GNNs), and deep learning models with GNNs. The experimental results show that GNNs identify more problematic sensors in fewer trials than rule-based models and other machine learning and deep learning models. In addition to training the models in a central server, we also show that localized versions of the deep learning models with GNNs yield predictive power comparable to centralized training. Consequently, each sensor may perform a local inspection to identify its health status and only send reminder signals to a centralized server if it is self-diagnosed as a faulty sensor.
Modern Cryptanalysis Methods, Advanced Network Attacks and Cloud Security

As we all know, modern cryptography is “the scientific study of strategies for safeguarding digital information, transactions, and distributed computations.” Generally, two processes make up cryptography: encryption and decryption. Information is hidden via encryption, which is then unlocked by decryption. In the reference of above definition of modern cryptography, this chapter covers cryptanalysis, types of cryptanalyses—linear and differential and types of attacks. In addition to various cryptanalysis this chapter also covers cryptography in network security, cloud security, blockchain and bitcoin.
Guided Depth Completion Using Active Infrared Images in Time of Flight Systems

Depth information has been successfully used in many computer vision applications, but depth imaging sensors frequently provide missing values, mainly around objects boundaries. These invalid values and image gaps cause serious problems in some applications. In order to estimate missing depth values and fill gaps in depth images (D), we propose a new algorithm for depth completion based on belief propagation. The rationale of the proposed technique is based on the idea that missing values must be estimated by taking into account object boundaries, mainly those related with depth discontinuities. Time of Flight (ToF) cameras provide depth information and some additional data, such as active infrared (IR) brightness images. Therefore, object boundaries information for depth missing areas can be reconstructed by using auxiliary IR information or by RGB images in RGB-D systems. These auxiliary images are used as a guidance for the depth completion, also known as depth inpainting. Experimental results show that our algorithm is very simple to implement, fast and produces better results than other more complex, and usually slower, existing methods.
Human Error - A Critical Contributing Factor to the Rise in Data Breaches: A Case Study of Higher Education

Abstract With increasing technical safeguards to protect information systems, Human error continues to be a critical factor contributing to the rise in information systems attacks and data breaches. Inadequate or unenforceable Cybersecurity policies or training can open doors for adversaries to circumvent technical safeguards and paint a picture of a growing cybersecurity problem. The problem investigated in this work assesses if organizations adequately invest in resources to provide industry-aligned cybersecurity education, training, and awareness that can minimize human error leading to cyber-attacks. This work aims to investigate breaches attributed to human errors and compare cybersecurity policies, education, training, and awareness programs in three different schools in New York State. The work focused on user awareness and vulnerable behaviours, effective training for users, and investigating start-of-the-art approaches to gauge or evaluate the organization’s cybersecurity stance when compared to industry frameworks like the NIST framework. A Triangulation research approach including quantitative, qualitative, and descriptive methods are adopted for this work. Instruments for data collection include a survey, literature review, qualitative analysis to identify research gaps, and assessments of the questionnaires. This work demonstrates that formulated enforced cybersecurity policies coupled with targeted security education, training, and awareness are instrumental to decreasing user errors, thereby reducing the probability of a cyber-attack.
A text style transfer system for reducing the physician–patient expertise gap: An analysis with automatic and human evaluations

Physicians and patients often come from different backgrounds and have varying levels of education, which can result in communication difficulties in the healthcare process. To address this expertise gap, we present a “Text Style Transfer” system. Our system uses Semantic Textual Similarity techniques based on Sentence Transformers models to create pseudo-parallel datasets from a large, non-parallel corpus of lay and expert texts. This approach allowed us to train a denoising autoencoder model (BART), overcoming the limitations of previous systems. Our extensive analysis, which includes both automatic metrics and human evaluations from both lay (patients) and expert (physicians) individuals, shows that our system outperforms state-of-the-art models and is comparable to human-provided gold references in some cases.
An effective single-model learning for multi-label data

Multi-label data classification (MLC) has become an increasingly active research area over the past decade. MLC refers to a classification problem where each instance can be associated with more than one class label. Capturing the correlation among labels and tackling the label imbalance are the main challenges in MLC. Problem transformation is one of the well-known approaches in this area that became the de-facto approach for MLC. Existing methods in this approach consider MLC as a collection of single-label tasks and solve each of them separately. To consider correlation among labels, some of them consider the combination of labels that appear in the training data as a separate label. The main drawback of these kinds of methods is the complexity of the model, which makes them not applicable in real-world applications. In this paper, we show how MLC can be efficiently and effectively tackled with a single classifier. Our proposed method maps the training data into a new sub-space for each label. Then, it pools all the mapped data together and efficiently trains a single classifier for all the labels together. Experimental results show that our method successfully tackles MLC tasks and outperforms the state-of-the-art methods.
Catering for the diversity of students in the 21st century classrooms

The article considers Universal Design for Learning as a comprehensive approach for catering the diversity of students in modern classrooms. The key concepts of the work are the actual concepts of the modern educational environment such as educational barriers and accessibility. Multiple means of representation, multiple means of action and expression as well as multiple means of engagement described in the given article allow educators to create classrooms with updated content providing the student-centered design.
Improving Neural Models for Natural Language Processing in Russian with Synonyms

Large-scale neural network models, including models for natural language processing, require large datasets that could be unavailable for low-resource languages or for special domains. We consider a way to approach the problem of poor variability and small size of available data for training NLP models based on augmenting the data with synonyms. We design a novel augmentation scheme that includes replacing words with synonyms, apply it to the Russian language and report improved results for the sentiment analysis task.
AgreementMakerLight

Ontology matching establishes correspondences between entities of related ontologies, with applications ranging from enabling semantic interoperability to supporting ontology and knowledge graph development. Its demand within the Semantic Web community is on the rise, as the popularity of knowledge graph supporting information systems or artificial intelligence applications continues to increase. In this article, we showcase AgreementMakerLight (AML), an ontology matching system in continuous development since 2013, with demonstrated performance over nine editions of the Ontology Alignment Evaluation Initiative (OAEI), and a history of real-world applications across a variety of domains. We overview AML’s architecture and algorithms, its user interfaces and functionalities, its performance, and its impact. AML has participated in more OAEI tracks since 2013 than any other matching system, has a median rank by F-measure between 1 and 2 across all tracks in every year since 2014, and a rank by run time between 3 and 4. Thus, it offers a combination of range, quality and efficiency that few matching systems can rival. Moreover, AML’s impact can be gauged by the 263 (non-self) publications that cite one or more of its papers, among which we count 34 real-world applications.