Reviewerly/exHarmony huggingface dataset:
Beyond the Node-Link Diagram

Abstract This chapter gives an overview over visualization techniques beyond the node-link diagram. Node-link diagrams are by far the most common visual representation to visualize networks, showing nodes as points, and links as lines or arcs between them. However, with increasing size of a network, as well as data about time and temporal change, geography, or other attributes about node and links, alternative representations are required. Starting from a discussion of the problems of visualizing dense networks through node-link diagrams (i.e. hairballs), this chapter discusses a range of alternatives to node-link diagrams: adjacency matrices to compare networks and visualize multivariate networks; techniques for networks with different node and link types; networks with quantitative attributes on nodes, Ontologies, networks changing over time, and geographic networks. The chapter concludes with a pointer to common network visualization tools and a discussion of how to choose among the wide range of visualization techniques.
The Separability Problem in Two Qubits Revisited

In this paper, we present an overview of the progress in the separability problem in bipartite systems, more specifically in a two quantum bits (qubits) system, from the separability criterion based on Bell’s inequalities in 1964 to the recent separability criteria.
Control of a single-photon router via an extra cavity

Controllable single-photon routing plays an important role in quantum networks. We investigate single-photon scattering in two one-dimensional (1D) waveguides by a three-level emitter with a cascade configuration, which is a dipole coupled to an extra cavity. The tunneling path for the transmission of a single photon is switched by whether the extra cavity contains photons. For the setup, the Autler–Townes splitting is modulated by the extra cavity, in which the transmission valley (reflection range) width is tunable in terms of the number of photons in the extra cavity. Our investigation will be beneficial to single-photon routing in quantum networks using quantifiable photon numbers in an extra cavity.
Anomaly Detection Using Puzzle-Based Data Augmentation to Overcome Data Imbalances and Deficiencies

Machine tools are used in a wide range of applications, and they can manufacture workpieces flexibly. Furthermore, they require maintenance; the overall costs include maintenance costs, which constitute a significant portion, and the costs involved in ensuring product quality. Therefore, anomaly detection in tool conditions is required, because these tools are essential industrial elements. However, the data related to tool conditions present some challenges: data imbalances and deficiencies. Data imbalances and deficiencies can affect the performance of anomaly detection models. A model trained using data with imbalances and deficiencies may miscalculate that abnormal data are normal data, leasing to errors. To overcome these problems, the proposed method has been designed using the wavelet transform, color space conversion, color extraction, puzzle-based data augmentation, and double transfer learning. The proposed method generated image data from time-series data, effectively extracted features, and generated new image data using puzzle-based data augmentation. The color information was processed to highlight features, and the proposed puzzle-based data augmentation was applied during processing to increase the amount of data to improve the performance of the anomaly detection model. The experimental results showed that the proposed method can classify normal and abnormal data with greater accuracy. In particular, the accuracy of abnormal data classification increased from 25.00% to 91.67%. This demonstrates that the proposed method is effective and can overcome data imbalances and deficiencies.
Graph neural network-based breast cancer diagnosis using ultrasound images with optimized graph construction integrating the medically significant features

An automated computerized approach can aid radiologists in the early diagnosis of breast cancer. In this study, a novel method is proposed for classifying breast tumors into benign and malignant, based on the ultrasound images through a Graph Neural Network (GNN) model utilizing clinically significant features.Ten informative features are extracted from the region of interest (ROI), based on the radiologists' diagnosis markers. The significance of the features is evaluated using density plot and T test statistical analysis method. A feature table is generated where each row represents individual image, considered as node, and the edges between the nodes are denoted by calculating the Spearman correlation coefficient. A graph dataset is generated and fed into the GNN model. The model is configured through ablation study and Bayesian optimization. The optimized model is then evaluated with different correlation thresholds for getting the highest performance with a shallow graph. The performance consistency is validated with k-fold cross validation. The impact of utilizing ROIs and handcrafted features for breast tumor classification is evaluated by comparing the model's performance with Histogram of Oriented Gradients (HOG) descriptor features from the entire ultrasound image. Lastly, a clustering-based analysis is performed to generate a new filtered graph, considering weak and strong relationships of the nodes, based on the similarities.The results indicate that with a threshold value of 0.95, the GNN model achieves the highest test accuracy of 99.48%, precision and recall of 100%, and F1 score of 99.28%, reducing the number of edges by 85.5%. The GNN model's performance is 86.91%, considering no threshold value for the graph generated from HOG descriptor features. Different threshold values for the Spearman's correlation score are experimented with and the performance is compared. No significant differences are observed between the previous graph and the filtered graph.The proposed approach might aid the radiologists in effective diagnosing and learning tumor pattern of breast cancer.
RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification

When radar receives target echoes to form plots, it is inevitably affected by clutter, which brings a lot of imprecise and uncertain information to target recognition. Traditional radar plot recognition algorithms often have poor performance in dealing with imprecise and uncertain information. To solve this problem, a radar plot recognition algorithm based on adaptive evidence classification (RPREC) is proposed in this paper. The RPREC can be considered as the evidence classification version under the belief functions. First, the recognition framework based on the belief functions for target, clutter, and uncertainty is created, and a deep neural network model classifier that can give the class of radar plots is also designed. Secondly, according to the classification results of each iteration round, the decision pieces of evidence are constructed and fused. Before being fused, evidence will be corrected based on the distribution of radar plots. Finally, based on the global fusion results, the class labels of all radar plots are updated, and the classifier is retrained and updated so as to iterate until all the class labels of radar plots are no longer changed. The performance of the RPREC is verified and analyzed based on the real radar plot datasets by comparison with other related methods.
Cybersecurity Students’ Interest in Government Careers: Impact of Demographic Characteristics and Job Dynamics

AbstractA critical shortage of cybersecurity talent exists in the United States. The lack of personnel has resulted in increased competition for the cybersecurity professionals. As the field of cybersecurity has evolved, a significant salary gap has surfaced between public and private employers. With private employers paying higher salaries, it has become harder for government agencies to hire cybersecurity professionals. Different policies and programs have been developed by government agencies to address this barrier. The current study examines the employment goals of a sample of cybersecurity students (n = 156). Specific attention is given to the types of employers students want to work for as well as how students rate the importance of different job dynamics. A sizeable number of the students (n = 90) expressed interest in working in public service. Job dynamics most important to students included being treated with respect, having job security, knowing expectation, safety, and opportunities for improvement. The results show significant demographic dissimilarities as well as differences in the ways that public service-oriented students (versus students seeking careers with private companies) rated job dynamics. Bivariate analyses showed that female and first-generation cybersecurity students were more likely to express interest in public service careers, as were juniors, seniors, and graduate students. In addition, students whose mother worked for the government were more likely than students whose mother worked for a private company to express interest in working in public service careers. Multivariate analyses confirmed these results. Female cybersecurity students also rated the importance of job security, mission attainment, personal empowerment, teamwork, commitment to representation, and personal empowerment higher than male cybersecurity students. Those who expressed interest in working in public service careers rated the importance of personal empowerment, collaboration, and physical conditions higher than those seeking careers in private industry.Keywords: Cybersecuritypublic servicecareers Ethical approvalThis research was approved by the Old Dominion University Institutional Review Board [Project 1790721-1 – Assessment of Cyber LeADERS]Additional informationFundingThis research is supported in part by the National Science Foundation under [grant DGE − 2042882].
What Is Hidden in Clear Sight and How to Find It—A Survey of the Integration of Artificial Intelligence and Eye Tracking

This paper presents an overview of the uses of the combination of eye tracking and artificial intelligence. In the paper, several aspects of both eye tracking and applied AI methods have been analyzed. It analyzes the eye tracking hardware used along with the sampling frequency, the number of test participants, additional parameters, the extraction of features, the artificial intelligence methods used and the methods of verification of the results. Finally, it includes a comparison of the results obtained in the analyzed literature and a discussion about them.
WIG-Net: Wavelet-Based Defocus Deblurring with IFA and GCN

Although the existing deblurring methods for defocused images are capable of approximately recovering clear images, they still exhibit certain limitations, such as ringing artifacts and remaining blur. Along these lines, in this work, a novel deep-learning-based method for image defocus deblurring was proposed, which can be applied to medical images, traffic monitoring, and other fields. The developed approach is equipped with wavelet transform, an iterative filter adaptive module, and graph neural network and was specifically designed for handling defocused blur. Our network exhibits excellent properties in preserving the original information during the restoration of clear images, thereby enhancing its capability to spatially address varying blurriness and improving the quality of deblurring. From the acquired experimental results, the superiority of the introduced method in the context of image defocus deblurring compared to the majority of the existing algorithms was clearly demonstrated.
Influencing Factors of Reading Effect of College Students' Red Classics in Intelligent Media Environment

Reading red classics is an important way of patriotism education in the new era. Guiding college students to read red classics is conducive to cultivating their red values and shaping their patriotic conduct. The development of intelligent media technology reshapes the way of thinking and behavior of college students, and also changes their reading methods to some extent. The progress of technology provides a new opportunity for the popularization of red classics. By analyzing the current situation of reading red classics among college students in the new situation, this paper sorts out the factors that affect reading promotion, including media space-time factors, content factors, scene factors and psychological factors. It is pointed out that the promotion of reading in colleges and universities needs to strengthen the construction of digital resources and brand of libraries, improve readers' digital literacy and ability, give full play to the role of reading promotion of red classics in colleges and universities, carry forward red culture and enhance the comprehensive quality of college students.
Research on Folk Culture Translation Based on Bassnett's Cultural Translation View

Folk culture is the best witness of the development process of a country and the living background of its ethnic groups. It can explain each ethnic group from the deepest level to the development of each track. Having gone through 5,000 years of trials and rains, China has a long history and profound culture. The translation and dissemination of folk culture plays a positive role in promoting regional development, carrying forward Chinese culture and enhancing the soft power of national culture. This paper analyzes the research status and value of folk culture translation, and discusses the application of cultural translation in folk culture translation through some examples.
Traffic Light Recognition in Autonomous Driving: Comparison of YOLOv7 and CenterNet2 with HSV Post-Processing

Advanced deep learning networks, such as the YOLO series have been widely utilized for traffic light recognition tasks. However, existing models, overlook the issues of additional noise caused by the external environment(e.g., signal lights of the vehicles on the road, lights on the side of the road etc.) in the real-world. This study aims to fill this research gap by applying hue-saturation-value(HSV) post-processing technique to the two of the most popular traffic light recognition models YOLOv7 and CenterNet2 and compare the results before and after the usage of the applied technique. After evaluations, the implemented post-processing method showed effectiveness in correctly classifying the detected traffic light signals, resulting in improved overall mAP results from 82.6% to 90.1 % for the YOLOv7 model and from 83.5% to 91.8% for the CenterNet2 model.
An Denoising Method Low-Dose CT Image Using Image Restoration Convolutional Neural Network

Radiation is widely used in medicine to diagnose and treat disease. CT (Computed Tomography) scans allow early detection of externally invisible diseases and appropriate treatment. However, radiation exposure from the examination may result in a future risk of cancer. Efforts are therefore being made to reduce radiation exposure. During the examination, noise is generated in the image when the dose is reduced. Noise reduces the visibility of the image and may cause lesions to be missed. In this study, we focus on Convolutional Neural Networks (CNNs), a type of deep learning model that has recorded high accuracy in image processing tasks. The proportion of frequency components in an image has more low-frequency components and fewer high-frequency components. Since image features are treated equally across channels, important information such as noise and edges are easily lost. To solve this problem, we propose CNN with channel attention module. In addition, we employ MAE as the loss function to enable effective learning. Using whole body slice CT images of pigs, we evaluate the image quality by Peak Signal-to-Noise Ratio (PSNR) and show that the proposed method is effective.
Power Consumption of Linear and Non-linear Filters on Multi-core Architecture by Using AVX and OpenMP

The general concept of filtering is to modify or enhance the image data by removing noises for smoothing, sharpening, and edge enhancement. There are mainly two types of filtering methods in image processing, namely linear and non-linear filters. This paper proposes the energy consumption of linear and non-linear filters on multi-core architecture by using AVX and OpenMP. Many researchers have contributed to the computation time of well-known filters such as the mean, median, Gaussian, Laplacian, IRR filters, and so on. Although these filters have been experimented related with to execution time, the measurement of energy consumption of linear and non-linear filters has still remained a gap for image processing. For this reason, this paper focuses on the experiments for the energy consumption of a linear filter (Gaussian filter) and a non-linear filter (median filter). Moreover, the parallel implementation of these filters and the serial computation also have been tested on CPU and GPU in the case of examining the measuring the amount of energy consumption of them. According to the experimental results of the matrix kernel becomes 4069 ×4096, in average, a linear filter is more consumed than a non-linear filter in the case of energy consumption of about 5.1 % and 3.9% in Watt and more efficient performance of about 2.5% and 1.12% in GFLOPS when testing on CPU and GPU respectively, when utilizing AVX and OpenMP in the parallel versions of these filters.
Exploring Generalization and Adaptability of Offline Reinforcement Learning for Robot Manipulation

Reinforcement learning (RL) has emerged as a promising approach for robot manipulation tasks. However, the data-intensive nature and substantial training time required by online RL techniques make them unsafe or impractical in certain situations like robot applications. This paper empirically investigates offline RL's feasibility, generalization, and adaptability for robot manipulation tasks compared to online RL approaches. We apply several state-of-the-art algorithms, including AWAC, CQL, and IQL, to robot push tasks to examine the feasibility and practicality of offline RL for robotic manipulation. We also investigate how the characteristics of the offline dataset, such as size, exploration ratio, and randomization, impact the performance of the offline RL. The generalization capabilities and adaptability of these algorithms are assessed in unseen environments with varied object properties and physics settings. The results demonstrate that offline RL not only shows promising performance but also exhibits better generalization capabilities compared to online RL. In terms of adaptation, offline RL achieves significant performance improvements through small steps of online fine-tuning. These findings underline the potential of offline RL as an effective and practical approach for real-world robot manipulation tasks.
A Method for Denoising from Low-Dose CT Images Based on Noise2Noise

Japan has the highest number of CT scanners per million people among developed countries, and the annual medical radiation dose is about six times higher than the world average. Screening of high-risk populations by means of low-dose CT has been verified to be able to reduce lung cancer mortality. However, the low-dose CT image noise should be accurately estimated in order to achieve good image quality as normal dose CT. In this paper, we apply a denoising method using convolutional neural networks without use of the clean images during learning phase. The CNN model is based on SRResNet, which has achieved high performance in super-resolution tasks, and has two attention mechanisms, Spatial Attention and Channel Attention, in the Residual Block. Experiments using whole body slice CT images of pigs showed that the proposed method is useful by comparison with normal dose CT images and by evaluation of image quality using peak signal-to-noise ratio (PSNR).
Noise Removal in Scattering Media Enviorment Using Peploaraphy and DCLGAN

Noise removal in scattering media environments such as fog and smoke are an important task in reality. For example, it can be applied in many fields such as searching under turbid water and rescuing in smoke. Therefore, the purpose of this research is to remove the noise under the scattering medium environment and to visualize the object. As a conventional method for removing noise, there is a technique called peplography. Peplograhy enables the denoising of dense fog and reconstructing the image in scattering media environments. However, peplography has a problem that complex noise exists after removing particularly deep noise, and as a result, it becomes difficult to visualize the object. This research aims to solve this problem. We propose a new denoising technology that combines peplography with DCLGAN. DCLGAN is an image transformation algorithm using deep learning and an improved model of CycleGAN which is also used in the field of noise reduction. In this study, we evaluate the conventional method and the proposed method for scattering media images. As the evaluation method, visual evaluation and numerical evaluation using the PSNR index are performed to demonstrate the effectiveness of the proposed method. We believe that the proposed method can be applied in various fields such as object visualization under turbid water and automatic driving technology with fog removed.
New entanglement-assisted quantum codes constructed from Hermitian LCD codes

&lt;abstract&gt;&lt;p&gt;Hermitian linear complementary dual (LCD) codes are a class of linear codes that intersect with their Hermitian dual trivially. Each Hermitian LCD code can give an entanglement-assisted quantum error-correcting code (EAQECC) with maximal entanglement. Methods of constructing Hermitian LCD codes from known codes were developed, and seven new Hermitian LCD codes with parameters $ [119,4,88]_{4} $, $ [123,4,91]_{4} $, $ [124,4,92]_{4} $, $ [136,4,101]_{4} $, $ [140,4,104]_{4} $, $ [188,4,140]_{4} $ and $ [212,4,158]_{4} $ were constructed. Seven families of Hermitian LCD codes and their related EAQECCs were derived from these codes. These new EAQECCs have better parameters than those known in the literature.&lt;/p&gt;&lt;/abstract&gt;
Decrypting the cryptomarkets: Trends over a decade of the Dark Web drug trade

Introduction The Dark Web is a subsection of the Internet only accessible through specific search engines, making it impossible to trace users. Due to extensive anonymity, the drug trade on the Dark Web makes regulation complicated. We sought to uncover the scope of the online drug trade on the Dark Web and the impact it may have on the dynamics of global drug trafficking. Methods We conducted a literature review to elucidate the availability and distribution of drugs on the Dark Web based on data reported in existing literature ( n = 14) between September 2012 and June 2019. We simultaneously collected data about substances and listings from Dark Web cryptomarkets ( n = 13) active between August 2022 and January 2023. Data from the literature review and the Dark Web scrape were combined to draw trends in the chronological availability and distribution of drugs between 2012 and 2023. Results The data collected from 13 cryptomarkets between late 2022 and early 2023 showed a relative change in substance distribution compared to 2012–2019, with a decrease in prescription drugs (from &gt;20% to &lt;5%) and a doubling of opioid listings (from 5.5% to 9.25%), while no major changes were observed on average during 2012–2019 according to literature. Conclusions The Dark Web warrants more attention in the analysis of the global drug trade. Understanding Dark Web drug markets can inform targeted interventions and strategies to reduce drug-related harms, while ongoing research is necessary to anticipate and respond to future changes in the landscape of the illicit drug trade.