Reviewerly/exHarmony huggingface dataset:
Real‐time fusion of multiple videos and <scp>3D</scp> real scenes based on optimal viewpoint selection

Abstract 3D real scenes are a digital virtual space that represents and portrays the real world in a photorealistic, three‐dimensional, and sequential manner. Existing methods for constructing and updating 3D models, such as oblique photography and laser scanning, are difficult to meet the demand of perceiving the real world intuitively, dynamically, and in real time. In recent years, the method of integrating rapidly rising video data and 3D models has become increasingly popular. Compared with existing methods, it enhances the real‐time perception of 3D scenes by taking advantage of the real‐time character of videos and the intuitive character of 3D models. In this article, we propose a real‐time fusion method of multiple videos and 3D real scenes based on optimal viewpoint selection. To begin, 3D reconstruction and video camera calibration were used to prepare the basic data for the fusion of videos and 3D model. Second, a visible‐surface detection‐based video space restoration method was provided, and the overlapping region between multiple videos was determined. Third, to split the overlapping region into the corresponding camera spaces, a segmentation method based on optimal viewpoint selection was given. Finally, the 2D videos were dynamically fitted to the 3D model using the dynamic texture mapping method, while accomplishing the fusing and rendering of the 3D real scene. After experimental verification, the overall effect of the multiple videos and 3D real scene fusion system implemented using the method proposed in this article is better, while the algorithm is less time‐consuming and efficient in rendering.
Diagnosis of heart disease using oversampling methods and decision tree classifier in cardiology

Heart disease is one of the most prevalent and critical diseases that endangers the lives of human beings. In addition to clinical diagnosis, machine learning and deep learning-based approaches are vital in the diagnosis of heart disease. This paper proposes a balanced and optimized machine-learning algorithm for heart disease detection. This technique combines oversampling techniques, attribute pruning, CART decision tree classifier, and rule pruning through hyper-parameter tuning to identify the presence of heart disease. It further identifies the key attributes that contribute to the occurrence of heart malfunctioning. Experimental results show that SMOTE sampled dataset exhibits effective performance when implemented using a balanced and optimized machine learning algorithm, with an improvement of 11%, 75%, 62%, and 71% in accuracy, precision, recall, and f1 scores when compared with the dataset that was not subjected to sampling. The algorithm works effectively when the imbalance ratio is high for a dataset. The algorithm can be used to predict the presence of heart disease even in highly imbalanced datasets and identify critical features in the malfunctioning of the heart.
Quantum Cryptography for Securing Personal Health Information in Hospitals

Healthcare systems widely use information technology (IT) for system authentication (digital signatures), web surfing, e-mails, instant messaging, protecting data at rest, Voice over Internet Protocol (VoIP) telephony, and cellular telephony.To protect patient identification and healthcare information, cryptographic systems are widely used to secure these data from malicious third parties (adversaries).In our healthcare systems, we have had reasonable success in the efficient storage of the information of our patients and their families, in its timely retrieval, and in ensuring its safety from adversaries.However, the data are increasing rapidly and our current computational systems could be inadequate in the not-sodistant future.In this context, there is a need for novel solutions.One possibility can be seen in quantum computing (QC) algorithms/devices that can provide elegant solutions based on subatomic interactions.In this review, we have summarized current information on the need, current options, and future possibilities for the use of QC algorithms/devices in large data systems such as healthcare.This article combines peerreviewed evidence from our own clinical studies with the results of an extensive literature search in the databases PubMed, EMBASE, and Scopus.
Design a Fully Automated Safety and Security System for Swimming Pools

The concept of a fully automated safety and security system for swimming pools is discussed in this paper, focusing on offering 24-hour of protection against drowning while swimming or when inadvertently falling into the water. The goal of this study, thus, is to reduce the number of unintentional deaths and drowning injuries by developing an efficient system that can take an appropriate action without any human intervention. The system monitors the swimmer’s oxygen level and takes action based on the measured values by raising the safety net if a certain threshold is exceeded or pressing the bracelet panic button if something happens while swimming, such as muscle cramps. Furthermore, when no one is using the pool, it will be covered with the safety net equipped with a vibration sensor that will sound an alarm if a body falls into the pool.
Distinguish normal white blood cells from leukemia cells by detection, classification, and counting blood cells using YOLOv5

Leukemia is a group of blood cancers that usually begin in the bone marrow and result in high numbers of abnormal blood cells. Distinguishing normal white blood cells from leukemia cells plays a role in the diagnosis of blood diseases. Over the years, manual methods can detect, classify, and count blood cells that are labor-intensive slow, time-consuming, subject to error, and costly. An automatic system is applied in the processing detection through microscopic images that will be fast, cheap, and high accuracy without special needs of equipment for lab testing. In this paper, we propose a method of detection, classification, and counting to distinguish five types of normal white blood cells (WBCs) from abnormal white blood cells (leukemia) using the You Only Look Once version 5 (YOLOv5) to aid the diagnosis of blood diseases. After using the YOLOv5 algorithm, we manually labeled the blood cells, and the leukemia cells labeled are the largest with 619 cells, while the number of normal WBCs labeled is a basophil, 115 neutrophils, 23 eosinophils, 80 lymphocytes, and 73 monocytes, respectively. The tested result with the average accuracy of detection and classification of the blood cells is 93.0%.
6G Decentralization

Decentralization was a key design objective underlying the design of the Internet, with independent networks collaborating to deliver packet network services. As web services become more central to daily activities, Web 3.0 seeks to use decentralization to disrupt the digital sovereignty of proprietary Web 2.0 platforms. Decentralization remains a tool for economic regulation to deter market dominance or anti-trust monopoly influences that can distort online services While public network infrastructures like 5G have been evolving native support for a greater variety of services decentralization has not been a major focus. With 6G requirements gathering in process, will decentralization become a key dimension of 6G performance?
Comparison of color features on copy-move forgery detection problem using HSV color space

ABSTRACTCopy-move forgery (CMF) is a variety of image faking in which parts of an image are duplicated and added inside the same image. Up to now, many applications have been proposed to expose this type of forgery. These algorithms were developed to detect suspicious images containing CMF. However, when attacks such as compression and noise addition are applied to the image in such forgery operations, CMF detection (CMFD) approaches have difficulty in recognizing the fake images. This study presents a keypoint-based CMFD approach that performs well under various post-transaction changes. This is achieved by extracting scale-invariant feature transform (SIFT) and KAZE features using the colour channels in the HSV colour space instead of greyscale images. After the SIFT and KAZE descriptors have been extracted from each colour channel, they are combined to form the feature that is used in the matching step to detect similar regions. Thus, the proposed CMFD approach is more effective because stronger features are obtained when they are combined. The test findings on the Image Manipulation Dataset (IMD) demonstrated that our technique had successfully identified CMF under a variety of post-transaction alterations.KEYWORDS: Copy-move forgerySIFTKAZEcolour features Disclosure statementNo potential conflict of interest was reported by the author(s).Author contributionsYıΙdız Aydin contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript.Data availability statementPlease contact the authors for data requests.Correction StatementThis article has been corrected with minor changes. These changes do not impact the academic content of the article.
Real-Time Solution of Unsteady Inverse Heat Conduction Problem Based on Parameter-Adaptive PID with Improved Whale Optimization Algorithm

To solve the problem of the common unsteady inverse heat conduction problem in the industrial field, a real-time solution method of improving the whale optimization algorithm (IWOA) and parameter-adaptive proportional-integral-differential (PID) is proposed in the paper. A feedback control system with IWOA-PID, which can inversely solve the boundary heat flux, is established. The deviation between the calculated temperature and the measured temperature of the measured point obtained by solving the direct heat conduction problem (DHCP) is used as the system input. The heat flux which is iteration-solved by IWOA-PID is used as system output. The method improves the initial solution distribution, global search capability and population diversity generalization of the traditional whale optimization algorithm (WOA), which effectively improves the parameter-adaptive capability of PID. The experimental results show that the solution method of inverse heat transfer proposed in the paper can accurately retrieve the variation of the boundary heat flux in real time and has good resistance and self-adaptability.
Financial Fraud Detection of Listed Companies in China: A Machine Learning Approach

As the focus of capital market supervision, financial report fraud has shown a development trend of enormous numbers, complex transactions, and hidden means in recent years. To improve audit efficiency and reduce the dependence on non-financial data, the study only uses the structured original data in the financial report to constructs a new fraud identification model, which can quickly detect fraud in China. This study takes the listed companies in China from 1998 to 2016 as research samples and selects 28 sets of raw data from financial reports. Then, this study compares the detection effectiveness of two single classification machine learning algorithms and five ensemble learning algorithms on fraud detection. Compared with single classification machine learning algorithms, the results show that ensemble learning algorithms are generally better at detecting fraud for Chinese listed companies, and the stacking algorithm performs the best. The study results provide direct evidence for rapid fraud detection using financial report raw data and ensemble learning algorithms. The study first proposes a stacking algorithm-based financial reporting fraud identification model for listed companies in China, which provides a simple and effective approach for investors, regulators, and management. It can also provide a reference for the detection of other fraud scenarios.
Deep-learning-based parallel single-pixel imaging for efficient 3D shape measurement in the presence of strong interreflections by using sampling Fourier strategy

Strong interreflections cause intractable systematic errors to traditional 3D shaping methods, for example, fringe projection profilometry. Parallel single-pixel imaging (PSI) captures the light transport coefficients between the camera projector pair, thus overcoming the influence of interreflections. However, PSI requires a large number of measurements, causing poor capturing efficiency. To achieve efficient 3D shape measurement in the presence of strong interreflections, we present a deep-learning-based parallel single-pixel imaging method (dlPSI), featuring adopting a deep-learning network to achieve accurate light transport coefficient reconstruction and using sampling Fourier strategy to reduce measurements. The deep-learning network is designed to suppress the noise caused by undersampling the Fourier domains. Experiments prove that our dlPSI with the characteristic-based deterministic Fourier sampling strategy achieves high-quality 3D shape measurement while saving 94% of measurements.
Discovering onion services through circuit fingerprinting attacks

Tor onion services provide anonymous service to clients using the Tor browser without disclosing the real address of the server. But an adversary could use a circuit fingerprinting attack to classify circuit types and discovers the network address of the onion service. Recently, Tor has used padding defenses to inject dummy cells to protect against circuit fingerprinting attacks. But we found that circuits still expose much information to the adversary. In this paper, we present a novel circuit fingerprinting attack, which divides the circuit into the circuit generated by the client and the circuit generated by the onion service. To get a more effective attack, we tried three state-of-the-art classification models called SVM, Random Forest and XGBoost, respectively. As the best performance, we attain 99.99% precision and 99.99% recall when using Random Forest and XGBoost classification models, respectively. And we also tried to classify circuit types using our features and the classification model mentioned above, which was first proposed by Kwon. The best performance was achieved with 99.99% precision and 99.99% recall when using the random forest classifier in circuit type classification. The experimental results show that we achieved highly accurate circuit fingerprinting attacks even when application-layer traffic is identical and some type of circuits using the defenses provided by Tor.
A comparative study of item space visualizations for recommender systems

Recommender systems aim at supporting users in their search and decision making process by selecting a small number of likely relevant items from a large set of options. Although automatically filtering unmanageably large item sets down to a few recommendations often produces results that match the user’s interests well, it also prevents users from understanding and exploring items in their larger context. This may reduce users’ perception of transparency and controllability of the system. Visualizations have been proposed as a means for overcoming this problem, with some visualizations providing a complete overview of the entire space of available items. However, thus far item space visualizations have rarely been investigated and compared in user studies. To address this, we developed and empirically compared three applications that present the user with personalized music recommendations embedded in a visualization of the entire item space. The three applications display the same item space as a list, as a treemap, and as a map, respectively. We compared these applications in an online user study and found, against our expectations, that they did not differ much in how the recommendations are perceived. Perception of transparency, recommendation quality, and degree of control over the recommendations received relatively high scores over all three applications. However, we did find a difference in hedonic user experience and perceived novelty of the recommendations. Both factors were perceived to be higher in the map condition. Backed up by a mediation analysis, we argue that a halo effect is the reason for the observed perceived novelty: participants transferred the novelty of the application to the novelty of the recommendations.
Incorporating software security: using developer workshops to engage product managers

Abstract Evidence from data breach reports shows that many competent software development teams still do not implement secure, privacy-preserving software, even though techniques to do so are now well-known. A major factor causing this is simply a lack of priority and resources for security, as decided by product managers. So, how can we help developers and product managers to work together to achieve appropriate decisions on security and privacy issues? This paper explores using structured workshops to support teams of developers in engaging product managers with software security and privacy, even in the absence of security professionals. The research used the Design Based Research methodology. This paper describes and justifies our workshop design and implementation, and describes our thematic coding of both participant interviews and workshop discussions to quantify and explore the workshops’ effectiveness. Based on trials in eight organizations, involving 88 developers, we found the workshops effective in helping development teams to identify, promote, and prioritize security issues with product managers. Comparisons between organizations suggested that such workshops are most effective with groups with limited security expertise, and when led by the development team leaders. We also found workshop participants needed minimal guidance to identify security threats, and a wide range of ways to promote possible security improvements. Empowering developers and product managers in this way offers a powerful grassroots approach to improve software security worldwide.
A Player-Specific Framework for Cricket Highlights Generation Using Deep Convolutional Neural Networks

Automatic ways to generate video summarization is a key technique to manage huge video content nowadays. The aim of video summaries is to provide important information in less time to viewers. There exist some techniques for video summarization in the cricket domain, however, to the best of our knowledge our proposed model is the first one to deal with specific player summaries in cricket videos successfully. In this study, we provide a novel framework and a valuable technique for cricket video summarization and classification. For video summary specific to the player, the proposed technique exploits the fact i.e., presence of Score Caption (SC) in frames. In the first stage, optical character recognition (OCR) is applied to extract text summary from SC to find all frames of the specific player such as the Start Frame (SF) to the Last Frame (LF). In the second stage, various frames of cricket videos are used in the supervised AlexNet classifier for training along with class labels such as positive and negative for binary classification. A pre-trained network is trained for binary classification of those frames which are attained from the first phase exhibiting the performance of a specific player along with some additional scenes. In the third phase, the person identification technique is employed to recognize frames containing the specific player. Then, frames are cropped and SIFT features are extracted from identified person to further cluster these frames using the fuzzy c-means clustering method. The reason behind the third phase is to further optimize the video summaries as the frames attained in the second stage included the partner player’s frame as well. The proposed framework successfully utilizes the cricket videoo dataset. Additionally, the technique is very efficient and useful in broadcasting cricket video highlights of a specific player. The experimental results signify that our proposed method surpasses the previously stated results, improving the overall accuracy of up to 95%.
Key frame extraction based on quaternion Fourier transform with multiple features fusion

Key frame extraction, an important technique to improve video viewing efficiency and reduce video redundancy, has attracted considerable attention. Notwithstanding, existing key frame extraction for surveillance video not only fails to extract the local detail information of the target accurately but also preprocess the image with grayscale, so the integrity of the image information is also damaged. In order to solve the above-described problems, the paper presents a key frame extraction method based on the quaternion Fourier transform with the multiple features fusion. Initially, the method extracts dynamic and static features that can characterize the color image information separately. Then, the quaternion matrix is used to perform the quaternion Fourier transform on the different features to obtain the fused phase spectrum of the Fourier transform. Immediately after, the information containing the overall structure of the image is filtered by Gaussian filtering, and the quaternion Fourier inverse transform is performed to obtain the fused feature map. Finally, an adaptive key frame filtering criterion, which uses the fused feature map to extract the key frames is constructed in this paper for accurately extracting key frames. Notably, experimental results indicate that the proposed method ensures the integrity of the image information while accurately capturing the global and local motion state of the target.
Multiple extended target tracking based on gamma box particle and labeled random finite sets

A gamma box particle (GBP) implementation of the extended target generalized labeled multi-Bernoulli (ET-GLMB) filter is proposed in this paper, named the ET-GBP-GLMB filter. The filter can simultaneously estimate the unknown number, measurement rate states, kinematics states, extension states and tracks of extended targets in the presence of clutter, missed detection and data association uncertainty. In order to obtain the GLMB recursion of the ET-GBP-GLMB filter, an appropriate measurement likelihood function that comprehensively considers the measurement rate state, kinematics state and extension state of each extended target is introduced for a given measurement partition cell, and the specific implementation steps of the filter are derived with necessary interval operations and approximations. Further, an extended target GBP labeled multi-Bernoulli (ET-GBP-LMB) filter is proposed as an effective approximation of the ET-GBP-GLMB filter. The advantages and disadvantages of the two proposed filters are presented in simulation examples. The simulation results show that, compared with the gamma sequential Monte Carlo (GSMC) implementation of the ET-GLMB filter, the GBP implementation of the ET-GLMB filter can effectively reduce the number of particles and the amount of computation. Compared with the ET-GBP-GLMB filter, the ET-GBP-LMB filter can further reduce the computational burden. Compared with the extended target box particle probability hypothesis density (ET-BP-PHD) filter, the two proposed filters can obtain better estimation results.
BRNet: Bi-residual network with aggregate connection

ResNet and its variants focusing on the trunk or skip-connection are the leading backbone networks. In this paper, we propose a bi-residual network (BRNet) by extending the residual block with the aggregate connection to extract salient information and achieve global feature representation from feature maps. In addition to the skip-connection that copy the information from the former layers directly, the devised aggregate connection exploits the average pooling to extract global information, and Relu to keep salient information from the feature maps. On one hand, BRNet can act as a plug-and-play module in the backbone networks like ResNet and its variants. On the other hand, BRNet can be further combined with the variants focusing on the trunk of ResNet, such as SENet, etc. The experiments conducted on CIFAR, ImageNet and COCO datasets show that BRNet achieves significant performance on various scenarios.