Reviewerly/exHarmony huggingface dataset:
CORDIC-Based FPGA Realization of a Spatially Rotating Translational Fractional-Order Multi-Scroll Grid Chaotic System

This paper proposes an algorithm and hardware realization of generalized chaotic systems using fractional calculus and rotation algorithms. Enhanced chaotic properties, flexibility, and controllability are achieved using fractional orders, a multi-scroll grid, a dynamic rotation angle(s) in two- and three-dimensional space, and translational parameters. The rotated system is successfully utilized as a Pseudo-Random Number Generator (PRNG) in an image encryption scheme. It preserves the chaotic dynamics and exhibits continuous chaotic behavior for all values of the rotation angle. The Coordinate Rotation Digital Computer (CORDIC) algorithm is used to implement rotation and the Grünwald–Letnikov (GL) technique is used for solving the fractional-order system. CORDIC enables complete control and dynamic spatial rotation by providing real-time computation of the sine and cosine functions. The proposed hardware architectures are realized on a Field-Programmable Gate Array (FPGA) using the Xilinx ISE 14.7 on Artix 7 XC7A100T kit. The Intellectual-Property (IP)-core-based implementation generates sine and cosine functions with a one-clock-cycle latency and provides a generic framework for rotating any chaotic system given its system of differential equations. The achieved throughputs are 821.92 Mbits/s and 520.768 Mbits/s for two- and three-dimensional rotating chaotic systems, respectively. Because it is amenable to digital realization, the proposed spatially rotating translational fractional-order multi-scroll grid chaotic system can fit various secure communication and motion control applications.
Local Binary Patterns Based on Neighbor-Center Difference Image for Color Texture Classification with Machine Learning Techniques

This is a topic that receives a lot of interest since many applications of computer vision focus on the detection of objects in visually appealing environments. Information about an object’s appearance and information regarding the object’s motion are both used as crucial signals in the process of identifying and recognising any given item. This information is used to characterise and recognise the item. The identification of objects based solely on their outward appearance has been the subject of a substantial amount of research. However, motion information in the recognition task has received only a marginal amount of attention, despite the fact that motion plays an essential role in the process of recognition. In order to analyze a moving picture in a way that is both fast and accurate, it is required to make use of motion information in conjunction with surface appearance in a strategy that has been designed. Dynamic texture is a kind of visual phenomenon that may be characterised as a type of visual phenomenon that shows spatially repeated features as well as some stationary properties during the course of time by using methodologies that are associated with machine learning. The design of modern VLSI systems takes into consideration a larger chip density, which results in a processor architecture with several cores that are capable of performing a wide range of functions (multicore processor architecture). It is becoming more challenging to run such complicated systems without the use of electric power. In order to increase the effectiveness of power optimization strategies while maintaining system performance for text data extraction, it has been developed and put into practice power optimization strategies that are based on scheduling algorithms. Over the last twenty years, texture analysis has been an increasingly busy and profitable field of study. Today, texture interpretation plays a vital role in various activities ranging from remote sensing to medical picture analysis. The absence of tools to newline analyze the many properties of texture pictures was the primary challenge faced by the texture analysis approach. Texture analysis may be roughly categorised as texture classification, texture segmentation, texture synthesis, and texture synthesis. Texture categorization is useful in numerous applications, such as the retrieval of picture databases, industrial agriculture applications, and biomedical applications. Texture categorization relies on three distinct methods, namely, statistical, spectral, and structural methods. Statistical methods are based on the statistical characteristics of the image’s grey level. Features are collected using second order statistical order, autocorrelation function, and grey level co-occurrence matrix function.
fairmodels: a Flexible Tool for Bias Detection, Visualization, and Mitigation in Binary Classification Models

Machine learning decision systems are becoming omnipresent in our lives. From dating apps to rating loan seekers, algorithms affect both our well-being and future. Typically, however, these systems are not infallible. Moreover, complex predictive models are eager to learn social biases present in historical data that may increase discrimination. If we want to create models responsibly, we need tools for in-depth validation of models also from potential discrimination. This article introduces an R package fairmodels that helps to validate fairness and eliminate bias in binary classification models quickly and flexibly. The fairmodels package offers a model-agnostic approach to bias detection, visualization, and mitigation. The implemented functions and fairness metrics enable model fairness validation from different perspectives. In addition, the package includes a series of methods for bias mitigation that aim to diminish the discrimination in the model. The package is designed to examine a single model and facilitate comparisons between multiple models.
Dissecting Latency in 360° Video Camera Sensing Systems

360° video camera sensing is an increasingly popular technology. Compared with traditional 2D video systems, it is challenging to ensure the viewing experience in 360° video camera sensing because the massive omnidirectional data introduce adverse effects on start-up delay, event-to-eye delay, and frame rate. Therefore, understanding the time consumption of computing tasks in 360° video camera sensing becomes the prerequisite to improving the system's delay performance and viewing experience. Despite the prior measurement studies on 360° video systems, none of them delves into the system pipeline and dissects the latency at the task level. In this paper, we perform the first in-depth measurement study of task-level time consumption for 360° video camera sensing. We start with identifying the subtle relationship between the three delay metrics and the time consumption breakdown across the system computing task. Next, we develop an open research prototype Zeus to characterize this relationship in various realistic usage scenarios. Our measurement of task-level time consumption demonstrates the importance of the camera CPU-GPU transfer and the server initialization, as well as the negligible effect of 360° video stitching on the delay metrics. Finally, we compare Zeus with a commercial system to validate that our results are representative and can be used to improve today's 360° video camera sensing systems.
A survey on deep learning-based single image crowd counting: Network design, loss function and supervisory signal

Single image crowd counting is a challenging computer vision problem with wide applications in public safety, city planning, traffic management, etc. With the recent development of deep learning techniques, crowd counting has aroused much attention and achieved great success in recent years. This survey is to provide a comprehensive summary of recent advances on deep learning-based crowd counting techniques via density map estimation by systematically reviewing and summarizing more than 200 works in the area since 2015. Our goals are to provide an up-to-date review of recent approaches, and educate new researchers in this field the design principles and trade-offs. After presenting publicly available datasets and evaluation metrics, we review the recent advances with detailed comparisons on three major design modules for crowd counting: deep neural network designs, loss functions, and supervisory signals. We study and compare the approaches using the public datasets and evaluation metrics. We conclude the survey with some future directions.
Steganography with High Reconstruction Robustness: Hiding of Encrypted Secret Images

As one of the important methods to protect information security, steganography can ensure the security of data in the process of information transmission, which has attracted much attention in the information security community. However, many current steganography algorithms are not sufficiently resistant to recent steganalysis algorithms, such as deep learning-based steganalysis algorithms. In this manuscript, a new steganography algorithm, based on residual networks and pixel shuffle, is proposed, which combines image encryption and image hiding, named Resen-Hi-Net, an algorithm that first encrypts a secret image and then hides it in a carrier image to produce a meaningful container image. The proposed Resen-Hi-Net has the advantages of both image encryption and image hiding. The experimental results showed that the proposed Resen-Hi-Net could realize both image encryption and image hiding; the visual container image quality was as high as 40.19 dB on average in PSNR to reduce the possibility of being attacked, and the reconstructed secret image quality was also good enough (34.39 dB on average in PSNR). In addition, the proposed Resen-Hi-Net has a strong ability to resist destructive attacks and various steganographic analyses.
Machine-Learning-Based Digital Twin System for Predicting the Progression of Prostate Cancer

Clinical decision support systems (CDSSs) enable users to make decisions based on clinical data from electronic medical records, facilitating personalized precision medicine treatments. A digital twin (DT) approach enables the interoperability between physical and virtual environments through data analysis using machine learning (ML). By combining DT with the prostate cancer (PCa) process, it is possible to predict cancer prognosis. In this study, we propose a DT-based prediction model for clinical decision-making in the PCa process. Pathology and biochemical recurrence (BCR) were predicted with ML using data from a clinical data warehouse and the PCa process. The DT model was developed using data from 404 patients. The BCR prediction accuracy increased according to the amount of data used, and reached as high as 96.25% when all data were used. The proposed DT-based predictive model can help provide a clinical decision support system for PCa. Further, it can be used to improve medical processes, promote health, and reduce medical costs and problems.
Graph Mixed Random Network Based on PageRank

In recent years, graph neural network algorithm (GNN) for graph semi-supervised classification has made great progress. However, in the task of node classification, the neighborhood size is often difficult to expand. The propagation of nodes always only considers the nearest neighbor nodes. Some algorithms usually approximately classify by message passing between direct (single-hop) neighbors. This paper proposes a simple and effective method, named Graph Mixed Random Network Based on PageRank (PMRGNN) to solve the above problems. In PMRGNN, we design a PageRank-based random propagation strategy for data augmentation. Then, two feature extractors are used in combination to supplement the mutual information between features. Finally, a graph regularization term is designed, which can find more useful information for classification results from neighbor nodes to improve the performance of the model. Experimental results on graph benchmark datasets show that the method of this paper outperforms several recently proposed GNN baselines on the semi-supervised node classification. In the research of over-smoothing and generalization, PMRGNN always maintains better performance. In classification visualization, it is more intuitive than other classification methods.
Solving boolean satisfiability problems with the quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) is one of the most prominent proposed applications for near-term quantum computing. Here we study the ability of QAOA to solve hard constraint satisfaction problems, as opposed to optimization problems. We focus on the fundamental boolean satisfiability problem, in the form of random $k$-SAT. We develop analytic bounds on the average success probability of QAOA over random boolean formulae at the satisfiability threshold, as the number of variables $n$ goes to infinity. The bounds hold for fixed parameters and when $k$ is a power of 2. We complement these theoretical results with numerical results on the performance of QAOA for small $n$, showing that these match the limiting theoretical bounds closely. We then use these results to compare QAOA with leading classical solvers. In the case of random 8-SAT, we find that for around 14 ansatz layers, QAOA matches the scaling performance of the highest-performance classical solver we tested, WalkSATlm. For larger numbers of layers, QAOA outperforms WalkSATlm, with an ultimate level of advantage that is still to be determined. Our methods provide a framework for analysing the performance of QAOA for hard constraint satisfaction problems and finding further speedups over classical algorithms.
Challenges in making blockchain privacy compliant for the digital world: some measures

Due to the pandemic, most of the personal transactions relating to finance, commerce and healthcare services have gone online making privacy preservation a critical requirement. Consequently, privacy has been made a critical parameter in Data Protection Regulations leading to the search for such a privacy compliant system which is also resilient to attacks. A detailed analysis of the Blockchain technology, which is becoming popular for secure applications in the finance sector, indicates that there are several challenges relating to user identity, transaction linkability, crypto-keys management, data privacy, usability, interoperability, and post- quantum compliance of privacy regulations which need to be resolved before its widespread adoption. Being a decentralised system, there is a need to analyse the vulnerability to attacks of each layer in the Blockchain architecture. This paper discusses the development flow of some of the privacy enhancement mechanisms like ZKPs, SMPC, Ring signatures, Mixing, Homomorphic Encryption and quantum resilient computing, bringing out their features and lacunae. There is a detailed discussion of the privacy mechanisms adopted by blockchain platforms like ZCash, Zerocoin, Hyperledger, Wanchain, Coin Party, Monero, Cryptomate, MixCoin, Coinshuffle, PICNIC and New Hope. Every platform has some limitation or the other and it is essential that researchers come out with mitigation steps for the existing mechanisms and come up with improved new Privacy Enhancement Techniques. One such architecture using PET has been proposed.
Low-Light Image Enhancement Based on Constraint Low-Rank Approximation Retinex Model

Images captured in a low-light environment are strongly influenced by noise and low contrast, which is detrimental to tasks such as image recognition and object detection. Retinex-based approaches have been continuously explored for low-light enhancement. Nevertheless, Retinex decomposition is a highly ill-posed problem. The estimation of the decomposed components should be combined with proper constraints. Meanwhile, the noise mixed in the low-light image causes unpleasant visual effects. To address these problems, we propose a Constraint Low-Rank Approximation Retinex model (CLAR). In this model, two exponential relative total variation constraints were imposed to ensure that the illumination is piece-wise smooth and that the reflectance component is piece-wise continuous. In addition, the low-rank prior was introduced to suppress the noise in the reflectance component. With a tailored separated alternating direction method of multipliers (ADMM) algorithm, the illumination and reflectance components were updated accurately. Experimental results on several public datasets verify the effectiveness of the proposed model subjectively and objectively.
Design and Implementation of Convolutional Neural Networks Accelerator Based on Multidie

To achieve real-time object detection tasks with high throughput and low latency, this paper proposes a multi-die hardware accelerator architecture.It implements three accelerators on the VU9P chip, each of which is bound to an independent super logic region (SLR).To reduce off-chip memory access and power consumption, this design uses three on-chip buffers to store the weights and intermediate result data on one hand; on the other hand, it minimizes data access and movement and maximizes data reuse.This design uses an 8-bit quantization strategy for both weights and feature maps to achieve twice the throughput and computational efficiency of a single digital signal processor (DSP).In addition, many operators are designed in the accelerator, and all of them are fully parameterized, so it is easy to extend the network, and the control of the accelerator can be realized by configuring the instruction group.By accelerating the YOLOv4-tiny algorithm, the accelerator architecture can achieve a frame rate of 148.14 frames per second (FPS) and a peak throughput of 2.76 tera operations per second (TOPS) at 200MHz with an energy efficiency ratio of 93.15 GOPS/W.Code is available at https://github.com/19801201/Verilog_CNN_Accelerator.
A Novel Reversible Data Hiding Algorithm Based on Enhanced Reduced Difference Expansion

Reversible data hiding is a data-hiding technique which has the ability to recover the original version from stego-images after the secret information is extracted. In this paper, we propose a novel reversible data-hiding scheme based on an enhanced reduced difference-expansion technique. In the proposed scheme, the original image is divided into non-overlapping quad-blocks for embedding data. Then, to enhance the security, the secret bits are encrypted based on the encryption key and a symmetry-based strategy. To improve embedding capacity further, two adjacent encrypted bits are converted into a corresponding decimal digit. Difference expansion (DE) technique is applied to embed a decimal form instead of a binary version. Moreover, to maintain the good image quality, the enhanced reduced difference-expansion technique is used to reduce the original difference values so that it is suitable for decimal embedding. The experimental results demonstrated that the proposed scheme has achieved better performance in comparison with previous solutions.
Insulators’ Identification and Missing Defect Detection in Aerial Images Based on Cascaded YOLO Models

Insulators identification and their missing defect detection are of paramount importance for the intelligent inspection of high-voltage transmission lines. As the backgrounds are complex, some insulators may be occluded, and the missing defect of the insulator is so small that it is not easily detected from aerial images with different backgrounds. To address the above issues, in this study, a cascaded You Only Look Once (YOLO) models are mainly explored to perform insulators and their defect detection in aerial images. Firstly, the datasets used for insulators location and missing defect detection are created. Secondly, a new model is proposed to locate the position of insulators, which is improved in the feature extraction network and multisacle prediction network based on previous YOLOv3-dense model. An improved YOLOv4-tiny model is used to conduct missing defect detection on the detected insulators. And then, the proposed YOLO models are trained and tested on the built datasets, respectively. Finally, the final models are cascaded for insulators identification and their missing defect detection. The average precision of missing defect detection can reach 98.4%, which is 5.2% higher than that of faster RCNN and 10.2% higher than that of SSD. The running time of the cascaded YOLO models for missing defect detection can reach 106 frames per second. Extensive experiments demonstrate that the proposed deep learning models achieve good performance in insulator identification and its missing defect detection from the inspection of high-voltage transmission lines.
Simple and Economical Shearography System for Testing of Aeronautical Composite Material

To detecting delaminations in honeycomb structures, shearography has great capability. It is commonly used by the aerospace industries for non-destructive testing (NDT). In this paper, we describe a simple and inexpensive shearography system suitable for routine control of composite material. In particular, the system is suitable for detecting delamination deriving from impact at low-velocity (tool drop, bird-strike, runway debris, etc.).
Analysis of e-Mail Spam Detection Using a Novel Machine Learning-Based Hybrid Bagging Technique

e-mail service providers and consumers find it challenging to distinguish between spam and nonspam e-mails. The purpose of spammers is to spread false information by sending annoying messages that catch the attention of the public. Various spam identification techniques have been suggested and evaluated in the past, but the results show that the more research in this regard is required to enhance accuracy and to reduce training time and error rate. Thus, this research proposes a novel machine learning-based hybrid bagging method for e-mail spam identification by combining two machine learning methods: random forest and J48 (decision tree). The proposed framework categorizes the e-mail into ham and spam. The database is split into multiple sets and provided as input to each method in this procedure. Moreover, tokenization, stemming, and stop word removal are performed in the preprocessing stage. Further, correlation feature selection (CFS) is employed in this research to select the required features from the preprocessed data. The effectiveness of the presented method is evaluated in terms of true-negative rates, accuracy, recall, precision, false-positive rate, f-measure, and false-negative rate; the outcomes of three studies are compared. According to the results, the presented hybrid bagged model-based SMD technology achieved 98 percent accuracy.
Factors of Risk Analysis for IoT Systems

The increasing rate at which IoT technologies are being developed has enabled smarter and innovative solutions in the sectors of health, energy, transportation, etc. Unfortunately, some inherent characteristics of these technologies are compromised to attack. Naturally, risk analysis emerges, as it is one of many steps to provide a reliable security strategy. However, the methodologies of any risk analysis must first adapt to the dynamics of the IoT system. This article seeks to shed light on whatever factors are part of an IoT system and thus contribute to security risks, IoT device vulnerabilities, susceptibility due to the application domain, attack surfaces, and interdependence as a product of the interconnection between IoT devices. Consequently, the importance of these factors in any risk evaluation is highlighted, especially the interdependence generated by IoT systems, which can cause the generation of an uncontrollable cascade of effects that can occur under certain conditions of any systematic risk event.