Reviewerly/exHarmony huggingface dataset:
Providing robust and reversible watermarking algorithm and detection of malicious manipulation

Abstract In this article, due to the importance of embedding encrypted information in the image, a watermarking method is proposed based on local statistics in pixels. The advantages of the proposed method include its complete reversibility and very high security, which is due to the use of the quadtree concept, finding similar parts in the image, and embedding information in low‐energy and less significant pixels. This method is an alternative and simplifier of the quadtree method, dividing the image into identical non‐overlapping blocks and then choosing the block with less energy as more suitable for watermarking. Also, two criteria of mean‐squared‐error and peak signal‐to‐noise ratio (PSNR) have been calculated and the PSNR shows that image quality has not changed too much after hiding the information than the original image. It should be noted that since the proposed method is applied to black and white images, the result can be generalized to different color channels in the image, and color images have more visible capabilities in terms of cached memory than black and white images because they contain three dimensions of information.
A Secure Two-Tier Domain Verification and Certificate Validation Integrating Intermediate Certificate Authorities and Secure Certificate Box

Digital certificates play a key role in the public key infrastructure (PKI). They are mainly used to secure communication between a browser and a webserver to keep the transmitted data safe from interception. Digital certificates are issued by certificate authorities (CAs). Numerous CAs serve as root CAs around the world. Moreover, root CAs can delegate intermediate CAs (ICAs). Any CA can issue a certificate for any entity on the internet world. However, it will be difficult for an end user to identify which authorities are trustworthy and genuine. In addition, adversaries can quickly induce fraudulent certificates due to a lack of rigorous background checks for domain possession at the time of certificate issuance. Our work is primarily oriented towards incorporating the domain validation and certificate validity check from the client side as an additional security fortification. This is in addition to a novel associative approach for domain validation using ICAs. This paper presents a novel two-tier system for domain validation of servers. The system operates at two levels. At the first level, an associative validation of the domains by making use of multiple ICAs is employed. A set of CAs validates domain ownership before issuing the certificate to prevent false issuance of the certificate. In addition to this, during the initiation of the client–server connection, the client itself validates the certificate to make sure that the certificate received securely from any webserver is a genuine one.
A Tale of EDA’s Long Tail: Long-Tailed Distribution Learning for Electronic Design Automation

Long-tailed distribution is a common and critical issue in the field of machine learning. While prior work addressed data imbalance in several tasks in electronic design automation (EDA), insufficient attention has been paid to the long-tailed distribution in real-world EDA problems. In this paper, we argue that conventional performance metrics can be misleading, especially in EDA contexts. Through two public EDA problems using convolutional neural networks and graph neural networks, we demonstrate that simple yet effective model-agnostic methods can alleviate the issue induced by long-tailed distribution when applying machine learning algorithms in EDA.
Blockchain: The Vibrant Technology for the Management of a Smart Cities

In recent decades, more people inhabit cities than rural areas, due to the increase in population, change of climate, and shortage of resources. Many countries are emphasizing the transformation of the city into a smart city for the effective utilization of the opportunities of urbanization. Today, smart cities are one of the thrust areas of many governments across the globe. Smart cities are being planned to promote a pollution-free environment and facilitate smart infrastructure. To enable operational efficiencies and to achieve sustainable economic growth in smart cities, innovative technologies are necessary. This book chapter focuses on the current urban challenges arising during the management of smart cities. It discusses one of the vibrant technologies – blockchain technology and its usage for the management of smart cities. Further, it explores the current scenario of global blockchain technology initiatives. It studies various domains of a smart city and also recognizes the domain where blockchain technology can be implemented for better livability and development of cities. Also, it focuses on the various use cases of blockchain in developing the smart city. Finally, it explains the practical feasibility for the successful implementation of blockchain technology briefly.
Comparative Analysis of Hardware Implementations of a Convolutional Neural Network

Artificial Neural Networks (ANNs) have become the most popular machine learning technique for data processing, performing central functions in a wide variety of applications. In many cases, these models are used within constrained scenarios, in which a local execution of the algorithm is necessary to avoid latency and safety issues of remote computing (e.g, autonomous vehicles, edge devices in IoT networks). Even so, the known computational complexity of these models is still a challenge in such contexts, as implementation costs and performance requirements are difficult to balance. In these scenarios, pa-rameter quantization techniques are essential to simplifying the operations and memory footprint to make the hardware implementation more viable. In this paper, a case study is devised in which a convolutional neural network (CNN) architecture is fully implemented in hardware with three different optimization strategies, having parameters mapped to low bit-width fixed point integers with a power-of-two quantization scheme. Both ASIC and FPGA implementation flows are followed, allowing for an in-depth analysis of each circuit version. The obtained results show that the adopted quantization process enables optimizations on the implemented circuit, reducing about 50% of the circuitry area and 87.5% of the memory requirement. At the same time, the application performance was kept at the same level.
A Study of Motion Coding Schemes for Learned Video Compression

This paper presents a study of motion coding schemes for learned video compression. Most learned video compression systems explicitly signal optical flow maps to characterize motion between video frames for motion compensation. The flow maps, usually of the same size as the video frames, represent a considerable portion of the compressed bitstream. This work studies several schemes to make a non-linear prediction of the flow maps for efficient motion coding. These include signaling an incremental flow map between a coding frame and a motion-compensated frame derived from the flow map predictor. In forming the flow map predictor, we propose a learned motion extrapolation module and a motion forward warping scheme. They are further incorporated into two novel approaches, termed double warping and frame synthesis with motion forward warping, in creating an inter-frame predictor by combining the incremental flow and the flow map predictor. Extensive experiments are conducted to analyze the merits and faults of these variants, and demonstrate their superiority to predictive motion coding and intra motion coding.
eSi-BTC: an energy efficient Bitcoin mining core

Bitcoin is the most well-adopted cryptocurrency today. As of 2022, it is estimated that around US$ 4 billion are transacted daily within the Bitcoin network. Optimizing the energy expenditure of Bitcoin mining is of interest to several parties, such as end-users, who make transactions on the Bitcoin network with lower fees, miners themselves, who increase mining profits due to lower operational costs, and government regulation bodies, who minimize the environmental and power-grid impacts of mining. In this work we present a Bitcoin mining core employing several optimizations and design techniques that aim to maximize energy efficiency. Results in a 6 nm technology node show a 33% improvement in energy efficiency when compared to the state-of-the-art implementation.
LETRNG—A Lightweight and Efficient True Random Number Generator for GNU/Linux Systems

Unpredictable and irreproducible digital keys are required to modulate security-related information in secure communication systems.True random number generators (TRNGs) rather than pseudorandom number generators (PRNGs) are required for the highest level of security.TRNG is a significant component in the digital security realm for extracting unpredictable binary bitstreams.Presently, most TRNGs extract high-quality "noise" from unpredictable physical random phenomena.Thus, these applications must be equipped with external hardware for collecting entropy and converting them into a random digital sequence.This study introduces a lightweight and efficient true random number generator (LETRNG) that uses the inherent randomness of a central processing unit (CPU) and an operating system (OS) as the source of entropy.We then utilize a lightweight post-processing method based on XOR and fair coin operation to generate an unbiased random binary sequence.Evaluations based on two famous test suites (NIST and ENT) show that LETRNG is perfectly capable of generating high-quality random numbers suitable for various GNU/Linux systems.
Information Purification Network for Remote Sensing Image Super-Resolution

Recently, several well-performing deep convolutional neural networks were proposed for remote sensing image super-resolution (SR). However, these methods rarely consider that remote sensing images are corruptible by additional noise, blurring, and other factors. Therefore, to eliminate the interference of these factors, especially the noise, we propose a novel information purification network (IPN) for remote sensing image SR. The proposed information purification block (IPB) can process channel-wise features differently by channel separation and rescale spatial-wise features adaptively through the proposed multi-scale spatial attention mechanism. We further design an information group to explore a more powerful expressive combination of IPBs. Moreover, long and short skip connections can transmit abundant low-frequency information, making IPBs pay more attention to high-frequency information. We mix the images under various degradation models as training data in the training phase. In this way, the network can directly reconstruct various degraded images. Experiments on AID and UC Merced Land-Use datasets under multiple degradation models demonstrate that the proposed IPN performs better than state-of-the-art methods.
Modified Fizeau interferometer with the polynomial and FFT smoothing algorithm

The modified Fizeau interferometer that allows to diagnose plane and spherical optical elements with the diameter ranging from 10 to 100 mm is discussed. The modified method of interference patterns reconstruction based on reference lines is described. Two algorithms — the algorithm of 4th order polynomial smoothing and the algorithm of fast Fourier transform are implemented. The modified method allowed to increase the reliability of interferometric pattern reconstruction and suppress the influence of incoming noise. Accuracy of the measurements is about &lambda;/10 (&lambda;=0.63 um).
Perceptually motivated deep neural network for video compression artifact removal

Recent advances have shown that latent representations of pre-trained Deep Convolutional Neural Networks (DCNNs) for classification can be modelled to generate scores that are well correlated with human perceptual judgement. In this paper we seek to extend the use of perceptually relevant losses in training a DCNN for video compression artefact removal. We will use internal representations of a pre-trained classification network as the basis of the loss functions. Specifically, the LPIPS metric and a perceptual discriminator will be responsible for low-level and high-level features respectively. The perceptual discriminator uses differing internal feature representations of the VGG network as its first stage of feature extraction. Initial results shows an increase in performance in perceptually based metrics such VMAF, LPIPS and BRISQUE, while showing a decrease in performance in PSNR.
Membership Functions for Fuzzy Support Vector Machine in a Noisy Environment

This chapter introduces the compounding of General Purpose Membership Functions (GPMFs) for overcoming class noise in a Fuzzy Support Vector Machine (FSVM). Traditional membership functions (MFs) characterize all samples of the class with a single MF.
Data Transmission Efficiency Optimization Algorithm of Laboratory Equipment Based on Frequency Control

In order to improve the efficiency and throughput of data transmission of laboratory instruments and equipment and reduce resource consumption, an optimization algorithm of data transmission efficiency of laboratory instruments and equipment based on frequency control is proposed. Firstly, the frequency control method is used to construct the real-time data transmission model of laboratory instruments and equipment, and the optimal equivalent load of instruments and equipment is obtained. Secondly, the filtering time constant is selected through the first-order low-pass filtering control model to obtain the value range of the filtering time constant. Finally, the filter time constant is optimized in real time according to the stabilized power prediction value sequence, the optimization objective function of data transmission efficiency is obtained, and the optimization of data transmission efficiency is realized by solving the objective function. The experimental results show that compared with the traditional algorithm, this algorithm can improve the data transmission efficiency of laboratory instruments and equipment and improve the data throughput and reduce the resource consumption. The data transmission efficiency of this algorithm is always maintained at more than 85%.
Research on the Enhancement of Teacher Training Students’ Professional Competence in the Context of Professional Accreditation

At present, China's teacher training colleges and universities are promoting the structural reform of teacher training in the context of professional certification of teacher training, striving to promote construction, reform and strengthening by means of evaluation, enhance the social responsibility, innovation and practical ability of teacher training students, and comprehensively guarantee and improve the quality of teacher training professionals. The purpose of this paper is to study the cultivation and enhancement of teacher training students' vocational competence in the context of teacher training professional accreditation, to put forward the weaknesses and blind spots in the cultivation of teacher training students' vocational competence in the context of professional accreditation, and then to give a series of improvement and enhancement strategies for the cultivation of teacher training students' vocational competence under professional accreditation, so as to provide some reference for the cultivation of teacher training students' vocational competence.
The Cross-Zone Navigation and Signage Systems for Combatting Cybersickness and Disorientation in Middle-Aged and Older People within a 3D Virtual Store

With the maturation and popularization of 3D virtual reality (3D VR) technology, various corporations have employed 3D VR animations to enrich the experience of visiting a store and change how products are presented. Because middle-aged and older adults have weaker mobility and perception abilities, their behaviors in 3D virtual stores may differ entirely from those of younger age groups. This study aimed to develop a cross-zone navigation system and a signage system for 3D virtual retail stores to provide middle-aged and older consumers with high-efficiency navigation for finding products quickly. Additionally, the effect of the systems on combating perceptual conflict was assessed to confirm the practicability of 3D virtual retail shopping. The results revealed that the cross-zone navigation system effectively assisted participants in searching for their desired products. Additionally, the cybersickness score (SSQ) of the cross-zone navigation system group was significantly lower than that of the map-based navigation system group. The participants who used both the cross-zone navigation system and the signage system exhibited the lowest perceptual conflict scores. Therefore, this study provides references for developing a novel navigation system for 3D virtual retail stores (i.e., a cross-zone navigation system with signage).
Threshold multiparty <scp>multi‐randomness</scp> secure partially homomorphic encryption for data security in cloud

Abstract Threshold cryptography is used to secure a cryptographic secret by dividing it into N shares and storing each claim on a diverse server. Any subgroup of t servers can use the secret without re‐making it. However, an adversary cannot recover the secret by compromising t − 1 servers. Homomorphic encryption operates directly on the ciphertext. In this study, a Threshold multiparty multi‐randomness secure partially homomorphic encryption ( TM‐MR‐SPHE ) algorithm is proposed to secure the outsourced data and perform multiplication and division operations on the ciphertext. The model is implemented in Eclipse IDE and AWS Toolkit for Eclipse and deployed in Amazon Elastic Beanstalk ( EB ) environment. This model is mainly used to secure the patient e‐health details and perform computation on outsourced data. The patient details are encrypted by the algorithm MR‐SPHE and uploaded in AWS (Amazon Web Service) S3 bucket. AWS identity and access management ( IAM ) creates the users' service and secret shares among multiple users in the EB environment. The proposed model performance is studied by comparing with other partially homomorphic Elgamal, Pailler, and Benaloh. This model achieves data integrity and confidentiality using the threshold multiparty with multi‐randomness secure partially homomorphic encryption.
Stacked Denoising Sparse Autoencoder-Based Fuzzy Rule Classifiers

With time, machine learning experts are unanimously agreeing that finding good features is one of the most important problems in pattern classification [1]. In fact, if given features are good, even a linear classifier would suffice to give excellent classification results.
Broken Heart: Privacy Leakage Analysis on ECG-Based Authentication Schemes

Authentications using biometrics, such as fingerprint recognition and electrocardiogram (ECG), have been actively used in various applications. Unlike traditional authentication methods, such as passwords or PINs, biometric-based authentication has an advantage in terms of security owing to its capability of liveness detection. Among the various types of biometrics, ECG-based authentication is widely utilized in many fields. Because of the inherent characteristics of ECG, however, the incautious design of ECG-based authentication may result in serious leakage of personal private information. In this paper, we extensively investigate ECG-based authentication schemes previously proposed in the literature and analyze possible privacy leakages by employing machine learning and deep learning techniques. We found that most schemes suffer from vulnerabilities that lead to the leakage of personal information, such as gender, age, and even diseases. We also identified some privacy-insensitive ECG fiducial points by utilizing feature selection algorithms. Based on these features, we present a privacy-preserving ECG-based authentication scheme.
Towards Neural Hardware Search: Power Estimation of CNNs for GPGPUs with Dynamic Frequency Scaling

Machine Learning (ML) algorithms are essential for emerging technologies such as autonomous driving and application-specific Internet of Things (IoT) devices. Convolutional Neural Network (CNN) is one of the major techniques used in such systems. This leads to leveraging ML accelerators like GPGPUs to meet the design constraints. However, GPGPUs have high power consumption, and selecting the most appropriate accelerator requires Design Space Exploration (DSE), which is usually time-consuming and needs high manual effort. Neural Hardware Search (NHS) is an upcoming approach to automate the DSE for Neural Networks. Therefore, automatic approaches for power, performance, and memory estimations are needed.In this paper, we present a novel approach, enabling designers to fast and accurately estimate the power consumption of CNNs inferencing on GPGPUs with Dynamic Frequency Scaling (DFS) in the early stages of the design process. The proposed approach uses static analysis for feature extraction and Random Forest Tree regression analysis for predictive model generation. Experimental results demonstrate that our approach can predict the CNNs power consumption with a Mean Absolute Percentage Error (MAPE) of 5.03% compared to the actual hardware.
Markov Model Based Traffic Classification with Multiple Features

Traffic prioritization has recently become more critical and crucial for home Wi-Fi networks due to the increased number of connected devices and applications. While some of these applications are delay sensitive, some have high throughput requirements. Quality of Service (QoS) in Wi-Fi is achieved via differentiation and prioritization of traffic streams, which can be performed successfully as long as the packets can be classified with high precision. As a solution for this problem, this paper presents a new Discrete Time Markov Chain-based traffic classification algorithm, which exploits a multidimensional feature set, named as k-Nearest Markov Component with 3 Dimensions (kNMC-3D). Considering results obtained on two different datasets with current, most popular multimedia applications from different categories, we present the performance of the proposed algorithm, kNMC-3D in comparison to kNMC, two feature extraction based machine learning techniques, Support Vector Machines (SVM) and Random Forest (RF) and a deep learning approach, Auto Encoder with RF (AE+RF). It is shown that kNMC-3D achieves 84.93% and 90.73% accuracy at the application level, 91.13% and 99.17% accuracy at category level on our dataset and a benchmark dataset, respectively. Outperforming the existing methods that focus mainly on feature extraction, kNMC-3D prevents information loss by making use of sequentiality in the traffic, while it improves kNMC by considering multiple features, number of bits, inter-arrival times and number of packets.
Trust Assessment Under the Integrated Air-Space-Ground Network Environment

While playing an increasing role in the field of air-space integrated networks, terminal entities are exposed to more serious security risks than ordinary terminal entities on the ground, including but not limited to astronomical risks (e.g., solar activity), link disruptions, and malicious attacks. In the integrated air-space-space network, after the terminal entity is loaded into the rocket for launch, real-time monitoring measures are implemented on the ground site for all links to the intended orbit. On top of the real-time monitoring measures, the need for a comprehensive evaluation of the air-space integration network is growing. In this paper, we further classify the security requirements and evaluation benchmarks of the terminal entities of the air-space integration network around the security evaluation requirements of the air-space integration network. On this basis, the relevant concept of trust value is introduced to assess the security status of terminal entities of air-space-sky integrated networks, and on this basis, a security assessment system of air-space-sky integrated network terminals is proposed. The exploration and development of field applications are initially realized.