Reviewerly/exHarmony huggingface dataset:
The Single-Photon Scattering Properties of Three-Level Giant Atoms under the Interaction of Dissipation and Local Coupling

Authors:
raw_author_name: Liangwei Lin
raw_affiliation_string: School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China
raw_author_name: Weiwei Zhang
raw_affiliation_string: School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China
raw_author_name: Qipeng Cai
raw_affiliation_string: School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China
raw_author_name: Yiguang Xu
raw_affiliation_string: School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China
raw_author_name: Xiaosheng Wang
raw_affiliation_string: School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China
raw_author_name: Xiaohong Fang
raw_affiliation_string: School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China
raw_author_name: Zixuan Chen
raw_affiliation_string: School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China

Abstract: The coupling of three-level giant atoms with one-dimensional waveguides can show interesting phenomena of transmission and reflection. Since the non-waveguide mode can cause the dissipation of external atoms, we consider the effect of the dissipation rate on the scattering of single photons in the system with giant atom–waveguide coupling. We find that as the dissipation rate of giant atoms increases, the transmission rate of a single photon increases and the reflection rate decreases. In addition, by varying the phase difference and decay rate, the giant atoms are able to achieve perfect transmission and total reflection over the entire frequency range. We also find and show the conditions for the conversion of the optimal frequency. When the cumulative phase of photons reaches a certain value, the system can achieve perfect transmission, which is independent of frequency. This model of coupling giant atoms with waveguides has a promising application in quantum communication and quantum information processing.
Prediction Model of Water Abundance of Weakly Cemented Sandstone Aquifer Based on Principal Component Analysis–Back Propagation Neural Network of Grey Correlation Analysis Decision Making

Authors:
raw_author_name: Yuguang Zhang
raw_affiliation_string: National Investment Hami Energy Development Co., Ltd., Hami 839000, China
raw_author_name: Wenping Li
raw_affiliation_string: School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China
raw_author_name: Qiqing Wang
raw_affiliation_string: School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China

Abstract: At present, in the vast majority of coal mine production processes in China, the degree of hydrogeological exploration often lags behind geological exploration. The main difficulty in evaluating the water richness of coal seam top and bottom water-bearing beds is that the existing evaluation methods often rely on less hydrogeological investigation data. How to utilize the abundant geological exploration data in the mining area to appraise the water-rich distribution of sandstone aquifers is a feasible and challenging methodology. At present, some experts and scholars have tried to use multivariate factor analysis to solve the problem of water-richness evaluation, and they have achieved certain results, but there are some shortcomings: (1) The prediction results are mostly qualitative estimations of the water-richness grade, and there is a lack of quantitative analysis of the units-inflow; and (2) at present, the more advanced prediction methods, such as the back propagation (BP) neural network model, have the disadvantages of low accuracy, requiring many iterations, and slow convergence speed. Therefore, with geological exploration data of the 1503E working face of the Yili No.1 coal mine as the basis., this paper uses grey correlation analysis to screen out the factor indexes suitable for the evaluation of the water richness of a weakly cemented sandstone aquifer, and it combines principal component analysis (PCA) with a BP neural network. Based on the selected factor indexes, a prediction model of the water richness of a weakly cemented sandstone aquifer is established. The results show that compared with the existing methods, the prediction accuracy is higher and has a certain application value.
Pooled Steganalysis via Model Discrepancy

Authors:
raw_author_name: Jiang Yu
raw_affiliation_string: Faculty of Business Information, Shanghai Business School, Shanghai 200235, China
raw_author_name: Jing Zhang
raw_affiliation_string: Faculty of Business Information, Shanghai Business School, Shanghai 200235, China
raw_author_name: Fengyong Li
raw_affiliation_string: College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 201306, China

Abstract: Pooled steganalysis aims to discover the guilty actor(s) among multiple normal actor(s). Existing techniques mainly rely on the high-dimension and time-consuming features. Moreover, the minor feature distance between cover and stego is detrimental to pooled steganalysis. To overcome these issues, this paper focuses on the discrepancy of the statistical characteristics of transmitted multiple images and designs a model-based effective pooled steganalysis strategy. Facing the public and monitored channel, without using the feature extractions, pooled steganalysis collects a set of images transmitted by a suspicious actor and use the corresponding distortion values as the statistic representation of the selected image set. Specifically, the normalized distortion of the suspicious image set generated via normal/guilty actor(s) is modelled as a normal distribution, and we apply maximum likelihood estimation (MLE) to estimate the parameter (cluster center) of the distribution by which we can represent the defined model. Considering the tremendous distortion difference between normal and stego image sets, we can deduce that the constructed model can effectively discover and reveal the existence of abnormal behavior of guilty actors. To show the discrepancy of different models, employing the logistic function and likelihood ratio test (LRT), we construct a new detector by which the ratio of cluster centers is turned into a probability. Depending on the generated probability and an optimal threshold, we make a judgment on whether the dubious actor is normal or guilty. Extensive experiments demonstrate that, compared to existing pooled steganalysis techniques, the proposed scheme exhibits great detection performance on the guilty actor(s) with lower complexity.
A Survey of Ethereum Smart Contract Security: Attacks and Detection

Authors:
raw_author_name: Tengyun Jiao
raw_affiliation_string: Swinburne University of Technology, Melbourne, Australia
raw_author_name: Zhiyu Xu
raw_affiliation_string: Swinburne University of Technology, Melbourne, Australia
raw_author_name: Minfeng Qi
raw_affiliation_string: Swinburne University of Technology, Melbourne, Australia
raw_author_name: Sheng Wen
raw_affiliation_string: Swinburne University of Technology, Melbourne, Australia
raw_author_name: Yang Xiang
raw_affiliation_string: Swinburne University of Technology, Melbourne, Australia

Abstract: A smart contract is a computerised transaction agreement that carries out predefined terms without human involvement or third-party intermediaries. It serves as a trust intermediary in several industries, including finance, insurance, and supply chain management, in the blockchain 2.0 era. With the increasing interest in smart contracts, security has become a serious problem. Examining typical vulnerability types and vulnerability detection methodologies is of special importance. In this research, a comprehensive evaluation of common smart contract security vulnerabilities is conducted, and a three-tier threat model is then provided to classify the vulnerabilities. In addition, we examine fourteen existing smart contract analysis tools for finding vulnerabilities and classify them according to the main technique they apply. This paper is designed to serve as a reference for people who wish to analyse deployed code and enhance existing detection techniques. At the conclusion, open issues and future research paths regarding smart contract vulnerability detection are presented.
Good models borrow, great models steal: intellectual property rights and generative AI

Authors:
raw_author_name: Simon Chesterman
raw_affiliation_string: AI Governance, AI Singapore; Faculty of Law, National University of Singapore

Abstract: Abstract Two critical policy questions will determine the impact of generative artificial intelligence (AI) on the knowledge economy and the creative sector. The first concerns how we think about the training of such models—in particular, whether the creators or owners of the data that are “scraped” (lawfully or unlawfully, with or without permission) should be compensated for that use. The second question revolves around the ownership of the output generated by AI, which is continually improving in quality and scale. These topics fall in the realm of intellectual property, a legal framework designed to incentivize and reward only human creativity and innovation. For some years, however, Britain has maintained a distinct category for “computer-generated” outputs; on the input issue, the EU and Singapore have recently introduced exceptions allowing for text and data mining or computational data analysis of existing works. This article explores the broader implications of these policy choices, weighing the advantages of reducing the cost of content creation and the value of expertise against the potential risk to various careers and sectors of the economy, which might be rendered unsustainable. Lessons may be found in the music industry, which also went through a period of unrestrained piracy in the early digital era, epitomized by the rise and fall of the file-sharing service Napster. Similar litigation and legislation may help navigate the present uncertainty, along with an emerging market for “legitimate” models that respect the copyright of humans and are clear about the provenance of their own creations.
Intelligent fault diagnosis of storage stacking machinery under variable working conditions using attention-based adaptive multimodal feature fusion networks

Authors:
raw_author_name: Xiangyin Meng
raw_affiliation_string: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China
raw_author_name: Yang Li
raw_affiliation_string: Institute of Smart City and Intelligent Transportation, Southwest Jiaotong University, Chengdu, China
raw_author_name: Xinxin Xie
raw_affiliation_string: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China
raw_author_name: Shichu Li
raw_affiliation_string: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China
raw_author_name: Lei Xie
raw_affiliation_string: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China
raw_author_name: Huiping Huang
raw_affiliation_string: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China
raw_author_name: Peng Guo
raw_affiliation_string: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China
raw_author_name: Min Zhang
raw_affiliation_string: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China
raw_author_name: Shide Xiao
raw_affiliation_string: School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China

Abstract: Due to the harsh working environment of storage stacking machinery, the fault information of important components is significantly complex, which leads to the problem of low classification accuracy and high computational complexity of existing deep learning-based fault diagnosis methods. To alleviate the problem, this paper presents a novel architecture named attention-based adaptive multimodal feature fusion networks for intelligent fault diagnosis of storage stacking machinery, which is aimed at improving the diagnostic precision and robustness of feature fusion network and learning the broader feature representation. Firstly, the long short-term memory layer is introduced to extract the feature information of multiple time steps to improve the self-extraction ability of multi-temporal features. Then, the maximum temporal feature fusion module is utilized to highlight the recognizability of deep fusion features. Finally, a residual layer with spanning connections is added to increase the utilization and characterization capability of deep fusion features. Experimental results demonstrate the effectiveness and superiority of the proposed method in intelligent fault diagnosis of storage stacking machinery under variable working conditions compared with some state-of-the-art deep learning-based methodologies.
Assessing primary school preservice teachers’ confidence to apply their TPACK in specific categories of technologies using a self-audit survey

Authors:
raw_author_name: David A. Martin
raw_affiliation_string: School of Education, Edith Cowan University, Perth, WA, Australia

Abstract: Abstract Internationally, university teacher educators have acted on the requirement that practising teachers need to be operational users of technologies. In response, coursework has been restructured to develop preservice teachers’ (PSTs) use and application of educational technologies for teaching and learning purposes. This paper presents the development and use of a self-audit survey designed to guide primary school PSTs’ self-directed learning and assessments in 10 specific categories of technologies, and to improve their confidence to apply their knowledge. The survey was administered to 296 PSTs in a pre- and post-course design and validated post hoc with Rasch analysis. Pre- and post-course comparison of responses showed significant increases in the PSTs’ self-assessed confidence in their abilities across all ability categories with medium to large effect sizes ( r = .42–.82). The survey had utility, providing each PST with a customised report with which to identify their technology learning needs; it also provided PSTs with the agency to be self-directed in their learning. The survey has the potential to provide teacher educators with fine-grained information regarding which technological pedagogical and content knowledge (TPACK) being currently taught can be adopted, adapted, or rejected to maximise PSTs’ TPACK development.
Measuring the motion of the eardrum using Digital Image Correlation

Authors:
raw_author_name: Enis Ukshini
raw_affiliation_string: 2020 Antwerp, Belgium; Laboratory of Biomedical Physics (BIMEF), University of Antwerp, Groenenborgerlaan 171
raw_author_name: Joris J.J. Dirckx
raw_affiliation_string: 2020 Antwerp, Belgium; Laboratory of Biomedical Physics (BIMEF), University of Antwerp, Groenenborgerlaan 171

Abstract: Abstract The human eardrum is a translucent curved membrane and is in direct contact with the middle ear bones. Studying this complicated biomechanical system remains challenging. Digital image correlation (DIC) is an optical technique that allows measuring the shape and displacement of curved surfaces in 3D. In this work, we discuss the advantages and disadvantages of using DIC to study the eardrum at frequencies around 1 kHz. Special attention is given to the application of speckle patterns required to perform DIC on the translucent eardrum. Additionally, we detail an alternative approach to high-speed cameras: phase-locked light pulses. Using light pulses synced to the phase of the sound, regular DIC cameras can be used at their full resolution. This enables a high spatial resolution to thoroughly investigate the motion of the eardrum at the relevant frequencies.
An efficient image encryption algorithm using a discrete memory-based logistic map with deep neural network

Authors:
raw_author_name: B. Sakthi Kumar
raw_affiliation_string: Department of ECE, Sri Eshwar College of Engineering, Coimbatore, India
raw_author_name: R. Revathi
raw_affiliation_string: Department of ECE, KLEF, Guntur, India

Abstract: Abstract In the last few years, multimedia technology has made tremendous strides. These days, the Web is frequently used to transfer multimedia content, including audio, video, and photos. However, the Internet is a very vulnerable medium with many security holes. To ensure that multimedia content carried across unprotected channels, like the Internet, is secure and private, several encryption techniques have been proposed. New encryption strategies must be developed because multimedia data streams cannot be encrypted using traditional methods. Therefore, the main goal of the recommended system is to present an analytical research approach for introducing a sophisticated framework wherein the suggested encryption technologies' efficacy is increased through the use of deep neural networks (DNNs). The robustness of the DNN principle is coupled with a discrete memristor-based logistic chaotic map notion for enhanced security performance. In this paper, three distinct encryption algorithms—Arnie cat with an artificial neural network (ANN), Henon map with an ANN, and logistic map with a DNN—are compared for security and performance with the suggested algorithm. Correlation coefficients, information entropy, number of pixels changing rate (NPCR), encryption quality, and encryption duration are the cryptographic analysis parameters examined here. The results show that the recommended implementation enhances security performance without degrading image quality. The proposed algorithm achieves 35.9% of UACI, 99.95% of NPCR, and 7.997231 of entropy.
Traffic sign recognition using CNN and Res-Net

Authors:
raw_author_name: J Cruz Antony
raw_affiliation_string: Sathyabama Institute of Science and Technology
raw_author_name: G M Karpura Dheepan
raw_affiliation_string: Sathyabama Institute of Science and Technology
raw_author_name: Veena K
raw_affiliation_string: Sathyabama Institute of Science and Technology
raw_author_name: Vellanki Vikas
raw_affiliation_string: Sathyabama Institute of Science and Technology

Abstract: &#x0D; In the realm of contemporary applications and everyday life, the significance of object recognition and classification cannot be overstated. A multitude of valuable domains, including G-lens technology, cancer prediction, Optical Character Recognition (OCR), Face Recognition, and more, heavily rely on the efficacy of image identification algorithms. Among these, Convolutional Neural Networks (CNN) have emerged as a cutting-edge technique that excels in its aptitude for feature extraction, offering pragmatic solutions to a diverse array of object recognition challenges. CNN's notable strength is underscored by its swifter execution, rendering it particularly advantageous for real-time processing. The domain of traffic sign recognition holds profound importance, especially in the development of practical applications like autonomous driving for vehicles such as Tesla, as well as in the realm of traffic surveillance. In this research endeavour, the focus was directed towards the Belgium Traffic Signs Dataset (BTS), an encompassing repository comprising a total of 62 distinct traffic signs. By employing a CNN model, a meticulously methodical approach was obtained commencing with a rigorous phase of data pre-processing. This preparatory stage was complemented by the strategic incorporation of residual blocks during model training, thereby enhancing the network's ability to glean intricate features from traffic sign images. Notably, our proposed methodology yielded a commendable accuracy rate of 94.25%, demonstrating the system's robust and proficient recognition capabilities. The distinctive prowess of our methodology shines through its substantial improvements in specific parameters compared to pre-existing techniques. Our approach thrives in terms of accuracy, capitalizing on CNN's rapid execution speed, and offering an efficient means of feature extraction. By effectively training on a diverse dataset encompassing 62 varied traffic signs, our model showcases a promising potential for real-world applications. The overarching analysis highlights the efficacy of our proposed technique, reaffirming its potency in achieving precise traffic sign recognition and positioning it as a viable solution for real-time scenarios and autonomous systems.
Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

Authors:
raw_author_name: Yixiao Zhang
raw_affiliation_string: School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
raw_author_name: Yuanming Gong
raw_affiliation_string: School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
raw_author_name: Xiaolong Chen
raw_affiliation_string: School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Abstract: Vehicle detection and location is one of the key sensing tasks of automatic driving systems. Traditional detection methods are easily affected by illumination, occlusion and scale changes in complex scenes, which limits the accuracy and robustness of detection. In order to solve these problems, this paper proposes a vehicle detection and location method for YOLOv5(You Only Look Once version 5) based on binocular vision. Binocular vision uses two cameras to obtain images from different angles at the same time. By calculating the difference between the two images, more accurate depth information can be obtained. The YOLOv5 algorithm is improved by adding the CBAM attention mechanism and replacing the loss function to improve target detection. Combining these two techniques can achieve accurate detection and localization of vehicles in 3D space. The method utilizes the depth information of binocular images and the improved YOLOv5 target detection algorithm to achieve accurate detection and localization of vehicles in front. Experimental results show that the method has high accuracy and robustness for vehicle detection and localization tasks.