Reviewerly/exHarmony huggingface dataset:
Denoising Seismic Data via a Threshold Shrink Method in the Non-Subsampled Contourlet Transform Domain

In seismic exploration, effective seismic signals can be seriously distorted by and interfered with noise, and the performance of traditional seismic denoising approaches can hardly meet the requirements of high-precision seismic exploration. To remarkably enhance signal-to-noise ratios (SNR) and adapt to high-precision seismic exploration, this work exploits the non-subsampled contourlet transform (NSCT) and threshold shrink method to design a new approach for suppressing seismic random noise. NSCT is an excellent multiscale, multidirectional, and shift-invariant image decomposition scheme, which can not only calculate exact contourlet transform coefficients through multiresolution analysis but also give an almost optimized approximation. It has better high-frequency response and stronger ability to describe curves and surfaces. Specifically, we propose to utilize the superior performance NSCT to decomposing the noisy seismic data into various frequency sub-bands and orientation response sub-bands, obtaining fine enough transform high frequencies to effectively achieve the separation of signals and noises. Besides, we use the adaptive Bayesian threshold shrink method instead of traditional handcraft threshold scheme for denoising the high-frequency sub-bands of NSCT coefficients, which pays more attention to the internal characteristics of the signals/data itself and improve the robustness of method, which can work better for preserving richer structure details of effective signals. The proposed method can achieve seismic random noise attenuation while retaining effective signals to the maximum degree. Experimental results reveal that the proposed method is superior to wavelet-based and curvelet-based threshold denoising methods, which increases synthetic seismic data with lower SNR from −8.2293 dB to 8.6838 dB, and 11.8084 dB and 9.1072 dB higher than two classic sparse transform based methods, respectively. Furthermore, we also apply the proposed method to process field data, which achieves satisfactory results.
A Study on the Design of English Speaking Examination System Based on SSM Framework

At present, the teaching, training, and assessment programs of English speaking courses in schools are basically carried out by the teaching method of face-to-face teaching by teachers, especially in the English speaking assessment; students and teachers communicate with each other to answer questions, while the students’ performance is subjectively judged by the school English teachers. This model not only consumes the teaching resources of school English teachers but also cannot ensure the uniformity of grading standards. To this end, this paper designs and proposes an English speaking test system with the aim of building an intelligent speaking test system by designing a speech recognition deepening frequency processing function through SSM framework combined with JSP technology and deep learning theory. The English speaking test system designed in this paper integrates several open source frameworks, adopts modular design, and divides it into two subsystems, student-side module system and teacher-side module system for system framework design, and finally, this system is tested for functionality. The system was designed and implemented through the framework description, functional description, and functional testing of this system, and finally, the English speaking test system was designed and implemented. The system is designed and implemented to change the traditional one-to-one mode of school teaching, save teachers’ manpower, strengthen the content of intensive training for students’ English proficiency and improve the frequency of exams, facilitate school administrators and English teachers to evaluate the effectiveness of their teaching implementation, and develop corresponding effective individualized teaching and guidance strategies for schools to provide convenience, which can greatly reduce the recognized labor intensity of English teachers and provide a correct evaluation of students, a correct evaluation of the level, and other roles.
BeDCV: Blockchain-Enabled Decentralized Consistency Verification for Cross-Chain Calculation

With the increase of data stored on the blockchain, the efficiency of storage and calculation of blockchain has gradually become a bottleneck restricting the development of blockchain. By storing data on multiple chains, blockchains can request data from other chains for calculation and the storage pressure can be alleviated. But the transfer of a large amount of data between chains suffers from low transfer efficiency and poor security. A reasonable design is to perform the calculation on the data storage chain and only transfer the results across chains. However, since the calculation process is invisible, blockchains cannot judge the consistency of calculation results from other chains. In this paper, we provide a blockchain-enabled decentralized consistency verification scheme for cross-chain calculation (BeDCV). Considering the decentralized characteristic of blockchain, we adopt the blockchain called <i>supervision chain</i> for decentralized auditing. We modify paillier homomorphic encryption to encrypt data involved in the calculation for correctness verification. Then, we aggregate the ciphertexts of data to generate the audit proof for integrity verification. Besides, we verify whether the data involved in the calculation are real-time by leveraging a counting bloom filter. The supervision chain can check the correctness, integrity, and real-time performance of cross-chain data calculation without revealing any original information about the data. The theoretical and experimental analysis demonstrates that BeDCV can verify the consistency of cross-chain data calculation result effectively, realizing secure and reliable expansion of blockchain.
Deep-Learning-Driven Secure Offline Blockchain in a Cyber-Physical Social System

A cyber-physical social system (CPSS), which integrates the social world with the cyber world and physical world, plays a crucial role in a more intelligent and efficient network. Recent advances in blockchain have significant impacts on securing the transactions in CPSS. Nevertheless, the deployment of blockchain faces fundamental engineering challenges. Specifically, in remote or disaster areas, users of CPSS typically cannot effectively connect to the blockchain system in real time due to the deficiency of Internet connection. In busy urban areas, however, users are reluctant to frequently connect to the blockchain system due to the costly communication connection. Thus, the offline blockchain system, which enables direct secured offline transaction channels among users in CPSS and efficiently synchronizes batch transactions to the global blockchain system, is a promising solution to address these problems. This article presents a novel CPSS architecture that can realize secure transactions for users participating in offline blockchain transactions by making use of an AI-based algorithm. We first introduce three typical offline blockchain transaction environments in CPSS, followed by analysis of the security issues of the proposed architecture. In addition, we present the hashed time locked contract (HTLC) established for the CPSS users participating in an offline blockchain transaction. To detect potential malicious attackers in offline transactions, we introduce an LSTM-based recurrent neural network (RNN) model to detect the malicious transaction witness. Numerical results are presented to validate the effectiveness of this model.
A Deep Learning-Based Hyperspectral Keypoint Representation Method and Its Application for 3D Reconstruction

In previous studies, the keypoints and their feature descriptors of a hyperspectral image have not received enough attention.Compared with RGB images, the rich feature information of a hyperspectral image can not only constrain the spatial positions of the keypoints, but also provide rich spectral features for their descriptors.In this paper, we propose a deep neural network method called Hyperspectral SuperPoint Network (HSSPN) to detect the keypoints from a hyperspectral image and generate the corresponding feature descriptors.The HSSPN method contains a Shared Encoder (which is responsible for fully extracting the features of the spatial and spectral dimensions) and two decoders: the Keypoint Decoder accurately detects the keypoints according to the feature map outputted by the Shared Encoder, while the Descriptor Decoder is responsible for generating stable feature descriptors.We collected some hyperspectral images with an electronically controlled turntable, and then annotated them with a Hyperspectral Homographic Adaptation (HHA) network.The trained HSSPN gave an excellent keypoint matching performance on the test set.Hyperspectral 3D reconstruction can obtain information in a 3D space and a spectral space at the same time.Most of the previous reconstruction methods are separated for the treatments of 3D reconstruction and hyperspectral imaging, which require complicated registrations.In this study, we propose a SfM-based hyperspectral 3D reconstruction method, in which we first collect hyperspectral images from multiple viewing angles, and then apply the HSSPN method to detect the keypoints and calculate their feature descriptors.These keypoints and feature descriptors are then used to reconstruct each spectral image of the target with the SfM method.The 3D reconstruction models at these wavelengths are simply superimposed to obtain a hyperspectral 3D reconstruction model.Compared with conventional SIFT-based SfM methods, our method not only contains richer spectral information, but also achieves better results in the reconstruction accuracy.
Coherent optical neural network with injection-locked VCSELs

We demonstrate analog coherent homodyne photoelectric multiplication with phase-modulated VCSEL arrays (operating at V π = 2 mV). Precise phase control over individual laser emitters enables MNIST image classification with accuracy near the ground truth.
Efficient and Trusted Data Sharing in a Sharding-Enabled Vehicular Blockchain

Data sharing is essential for future autonomous vehicles in vehicular networks. Building trust in the shared data can be challenging among mutually unknown vehicles in an open and distributed environment. Blockchain is the key to establish trusted data sharing in a decentralized vehicular environment. However, the low efficiency (e.g., transaction per second, TPS) of blockchain is the bottleneck for the efficient data sharing. Moreover, the highly dynamic topology, the imperfect wireless links, and the limited radio coverage may further slow down the blockchain consensus process. To improve the efficiency and trustworthiness of data sharing in a vehicular network, we propose a sharding-enabled vehicular blockchain system. Specifically, the blockchain-enabled vehicles are dynamically allocated to different shards based on their geographical locations so that transactions are processed in parallel. We design an efficient and trusted sharding-enabled blockchain consensus mechanism by integrating a reputation-assisted intra-shard consensus and a vehicle-assisted inter-shard consensus. The reputation assistance in intra-shard consensus is designed to defend against the collusive false-block attack that may happen in a small-scale shard environment. The vehicle-assisted inter-shard consensus is designed to tackle the issues caused by defective roadside units (RSUs). Simulation results demonstrate the improved performance on efficiency and trustwor-thiness of the proposed system. In addition, trusted data sharing can be established even under the conditions of malicious vehicles or attacked RSUs.
Neural Modeling of Portrait Bas-Relief From a Single Photograph

In this paper, we present an end-to-end neural solution to model portrait bas-relief from a single photograph, which is cast as a problem of image-to-depth translation. The main challenge is the lack of bas-relief data for network training. To solve this problem, we propose a semi-automatic pipeline to synthesize bas-relief samples. The main idea is to first construct normal maps from photos, and then generate bas-relief samples by reconstructing pixel-wise depths. In total, our synthetic dataset contains 23 k pixel-wise photo/bas-relief pairs. Since the process of bas-relief synthesis requires a certain amount of user interactions, we propose end-to-end solutions with various network architectures, and train them on the synthetic data. We select the one that gave the best results through qualitative and quantitative comparisons. Experiments on numerous portrait photos, comparisons with state-of-the-art methods and evaluations by artists have proven the effectiveness and efficiency of the selected network.
RUPA: A High Performance, Energy Efficient Accelerator for Rule-Based Password Generation in Heterogenous Password Recovery System

There has been a growing demand for energy efficient and high performance password recovery systems. As password generation and password validation are two integral components of any password recovery system, the former yet lags behind the latter in performance particularly for the case when the popular rule-based password generation method is applied to the heterogeneous CPU-FPGA system. In this paper, we thus present a high performance, energy efficient accelerator to speed up the rule functions in rule-based password generation. Dubbed RUPA, this proposed accelerator explores previously undiscovered computational features and memory access patterns for processing the rule functions. Specially, we show that the rule functions can be mapped to three distinct groups according to their character dependency graphs. Correspondingly, three kinds of datapath units, referred to as rule logic units, are created, and the rule functions from the same group will be processed in their shared rule logic unit. Compared with the state-of-the-art password recovery system built upon a CPU-GPU platform, the FPGA-based RUPA system achieves 5.3x speed improvement and is 33.1x more energy efficient. If RUPA is integrated into the popular password recovery tool John the Ripper (JtR), JtR's rule-based attack performance can soar by more than 48.7x.
Just Noticeable Difference Prediction and Image Quality Assessment

The paper shows that the information of the first just noticeable difference (JND) point position can significantly improve the performance of the objective peak signal-to-noise ratio (PSNR) measure in assessing the quality of JPEG compressed images. The degree of improvement depends on the choice of the first JND point position prediction model. Also, the paper shows that simple features derived from the gradient magnitude (spatial information and spatial frequency) of the original uncompressed image can be used for reliable position prediction. The analysis was conducted on two publicly available JND subject-rated image datasets MCL-JCI and JND-Pano. Among others, the linear correlation coefficient is used as an objective measurement parameter in prediction and in image quality assessment analysis. The prediction based on spatial frequency provided the best results, with over 95% of agreement with ground truth JND points position. This simple picture-wise prediction model has significantly improved the performance of conventional PSNR measure, with over 90% of agreement with subjective scores in image quality assessment. The PSNR performance is most enhanced by using a deep learning approach, where the correlation with subjective test results is close to 92%.
On the Implementation of Contact Tracing via GPS

The covid-19 pandemic has impacted the world. One of the mitigation technique to limit the spread of the virus is contact tracing. Contact tracing techniques applies to any infectious disease. Digital contact tracing via mobile phone using GPS coordinates was investigated. Implementation decision such as the location service's configuration mode has an impact on the accuracy of location data captured as well as the battery usage. The limitations and issues associated to the implementation of mobile contact tracing applications are identified.
Network security education: SQL injection attacks

This paper presents an application that is used in network security courses at the Department of Computing and Control, Faculty of Technical Sciences in Novi Sad. The application is used to demonstrate various types of SQL injection attacks to the students. An overview of this class of attacks is given, as well as a description of the application. The application has been written in PHP language, and relies on MariaDB database.
Development of domain specific language for modeling software components following AUTOSAR standard

Developing domain specific language for supporting of modeling software components that will comply to AUTOSAR standard. Application for supporting of modeling software components is based on TextX python module and internally developed modeling framework. Modeling via application brings safer and much faster way of applying changes to various models. In this paperwork it is shown the basic algorithm which application is working, application parts whit their functionalities and example of using domain specific language with results showing some changes in model.
Efficient Reversible Data Hiding Based on Connected Component Construction and Prediction Error Adjustment

To achieve a good trade-off between the data-embedding payload and the data-embedding distortion, mainstream reversible data hiding (RDH) algorithms perform data embedding on a well-built prediction error histogram. This requires us to design a good predictor to determine the prediction errors of cover elements and find a good strategy to construct an ordered prediction error sequence to be embedded. However, many existing RDH algorithms use a fixed predictor throughout the prediction process, which does not take into account the statistical characteristics of local context. Moreover, during the construction of the prediction error sequence, these algorithms ignore the fact that adjacent cover elements may have the identical priority of data embedding. As a result, there is still room for improving the payload-distortion performance. Motivated by this insight, in this article, we propose a new content prediction and selection strategy for efficient RDH in digital images to provide better payload-distortion performance. The core idea is to construct multiple connected components for a given cover image so that the prediction errors of the cover pixels within a connected component are close to each other. Accordingly, the most suitable connected components can be preferentially used for data embedding. Moreover, the prediction errors of the cover pixels are adaptively adjusted according to their local context, allowing a relatively sharp prediction error histogram to be constructed. Experimental results validate that the proposed method is significantly superior to some advanced works regarding payload-distortion performance, demonstrating the practicality of our method.
Construction of Higher Education Management and Student Achievement Evaluation Mechanism Based on Apriori Algorithm

The Apriori algorithm and DM technology are introduced, examined, and a relationship between bettering teachers’ classroom teaching quality, methods, and means is discussed. An evaluation system for student achievement in higher education is proposed and built on the basis of the Apriori algorithm. The Apriori algorithm is also improved in order to further avoid blind search during mining and increase the effectiveness of frequent itemsets. This paper avoids repeatedly scanning the database and instead reads the entire database into a two-dimensional array, which increases program performance. The algorithm is used to extract useful data from the database using data association mining in the higher education management system. According to the experimental findings, the improved Apriori algorithm’s accuracy can reach 94.81%, which is 10.31% higher than the accuracy of the original Apriori algorithm. The outcomes demonstrate the applicability and reliability of the algorithm model developed in this paper. For managers in teaching and management, it can be a useful reference.
Construction of German Machine Intelligent Translation System Based on Data Mining Algorithm

The continuous expansion of the international market makes more people pay attention to learning different languages. As one of the most popular languages, German is favored by scholars. The use of intelligent translation systems to learn German has become the main way of learning. The increasing development of science and technology is accompanied by the emergence of a large number of new German words. The traditional German intelligent translation system has a slow update speed of words and cannot recognize new words, which seriously affects the accuracy of the translation. According to these questions, in this paper, the data mining algorithm is introduced into the German machine intelligent translation system. By evaluating the German machine intelligent translation system using a data mining algorithm in four aspects: translation speed, accuracy rate, recall rate, and user satisfaction, the evaluation results found that the translation speed of the system increased by 4.68%, the accuracy rate increased by 4.2, the recall rate has increased by 3.8%, and the user satisfaction score has increased by 0.825 points. The data mining algorithm can improve the German machine intelligent translation system and users’ interest in learning and promote German education.
Construct maximally five- and seven-qubit entangled states via three- qubit GHZ state

Different mathematics forms of entangled state can judge prepared many particles is if entangled. Entangled state is a key role of quantum information technology. The more entangled states are generated, the better performances are presented. In order to make it more convenient to generate entangled state or judge the state prepared is entangled, we investigate three-qubit GHZ state and expand recurrence relation based on an expand recurrence relation of multi-qubit state. Furthermore, new forms of maximally five and seven-qubit entangled states are reconstructed via three-qubit GHZ state. These new forms stand for universal law of n-qubit and [Formula: see text]-qubit can have direct application in quantum entangle state generation and quantum communications.