Characteristics Of Concealment Research Articles

Research on Legal Issues of Tax Collection and Administration for Online Live Streaming Rewards

With the tide of the digital economy, an increasing number of individuals are showcasing their talents through online live streaming and earning reward income. However, compared to other income sources of the streamers, the nature of live streaming rewards presents a significant challenge to tax authorities due to its concealed characteristics. Based on the current legislative status in our country, this paper deeply analyzes the connotation and classification of live streaming reward income, revealing numerous issues faced in the field of tax collection and administration. These issues include but are not limited to the ambiguity of the taxpayer, unclear tax categories and rates, imperfect tax reporting systems, underdeveloped tax information sharing mechanisms, and an incomplete tax credit system. To effectively address these challenges, this paper constructs an overall framework around the elements of taxation and, in conjunction with existing tax laws and regulations, proposes practical and feasible suggestions for improvement.

Research on chaotic secure optical communication system based on dispersion keying with time delay signatures concealment

In response to the possibility of time delay (TD) signatures (TDSs) leakage becoming a serious problem in chaotic optical communication, from the perspective of improving the security of chaotic optical communication, this paper proposes a chaotic secure optical communication system based on dispersion keying, which generates signals with TDSs concealment characteristics. In this scheme, a common laser is used to drive chaotic synchronization between the transmitter and the receiver. The complex chaotic signal is generated by the combination of all-optical feedback and optical injection, and the TDSs are hidden by using the structure of dual filters and setting the parameters of each device reasonably. The switch is controlled by binary message to connect fiber Bragg gratings (FBGs) with different dispersion coefficients and maps different symbols to different chaotic attractors instead of the traditional way of directly sending messages to the channel. In addition, the generated signal no longer suffered from return map attacks. FBGs can compress and expand signals in the time domain to enhance the complexity of chaotic signals, and the dispersion value is an important key to effectively increasing the key space. Finally, the performance of TDSs concealment, sensitivity of parameter mismatch, and return map attack in this scheme are discussed in detail. The scheme still achieves a bit error rate (BER) of 1.74 × 10−4 when transmitting at a rate of 10Gbit/s under harsh communication environments such as 10 dB signal-to-noise ratio (SNR), which provides a reference for realizing high-speed and secure optical communication at the physical layer.

Transmission of Lamb wave in a micro-mechanically piezoelectric fiber-reinforced composite plate

The present work addresses the propagation characteristics of Lamb wave in a piezoelectric fiber-reinforced composite material plate. The considered plate is micro-mechanically modeled using the analytical techniques of the Strength of Materials and Rule of Mixtures. Secular equations are deduced for symmetric and antisymmetric modes of Lamb wave by employing suitable boundary conditions. The data of two distinct PFRC materials are taken into account, viz. PZT-5A-epoxy and CdSe-epoxy with different fiber volume fractions to investigate the latent characteristics of Lamb wave propagation. The amplitudes of dilatation and electrical potential of considered material are examined numerically and manifested graphically for both kinds of modes, i.e., symmetric and antisymmetric modes. The obtained graphical results are also compared for both kinds of modes. A comparative study has been carried out to exhibit the concealed characteristics of Lamb wave propagation for various distinct cases of considered structure for both symmetric and antisymmetric modes. The determined analytical results are in well agreement with the results present in the existing literature.

Optical authentication scheme based on all-optical neural network.

Diffractive deep neural network is architectural designs based on the principles of neural networks, which consists of multiple diffraction layers and has the remarkable ability to perform machine learning tasks at the speed of light. In this paper, a novel optical authentication system was presented that utilizes the diffractive deep neural network principle. By carefully manipulating a light beam with both a public key and a private key, we are able to generate a unique and secure image representation at a precise distance. The generated image can undergo authentication by being processed through the proposed authentication system. Leveraging the utilization of invisible terahertz light, the certification system possesses inherent characteristics of concealment and enhanced security. Additionally, the entire certification process operates solely through the manipulation of the light beam, eliminating the need for electronic calculations. As a result, the system offers rapid certification speed. The proposed optical authentication scheme is further validated through computer simulations, which showcase its robust security and high precision. This method holds immense potential for diverse applications in optical neural network authentication, warranting a broad scope of future prospects.

Gaussian meta-feature balanced aggregation for few-shot synthetic aperture radar target detection

Due to the high mobility and strong concealment characteristics of synthetic aperture radar (SAR) targets, the corresponding SAR datasets exhibit few-shot data properties, and there is a significant lack of research on few-shot target detection methods in the SAR domain. Furthermore, this study is subject to the following limitations: the scarcity of SAR data and significant sample variations make it difficult to control class centers using existing methods, and the learned models tend to be biased towards base classes while easily confusing novel classes with base classes. These limitations hinder the generalization of knowledge from base classes when detecting novel class targets. In this work, we propose a novel few-shot SAR target detection method based on Gaussian meta-feature balanced aggregation (GMFBA), which is based on meta-learning. Specifically, we first propose two novel feature aggregation methods with Gaussian metrics, namely Gaussian projection distribution metric (GPDM) and Gaussian kernel mean embedding metric (GKMEM). By estimating class distribution with variational autoencoders to replace traditional class prototypes, we sample from robust distributions and measure projection Wasserstein distance and Gaussian kernel mean embedding distance with prior distributions, obtaining the best robust support features under the optimal measurement results. Then, based on GPDM and GKMEM, we propose a novel balanced inter-class uncorrelated aggregation (BICUA) method, which extracts support features of each class according to the proportion of samples and aggregates them with query features in a balanced manner, promoting feature representation between different classes and ensuring no interference between features to significantly reduce confusion between base classes and novel classes. Specifically, GMFBA outperforms the state-of-the-art method G-FSOD significantly in all settings, achieving state-of-the-art performance. In contrast, the novel class detection performance of GMFBA has shown an average improvement of 8.56% on split1 and split2 of the SRSDD-v1.0 dataset, and an average improvement of 1.41% on split1 and split2 of the MSAR-1.0 dataset. The code is available at https://github.com/Caltech-Z/GMFBA.

Scattering of Love-type wave in piezoelectric–piezomagnetic layered structure due to abrupt thickening

The present paper discusses the scattering phenomenon of Love-type wave on the functionally graded piezoelectric material (FGPEM) stratum overlying a functionally graded piezomagnetic material (FGPMM) substrate containing abrupt thickening at the uppermost free surface. The expressions for dispersion relation and group velocity (GV) are obtained for four kinds of electric-magnetic boundary conditions. The expressions for mechanical displacement (MD) component, electric potential (EP), and magnetic potential (MP) functions for stratum and substrate are deduced by using the perturbation method and residue theorem. The effect of piezoelectric and piezomagnetic coupling parameters and abrupt thickening parameters on phase velocity (PV), GV, MD component, and EP & MP functions associated with the stratum are graphically represented. A comparative study is carried out to delineate the concealed characteristics of wave on the considered model due to three different shaped abrupt thickening for four kinds of electric-magnetic boundary conditions (i.e. electrically short and magnetically short (ESMS), electrically open and magnetically short (EOMS), electrically open and magnetically open (EOMO), and electrically short and magnetically open (ESMO)).

Rayleigh wave in a piezoelectric micro-mechanical bi-material system with mechanically and dielectrically imperfect interfaces

The present work investigates the propagation behavior of Rayleigh waves in a piezoelectric fiber-reinforced composite (PFRC) material stratum lying over a PFRC substrate with mechanically and dielectrically imperfect interfaces. The PFRC material structure is micro-mechanically modeled using the analytical techniques of the Strength of Materials (SM) and Rule of Mixtures (RM). Dispersion equations are obtained by employing suitable boundary conditions for electrically open and short conditions. The data of CdSe-epoxy and PZT-5A-epoxy materials as PFRC materials have been taken into account for fiber volume fraction to investigate the latent characteristics of Rayleigh wave propagation. Numerical calculations have been carried out to study the influence of mechanically compliant and dielectrically weakly (MCDW)/highly (MCDH) conducting imperfect interfaces on Rayleigh wave propagation in the considered structure. A comparative study has also been carried out to exhibit the concealed characteristics of Rayleigh wave propagation for various distinct cases in both the electrical conditions and manifested graphically for the fundamental mode of Rayleigh wave.

Takeout Platform Uses Big Data for Price Discrimination—Meituan Takeout Platform Case Study

In the era of big data, more and more take-out platforms use algorithms to analyze customer data. These algorithms will enable users to retrieve items that are more suitable for them, and also accelerate the speed of their receipt of goods, so that users can get a better shopping experience. However, the takeaway platform uses these data to implement price discrimination through algorithms, and treats different users differently, thus damaging the rights and interests of consumers. The price discrimination based on big data algorithm has the characteristics of concealment and complexity. Therefore, there are certain difficulties in the governance of this behavior, which requires the joint efforts of the national legal department, take-out platform and consumers.

A Deep 1-D CNN and Bidirectional LSTM Ensemble Model With Arbitration Mechanism for LDDoS Attack Detection

Low-rate Distributed Denial of Service (LDDoS) attacks are complex and common security issues, which disrupt the essential services of the varied and emerging networks. However, the traditional detection models are normally in a dilemma condition since LDDoS attacks have the characteristics of concealment and persistence in space and time dimensions. In this paper, we propose a novel, practical and fast ensemble model (FastCBLA-EM) to detect the LDDoS attacks. In the FastCBLA-EM, a dilated padding-based dual sliding method is worked out for producing fixed-length chronological order samples from flows per well-tuned packet step. Next, a competitive network composed of 1-D Convolutional Neural Network (1-D CNN) and a bidirectional Long Short Term Memory (bi-LSTM) network is employed to learn both spatial and temporal representations of samples, parallelly. Finally, an arbitration mechanism based on the enhanced attention method is used to assemble and weigh these representations for detecting LDDoS attacks. We compare the performances of the FastCBLA-EM with other models by obtaining a group of well-tuned hyperparameters in the revised ISCX-2016-SlowDos flow-based dataset. Experimental results demonstrate that the detection accuracy of the FastCBLA-EM can be up to 99.7% and the time complexity is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n)$</tex-math></inline-formula> .

Rock response characteristics in the area of the hidden collapse column induced by mining

The hidden collapse column has the characteristics of concealment, suddenness and connection with karst water, which pose a serious threat to safe production in coal mines. In this study, a numerical model of collapse columns with a random distribution of pores was constructed by a finite difference calculation program. Numerical simulation analyses of the coal mining face advance were carried out to explore the response characteristics of rock in the collapse column area under the influence of four factors: mining impact, lateral pressure coefficient, pore water and confined groundwater. The results show that the abutment pressure reaches its maximum when the working face advances to the boundary of the elastic stress elevated zone of the collapse column. The plastic zone above the collapse column shows a "Λ" shape and keeps growing during the advance of the working face. The height of the plastic zone in the top and bottom of the coal seam increases with the increasing lateral pressure coefficient. An increase in the lateral pressure coefficient can amplify the effect of mining on the vertical displacement and plastic zone distribution of the collapse column. When the collapse column is connected to the confined groundwater, the pore water pressure will increase significantly with the advancement of the working face. Under the joint action of confined groundwater and pore water, the extent of the plastic zone around the collapse column will be larger, and the development of the plastic zone inside the collapse column is especially obvious. This study will provide a basis for revealing the rock response rules in the area of the hidden collapse column during mining.

Static creep rockburst mechanism and CT inversion verification of deep thick coal seam roadways

AbstractCompared with the traditional “dynamic‐static” load rockburst induced by working face mining, a new typical disaster with concealment characteristics has appeared in deep thick coal seam roadways, namely, static creep rockburst, the occurrence mechanism of which remains unclear. Through the method of combining theory with practice, this paper elucidated the mechanical mechanism of static creep rockburst of deep thick coal seam roadways, along with the CT inversion of its rockburst risk. Next, by establishing the creep model and three‐dimensional creep formula under static conditions, it was observed that the main conditions for the unstable creep of deep thick coal seam roadway were high stress and thick coal seam. In addition, by analyzing the unstable creep failure process of surrounding rock in static roadways, it was found that the weakening of surrounding rock strength and stress relief were the reasons for starting rockburst and reducing impedance. The mechanical conditions of creep rockburst caused by stress superposition in weak areas of the roadway under static conditions were proposed, and the criterion of rockburst possibility was given. Finally, according to the characteristics of spontaneity and time delay of such a rockburst disaster, the static creep rockburst evaluation of thick coal seam roadway before mining was achieved by CT inversion, and the research conclusions were thoroughly verified.

Distributed Multi-Attention Generative Adversarial Network for Surrounding Vehicles Trajectories Prediction Based On Comprehensive Social Repulsion

Trajectories prediction for vehicles is a key task in autonomous driving. This task is complicated by the presence of social interactions between vehicles and their physical constraints with the scene. From dominant and concealed characteristics of the driving scenarios, this paper proposes a distributed multi-attention generative adversarial network named DTMA-GAN. Firstly, temporal and spatial attention heads are used in the framework of DTMA-GAN to extract scene information, driving features of self-vehicles and social interaction features among vehicles. These features help the model learn the position in the large scene and capture the most salient parts of the image associated with the path. Secondly, a subset of the Euclidean space is modeled using the lane environment and a collision-free set of states is constructed to generate a safe driving area via the environment occupancy points set. Finally, the multiple trajectories with confidence generated by GAN are combined with this candidate driving area, and an accurate, reasonable, and long-term vehicle trajectory is selected using non-maximum suppression. And we use publicly available NGSIM and Argoverse datasets for model evaluation. Experimental results show that compared with CS-LSTM, GRIP and other excellent algorithms, this algorithm can effectively reduce the Root Mean Square Error for trajectory prediction in dynamic environments. And ablation experiments show that all the module components are effective.

PREDICTION OF KARST GROUND COLLAPSE BASED ON FACTOR ANALYSIS-GA-ELM MODEL

Karst ground collapse is the result of many factors, and has the characteristics of concealment and abruptness. In order to predict karst ground collapse quickly and accurately, this paper presents a karst ground collapse prediction model which is based on factor analysis, genetic algorithm(GA) and extreme learning machine(ELM). Firstly, we identify eight typical influencing factors in the karst area. Then, we extract five common factors by factor analysis. Finally, we input the common factors into GA-ELM model and predict karst ground collapse. The sample data contains 20 groups of actual case data, of which 12 groups as training sets and the other 8 groups as test sets. The conclusions are as follows: Factor analysis can not only simplify the ELM model network structure, but also improve the prediction accuracy under the same number of hidden layer of neuron nodes. In the case of a small number of samples, the prediction accuracy of the ELM model can be improved by increasing the number of hidden layer neuron nodes. Compared with the ELM model, the prediction accuracy of GA-ELM model is improved significantly, with stronger learning and prediction ability. The prediction method of karst ground collapse based on Factor Analysis-GA-ELM model is simple, accurate and efficient.

Exploring Deep Learning Features for Automatic Classification of Human Emotion Using EEG Rhythms

Emotion recognition (ER) from Electroencephalogram (EEG) signals is a challenging task due to the non-linearity and non-stationarity nature of EEG signals. Existing feature extraction methods cannot extract the deep concealed characteristics of EEG signals from different layers for efficient classification scheme and also hard to select appropriate and effective feature extraction methods for different types of EEG data. Hence this study intends to develop an efficient deep feature extraction based method to automatically classify emotion status of people. In order to discover reliable deep features, five deep convolutional neural networks (CNN) models are considered: AlexNet, VGG16, ResNet50, SqueezeNet and MobilNetv2. Pre-processing, Wavelet Transform (WT), and Continuous Wavelet Transform (CWT) are employed to convert the EEG signals into EEG rhythm images then five well-known pretrained CNN models are employed for feature extraction. Finally, the proposed method puts the obtained features as input to the support vector machine (SVM) method for classifying them into binary emotion classes: valence and arousal classes. The DEAP dataset was used in experimental works. The experimental results demonstrate that the AlexNet features with Alpha rhythm produces better accuracy scores (91.07% in channel Oz) than the other deep features for the valence discrimination, and the MobilNetv2 features yields the highest accuracy score (98.93% in Delta rhythm (with channel C3) for arousal discrimination.

Love-type wave in low-velocity piezoelectric-viscoelastic stratum with mass loading

An exact approach is used to investigate the propagation of a Love-type wave in a low-velocity piezoelectric-viscoelastic material (PV) stratum bonded to a functionally graded transversely isotropic viscoelastic (FGTIV) material substrate. The dispersion relations are obtained for electrically open (EO) and electrically short (ES) conditions on the upper free surface of the considered structure by using Valeev’s method of infinite determinants. The expression of group velocity of a Love-type wave in a low-velocity layered structure is also obtained in compact form. The obtained closed-form dispersion relations of a Love-type wave are matched with the standard result of a Love-type wave when reduced to the isotropic case. In order to observe the concealed characteristics of a Love-type wave, a detailed study is carried out graphically, to forefront the effect of various influencing parameters, viz. piezoelectric coupling parameter and magnifying gradient (MG) parameters. The profound impact of mass loading on the upper free surface of the considered layered structure is also studied.

Applications of airborne laser scanning for determining marine geoid and surface waves properties

ABSTRACT Marine and coastal applications require now more than ever accurate and expansive understanding of the marine surface topography in the offshore domain, with the relationship of sea surface heights and geoid (equipotential surface of the Earth) being the key components. This study demonstrates some of the under-utilised and unexplored marine applications of airborne laser scanning (ALS) with respect to geoid heights verification and validation, as well as identification of surface waves properties. A synergistic methodology was developed that combines ALS measurements, hydrodynamic models and tide gauge records in conjunction with geoid models. Examination of the determined discrepancies with respect to the ALS-derived sea surface heights reveals concealed characteristics of the geoid and dynamic topography. A sea surface height accuracy of 1.7 cm in terms of standard deviation was achieved using a high-resolution regional geoid model. Furthermore, ALS point cloud data can be used to retrieve surface wavefield properties (waves heights, wavelengths and directions). In this study a direct method approach is presented. Such a deeper insight into the wave dynamics plays an enormous contribution in the understanding and quantification of coastal processes (erosion and sediment transport), as well as validation and calibration of wave models and relevant sensors.

Initiator or Intermediary? A Case Study on Network Relation of Environmental Regulatory Capture in China.

Behind the environmental regulatory capture (ERC) lies a complex network of interactions and interests. Identifying the roles of stakeholders in the ERC network and their behavioral motives can illuminate the mechanism of ERC incidents, and provide policy recommendations for reducing other types of regulatory capture. Drawing on the regulatory capture and principal-agent theories, this study develops a triple-layer principal-agent model of environmental regulation practices in China. We further conduct a social network analysis (SNA) on the ERC case in the Environmental Protection Bureau (EPB) of Bobai County, Guangxi Province, China to illustrate the hidden interactions and interest transmission structure among stakeholders in ERC. The results show that the ERC presents obvious characteristics of concealment and complexity, and individual capture often evolves into collective capture. Different stakeholders are in different positions and play different roles in the network. The environmental regulatory authority, the commissioned regulatory agency, and the agency of enterprises form the core power circle of the ERC network, in which the first two play the role of intermediary and the latter acts as an initiator. They together occupy the structural hole position and dominate the evolution of the ERC network. Peripheral structure stakeholders play the role of “bridge” and profit from the expanding ERC network. It is recommended that the principle of decentralization and the balance of power must be taken into consideration. Quantitative analysis methods such as SNA should be applied to clarify accountability when punishing responsible persons. More subjects are also encouraged to participate in environmental regulations and report illegal acts actively. Finally, a blacklist system should be established in the field of environmental protection for regulating the professional and social morality of all parties.

Whole-process monitoring of sinkhole collapse based on distributed optical fiber strain-vibration joint system and its case study in railway subgrade

Abstract Sinkhole is a unique geological disaster, which has the characteristics of concealment in space, accumulation in the development process and sudden collapse caused by roof instability, and the whole development process contains deformation and vibration information. This paper presents a distributed optical fiber strain-vibration joint system based on distributed optical fiber strain and vibration sensing techniques for collapse monitoring. Firstly, a multi-parameter distributed fiber optical sensor is developed and its strain-vibration sensing characteristics are studied. And then, one model test and a case study related to collapse monitoring are carried out, the multi-parameter distributed optical fiber sensors are used to monitor the development of the sinkholes. In comparison of the strain and the vibration data, the strain can be used to evaluate the subsidence, and the vibration signal can be used to monitor the sudden collapse, which proves it is a promising solution for whole-process monitoring of sinkhole collapse.

Classification of Alcoholic EEG Signals Using a Deep Learning Method

Most of the traditional alcoholism detection methods are developed based on machine learning based methods that cannot extract the deep concealed characteristics of Electroencephalogram (EEG) signals from different layers. Hence, this study aims to introduce a deep leaning-based method that can automatically identify alcoholic EEG signals. It also explores if a hand-crafted feature extraction method is worth applying to deep learning techniques for classification of alcoholism. To investigate this, this paper presents two deep learning-based algorithms for classification of alcoholic EEG signals for comparison. In Algorithm 1, Principal Component Analysis (PCA) based feature extraction technique has been applied to extract representative components and then the extracted features are used as input to Artificial neural network (ANN) for classification. In Algorithm 2, the raw EEG data are directly used as inputs to a deep learning method: `long short-term memory (LSTM)' for detection of alcoholism. The proposed algorithms were tested on a publicly available UCI Alcoholic EEG dataset. The experimental results show that the proposed Algorithm 2 could achieve an average classification accuracy of 93% while this accuracy is 86% for the proposed Algorithm 1. The comparative evaluations with the state-of-the-art algorithms indicate that Algorithm 2 also outperforms other competing algorithms in the literature. Thus deep learning algorithm when applied to raw data, can produce better performance than the combination of the hand-crafted feature method and the deep leaning algorithm. Our proposed system can be used to determine the extent of alcoholism-related changes in EEG signals and the effectiveness of therapeutic plans.

The Characteristics and Tectonic Evolution of Deep Buried Structures in Manas in the Southern Margin of Junggar Basin

The Manas anticline belongs to the second tectonic belt in front of the southern margin of Junggar Basin. Due to the multi-stage tectonic compression activity of the Indian plate and the Eurasian plate since the Cenozoic era, the tectonic activity is frequent and the deep tectonic style is complex, so the deep tectonic style of the quasi-south has not been clearly understood. For the study of the deep concealed characteristics of Manas anticline structure, in this paper, we will use the theory of fault-related fold, the geometric analysis and the balanced section forward simulation to construct the detailed structural analysis and simulation of kinematics of structural deformation process of the type section of the Manas anticline based on wide line seismic profile. The study shows that the vertical and upward parts of the Manas anticline are fault-propagated folds and thrust nappe structures while the deep parts are mainly composed of complex structural triangular wedges, burst structures and secondary fold regulating faults. The deformation process is the early low-amplitude fault-turning fold, which is blocked at the front of thrust and forms complex triangular wedge and burst structure. In the late neogene, large thrust faults and fault-propagated folds were developed along the bottom of Anji Haihe formation under the action of strong thrust nappe in the piedmont.