Instance Reweighting Adversarial Training Based on Confused Label

Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks,which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights.The probability margin(PM)method is a promising approach to continuously and path-independently mea-suring such closeness between the example and decision boundary.However,the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories,where the latter is closer to multi-classification decision boundaries and is supported to be more critical in our observation.To tackle this problem,this paper proposed an improved PM criterion,called confused-label-based PM(CL-PM),to measure the closeness mentioned above and reweight adversarial examples during training.Specifi-cally,a confused label(CL)is defined as the label whose prediction probability is greater than that of the ground truth label given a specific adversarial example.Instead of considering the discrepancy between the probability of the true label and the probability of the most misclassified label as the PM method does,we evaluate the closeness by accumulating the probability differences of all the CLs and ground truth label.CL-PM shares a negative correlation with data vulnerability:data with larger/smaller CL-PM is safer/riskier and should have a smaller/larger weight.Experiments demonstrated that CL-PM is more reliable in indicating the closeness regarding multiple misclassified categories,and reweighting adversarial training based on CL-PM outperformed state-of-the-art counterparts.

A method to predict rockburst using temporal trend test and its application

Rockbursts have become a significant hazard in underground mining,underscoring the need for a robust early warning model to ensure safety management.This study presents a novel approach for rockburst prediction,integrating the Mann-Kendall trend test(MKT)and multi-indices fusion to enable real-time and quantitative assessment of rockburst hazards.The methodology employed in this study involves the development of a comprehensive precursory index library for rockbursts.The MKT is then applied to analyze the real-time trend of each index,with adherence to rockburst characterization laws serving as the warning criterion.By employing a confusion matrix,the warning effectiveness of each index is assessed,enabling index preference determination.Ultimately,the integrated rockburst hazard index Q is derived through data fusion.The results demonstrate that the proposed model achieves a warning effectiveness of 0.563 for Q,surpassing the performance of any individual index.Moreover,the model’s adaptability and scalability are enhanced through periodic updates driven by actual field monitoring data,making it suitable for complex underground working environments.By providing an efficient and accurate basis for decision-making,the proposed model holds great potential for the prevention and control of rockbursts.It offers a valuable tool for enhancing safety measures in underground mining operations.

Risk assessment of rockburst using SMOTE oversampling and integration algorithms under GBDT framework

Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is gradually becoming a trend.In this study,the integrated algorithms under Gradient Boosting Decision Tree(GBDT)framework were used to evaluate and classify rockburst intensity.First,a total of 301 rock burst data samples were obtained from a case database,and the data were preprocessed using synthetic minority over-sampling technique(SMOTE).Then,the rockburst evaluation models including GBDT,eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Categorical Features Gradient Boosting(CatBoost)were established,and the optimal hyperparameters of the models were obtained through random search grid and five-fold cross-validation.Afterwards,use the optimal hyperparameter configuration to fit the evaluation models,and analyze these models using test set.In order to evaluate the performance,metrics including accuracy,precision,recall,and F1-score were selected to analyze and compare with other machine learning models.Finally,the trained models were used to conduct rock burst risk assessment on rock samples from a mine in Shanxi Province,China,and providing theoretical guidance for the mine's safe production work.The models under the GBDT framework perform well in the evaluation of rockburst levels,and the proposed methods can provide a reliable reference for rockburst risk level analysis and safety management.

Confusing Object Detection:A Survey

Confusing object detection(COD),such as glass,mirrors,and camouflaged objects,represents a burgeoning visual detection task centered on pinpointing and distinguishing concealed targets within intricate backgrounds,leveraging deep learning methodologies.Despite garnering increasing attention in computer vision,the focus of most existing works leans toward formulating task-specific solutions rather than delving into in-depth analyses of methodological structures.As of now,there is a notable absence of a comprehensive systematic review that focuses on recently proposed deep learning-based models for these specific tasks.To fill this gap,our study presents a pioneering review that covers both themodels and the publicly available benchmark datasets,while also identifying potential directions for future research in this field.The current dataset primarily focuses on single confusing object detection at the image level,with some studies extending to video-level data.We conduct an in-depth analysis of deep learning architectures,revealing that the current state-of-the-art(SOTA)COD methods demonstrate promising performance in single object detection.We also compile and provide detailed descriptions ofwidely used datasets relevant to these detection tasks.Our endeavor extends to discussing the limitations observed in current methodologies,alongside proposed solutions aimed at enhancing detection accuracy.Additionally,we deliberate on relevant applications and outline future research trajectories,aiming to catalyze advancements in the field of glass,mirror,and camouflaged object detection.

Image encryption algorithm based on multiple chaotic systems and improved Joseph block scrambling

With the rapid development of digital information technology,images are increasingly used in various fields.To ensure the security of image data,prevent unauthorized tampering and leakage,maintain personal privacy,and protect intellectual property rights,this study proposes an innovative color image encryption algorithm.Initially,the Mersenne Twister algorithm is utilized to generate high-quality pseudo-random numbers,establishing a robust basis for subsequent operations.Subsequently,two distinct chaotic systems,the autonomous non-Hamiltonian chaotic system and the tentlogistic-cosine chaotic mapping,are employed to produce chaotic random sequences.These chaotic sequences are used to control the encoding and decoding process of the DNA,effectively scrambling the image pixels.Furthermore,the complexity of the encryption process is enhanced through improved Joseph block scrambling.Thorough experimental verification,research,and analysis,the average value of the information entropy test data reaches as high as 7.999.Additionally,the average value of the number of pixels change rate(NPCR)test data is 99.6101%,which closely approaches the ideal value of 99.6094%.This algorithm not only guarantees image quality but also substantially raises the difficulty of decryption.

A chaotic hierarchical encryption/watermark embedding scheme for multi-medical images based on row-column confusion and closed-loop bi-directional diffusion

Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is designed,and a multiimage encryption(MIE)algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper.While ensuring secure communication of medical image information,people with different security levels have different levels of decryption keys,and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys.The highest security level can obtain decrypted images without watermarks,and at the same time,patient information and copyright attribution can be verified by obtaining watermark images.The experimental results show that the scheme is sufficiently secure as an MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.

Incidence and Risk Factors of Sub-syndromal Delirium in Patients after Cardiac Surgery

[Objectives]This study aimed to investigate the incidence and risk factors associated with SSD in patients following cardiac surgery.[Methods]A total of 378 patients who underwent cardiac surgery in Taihe Hospital were recruited and screened.Diagnosis of delirium was made using evaluation methods and DSM-5 criteria.SSD was defined as the presence of one or more core features of delirium without meeting the full diagnostic criteria.Statistical analysis included independent samples t-test for group comparisons and binary logistic regression analysis to identify independent risk factors for SSD after cardiac surgery.[Results]Among the 378 subjects,112(29.63%)had SSD,28(7.41%)had delirium,and the remaining 238 patients(62.96%)did not present with delirium.Univariate analysis revealed that age,APACHE II score,duration of aortic clamping,length of ICU stay,duration of sedation use,and daily sleep time were significant risk factors for the occurrence of SSD(P<0.05).Logistic regression analysis identified age>70 years old,APACHE II score>20 points,length of ICU stay>5 d,and duration of sedation use>24 h as independent risk factors for SSD after cardiac surgery(P<0.05).A functional model was fitted based on the analysis results of the binary logistic regression model,yielding the equation logit P=1.472X_(1)+2.213X_(2)+3.028X_(3)+1.306X_(4).[Conclusions]Comprehensive clinical assessment is crucial for patients undergoing cardiac surgery,and appropriate preventive measures should be taken for patients with identified risk factors.Close monitoring of the patient s consciousness should be implemented postoperatively,and timely interventions should be conducted.Further research should focus on model validation and optimization.

Dazed and Confused in Academia

Recently,Joe,a foreign English teacher in China,said to me,"I feel dazed and disoriented;my students here are unusually quiet. I can’t tell if they understand my lessons." His plight is not surprising.Many foreign ESL(English as a second language)instructors often feel confused when they first step into a Chinese classroom.Chinese ESL students are different because their

Confused About Confucius?

confucius is one of the rare men who impacted human history by dint of his personal and intellectual gifts. So much legend and tradition whirls around this great figure that it becomes hard to clearly understand the man and his teachings.

Asymmetric image encryption algorithm based on a new three-dimensional improved logistic chaotic map

Based on some analyses of existing chaotic image encryption frameworks and a new designed three-dimensional improved logistic chaotic map(3D-ILM),an asymmetric image encryption algorithm using public-key Rivest–Shamir–Adleman(RSA)is presented in this paper.In the first stage,a new 3D-ILM is proposed to enhance the chaotic behavior considering analysis of time sequence,Lyapunov exponent,and Shannon entropy.In the second stage,combined with the public key RSA algorithm,a new key acquisition mathematical model(MKA)is constructed to obtain the initial keys for the 3D-ILM.Consequently,the key stream can be produced depending on the plain image for a higher security.Moreover,a novel process model(NPM)for the input of the 3D-ILM is built,which is built to improve the distribution uniformity of the chaotic sequence.In the third stage,to encrypt the plain image,a pre-process by exclusive OR(XOR)operation with a random matrix is applied.Then,the pre-processed image is performed by a permutation for rows,a downward modulo function for adjacent pixels,a permutation for columns,a forward direction XOR addition-modulo diffusion,and a backward direction XOR addition-modulo diffusion to achieve the final cipher image.Moreover,experiments show that the the proposed algorithm has a better performance.Especially,the number of pixels change rate(NPCR)is close to ideal case 99.6094%,with the unified average changing intensity(UACI)close to 33.4634%,and the information entropy(IE)close to 8.

Dual Image Cryptosystem Using Henon Map and Discrete Fourier Transform

This paper introduces an efficient image cryptography system.The pro-posed image cryptography system is based on employing the two-dimensional(2D)chaotic henon map(CHM)in the Discrete Fourier Transform(DFT).The proposed DFT-based CHM image cryptography has two procedures which are the encryption and decryption procedures.In the proposed DFT-based CHM image cryptography,the confusion is employed using the CHM while the diffu-sion is realized using the DFT.So,the proposed DFT-based CHM image crypto-graphy achieves both confusion and diffusion characteristics.The encryption procedure starts by applying the DFT on the image then the DFT transformed image is scrambled using the CHM and the inverse DFT is applied to get the final-ly encrypted image.The decryption procedure follows the inverse procedure of encryption.The proposed DFT-based CHM image cryptography system is exam-ined using a set of security tests like statistical tests,entropy tests,differential tests,and sensitivity tests.The obtained results confirm and ensure the superiority of the proposed DFT-based CHM image cryptography system.These outcomes encourage the employment of the proposed DFT-based CHM image cryptography system in real-time image and video applications.

A Novel Lightweight Image Encryption Scheme

Encryption algorithms are one of the methods to protect dataduring its transmission through an unsafe transmission medium. But encryptionmethods need a lot of time during encryption and decryption, so itis necessary to find encryption algorithms that consume little time whilepreserving the security of the data. In this paper, more than one algorithmwas combined to obtain high security with a short implementation time. Achaotic system, DNA computing, and Salsa20 were combined. A proposed5D chaos system was used to generate more robust keys in a Salsa algorithmand DNA computing. Also, the confusion is performed using a new SBox.The proposed chaos system achieves three positive Lyapunov values.So results demonstrate of the proposed scheme has a sufficient peak signalto-noise ratio, a low correlation, and a large key space. These factors makeit more efficient than its classical counterpart and can resist statistical anddifferential attacks. The number of changing pixel rates (NPCR) and theunified averaged changed intensity (UACI) values were 0.99710 and UACI33.68. The entropy oscillates from 7.9965 to 7.9982 for the tested encryptedimages. The suggested approach is resistant to heavy attacks and takes lesstime to execute than previously discussed methods, making it an efficient,lightweight image encryption scheme. The method provides lower correlationcoefficients than other methods, another indicator of an efficient imageencryption system. Even though the proposed scheme has useful applicationsin image transmission, it still requires profound improvement in implementingthe high-intelligence scheme and verifying its feasibility on devices with theInternet of Things (IoT) enabled.

Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

IIF(Indirect Immune Florescence)has gained much attention recently due to its importance in medical sciences.The primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune diseases.The use of IIF for detecting autoimmune diseases is widespread in different medical areas.Nearly 80 different types of autoimmune diseases have existed in various body parts.The IIF has been used for image classification in both ways,manually and by using the Computer-Aided Detection(CAD)system.The data scientists conducted various research works using an automatic CAD system with low accuracy.The diseases in the human body can be detected with the help of Transfer Learning(TL),an advanced Convolutional Neural Network(CNN)approach.The baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells(HEP)type II cells and the Sub Class Discriminant(SDA)analysis technique used to detect autoimmune diseases.The technique yielded an accuracy of up to 90.03%,which was not reliable for detecting autoimmune disease in the mitotic cells of the body.In the current research,the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of TL.Data augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL models.These models are named Inception V3,Dens Net 121,VGG-16,and Mobile Net,and their performance can be calculated through parameters of the confusion matrix(accuracy,precision,recall,and F1 measures).The results show that the accuracy value of VGG-16 is 78.00%,Inception V3 is 92.00%,Dense Net 121 is 95.00%,and Mobile Net shows 88.00%accuracy,respectively.Therefore,DenseNet-121 shows the highest performance with suitable analysis of autoimmune diseases.The overall performance highlighted that TL is a suitable and enhanced technique compared to its counterparts.Also,the proposed technique is used to detect autoimmune diseases with a minimal margin of errors and flaws.

Predictive-Analysis-based Machine Learning Model for Fraud Detection with Boosting Classifiers

Fraud detection for credit/debit card,loan defaulters and similar types is achievable with the assistance of Machine Learning(ML)algorithms as they are well capable of learning from previous fraud trends or historical data and spot them in current or future transactions.Fraudulent cases are scant in the comparison of non-fraudulent observations,almost in all the datasets.In such cases detecting fraudulent transaction are quite difficult.The most effective way to prevent loan default is to identify non-performing loans as soon as possible.Machine learning algorithms are coming into sight as adept at handling such data with enough computing influence.In this paper,the rendering of different machine learning algorithms such as Decision Tree,Random Forest,linear regression,and Gradient Boosting method are compared for detection and prediction of fraud cases using loan fraudulent manifestations.Further model accuracy metric have been performed with confusion matrix and calculation of accuracy,precision,recall and F-1 score along with Receiver Operating Characteristic(ROC)curves.

A New Method for Image Tamper Detection Based on an Improved U-Net

With the improvement of image editing technology,the threshold of image tampering technology decreases,which leads to a decrease in the authenticity of image content.This has also driven research on image forgery detection techniques.In this paper,a U-Net with multiple sensory field feature extraction(MSCU-Net)for image forgery detection is proposed.The proposed MSCU-Net is an end-to-end image essential attribute segmentation network that can perform image forgery detection without any pre-processing or post-processing.MSCU-Net replaces the single-scale convolution module in the original network with an improved multiple perceptual field convolution module so that the decoder can synthesize the features of different perceptual fields use residual propagation and residual feedback to recall the input feature information and consolidate the input feature information to make the difference in image attributes between the untampered and tampered regions more obvious,and introduce the channel coordinate confusion attention mechanism(CCCA)in skip-connection to further improve the segmentation accuracy of the network.In this paper,extensive experiments are conducted on various mainstream datasets,and the results verify the effectiveness of the proposed method,which outperforms the state-of-the-art image forgery detection methods.

An Analysis on the Role Identification of Clones in Never Let Me Go

With the guidance of Erikson’s identity theory,the article analyzes the clones’identity exploration through role identification in Never Let Me Go.It interprets the clones’puzzlement about their identity,and the process of their identity quest as well as role identification.Through the specific analysis,it is concluded that the clones,represented by Kathy,Tommy and Ruth,have gained self-certainty and social identity by realizing the role identity as a“carer”and a“donor”,in the meantime,they have constructed their identity as social persons with souls like ordinary people.Furthermore,the findings shows that Never Let Me Go is actually a microcosm of human’s quest for identity.Ishiguro aims to express his meditation on human life in the novel:human life is a process of seeking self-identity and social roles,and then fulfilling the obligations of the roles,which confirms Ishiguro’s internationalism that he attempts to convey his contemplation on human existence through his works.

Leaf epidermal characters of Lonicera japonica and Lonicera confuse and their ecology adaptation

The leaf epidermis of Japanese honeysuckle （Lonicera japonica Thunb.） and Wild Honeysuckle （Lonicera confusa） in the genus of Flos Lonicerae were mainly observed by scanning electron microscopes （SEM） to study the characteristics of stomata, trichomes and dermal cell, etc.. The results showed that stoma exists only on the lower epidermis and its distribution is irregular, and leaf epidermis consist of epidermis cells, stoma complexes and bushy trichomes including glandular hair and non-glandular hair. On the upper epidermis, anticlinal wall caves in sinuous groove to countercheck the transpiration. Evidences from leaf morphological structures serve as another proof on drought-resistant mechanisms. Some strumaes distributing regularly are hypothesized as oxalic calcium on the lower epidermis under laser scanning confocal microscopy （LSCM） with Fluo-3/AM, which can increase their endurance to drought stress. Therefore, the above characteristics of Flos Lonicerae can reduce the loss of water and make Japanese honeysuckle and Wild Honeysuckle adapt to the droughty environment at Karst area in southwest China. However, there is some difference of the two species. From the SEM （Scanning Electron Microscopy） result, it is shown that on the upper epidermis, some glandular hair regularly present along the midrib of Japanese honeysuckle, but Wild Honeysuckle has no glandular hair on the upper epidermis, which can verify the relationships of Flos Lonicerae species and provide the significance for classification of Flos Lonicerae.

Study on Extracting Information about Settlements Distribution in Western Jilin Province Based on Decision Tree Model

With western Jilin Province as the study region, spectral characteristics and texture features of remote sensing images were taken as the classification basis to construct a Decision Tree Model and extract information about settlements in western Jilin Province, and the manually-extracted information about settlements in western Jilin Province was evaluated by confusion matrix. The results showed that Decision Tree Model was convenient for extracting settlements information by integrating spectral and texture features, and the accuracy of such a method was higher than that of the traditional Maximum Liklihood Method, in addition, calculation methods of extracting settlements information by this mean were concluded.

Lessons learned from the past serving as a guide for the future---On a state harmonious development from Naipaurs novel A bend in the river

Naipaul＇s novel A bend in the river illustrates the social confusion and people＇s destitution and wasting time in African countries after their national independence. An important reason is the president＇s self-publicity, tyrannical hess and destructiveness. Whereas the author＇s subjective suspicion is visible in democratic development of African countries. Indeed, in a country development, it is quite necessary to depend on its citizen, observing public feelings; controlling a state with science; benefiting citizen; utilizing resources; constructing homestead, intending creation with a harmonious and stable way.

《英美文学选读》辅导(16)

1.文学常识1.Modernism-It is a label loosely applied to theworks of certain writers of the late nineteenthand early twentieth centuries who investigatedthe structure and texture of literature andchallenged its CONVENTIONS.The years from1910 to 1930 are often called the Era of Moder-nism,for there seems to have been in both