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.

Candidate Expansion Algorithm Based on WeightedSyllable Confusion Matrix for Mandarin LVCSR

The inclusion of more potentially correct words in the candidate sets is important to improve the accuracy of Large Vocabulary Continuous Speech Recognition （LVCSR）. A candidate expansion algorithm based on the Weighted Syllable Confusion Matrix （WSCM） is proposed. First, WSCM is derived from a confusion network. Then, the reeognised candidates in the confusion network is used to conjeeture the most likely correct words based on WSCM, after which, the conjectured words are combined with the recognised candidates to produce an expanded candidate set. Finally, a combined model having mutual information and a trigram language model is used to rerank the candidates. The experiments on Mandarin film data show that an improvement of 9.57% in the character correction rate is obtained over the initial recognition performance on those light erroneous utterances.

Confusion of Life in The Catcher in the Rye:Viewed From Holden's Inner World

The Catcher in the Rye is a novel published in 1951.Since its publication,it has been listed as one of the world greatest masterpieces.The story in the novel deals with a rebellious teenage schoolboy Holden and his quixotic experiences in New York,taking place in December.Through the tone of a middle schoolboy,the author shows the deepest confusion of teenagers during 1950 s in their lives.It also exposes a clearer understanding of adolescents' particular psychology.This article gives a discussion of Holden's confusion of life viewed from his inner world.

Meige Syndrome: An Eternal Diagnostic Confusion

Meige syndrome is an idiopathic dystonia characterized by combination of blepharospasm and involuntary movements of the lower facial and/or masticatory (jaw) muscles. The condition is rare and has a variety of clinical presentations which often lead to its misdiagnosis. We report a case of Meige syndrome repeatedly misdiagnosed and treated unsuccessfully as conversion disorder.

Iatrogenic Horner's syndrome: A cause for diagnostic confusion in the emergency department

Horner＇s syndrome （HS） results from interruption of sympathetic nervous supply to the eye and manifests clinically with partial ptosis, miosis and enophthalmos, along with anhidrosis of face on the affected side.

Centroids analysis for circle of confusion in reverse Hartmann test

The point spread function（PSF） is investigated in order to study the centroids algorithm in a reverse Hartmann test（RHT） system. Instead of the diffractive Airy disk in previous researches, the intensity of PSF behaves as a circle of confusion（CoC） and is evaluated in terms of the Lommel function in this paper. The fitting of a single spot with the Gaussian profile to identify its centroid forms the basis of the proposed centroid algorithm. In the implementation process, gray compensation is performed to obtain an intensity distribution in the form of a two-dimensional（2D） Gauss function while the center of the peak is derived as a centroid value. The segmental fringe is also fitted row by row with the one-dimensional（1D） Gauss function and reconstituted by averaged parameter values. The condition used for the proposed method is determined by the strength of linear dependence evaluated by Pearson＇s correlation coefficient between profiles of Airy disk and CoC. The accuracies of CoC fitting and centroid computation are theoretically and experimentally demonstrated by simulation and RHTs. The simulation results show that when the correlation coefficient value is more than 0.9999, the proposed centroid algorithm reduces the root-mean-square error（RMSE） by nearly one order of magnitude, thus achieving an accuracy of - 0.01 pixel or better performance in experiment. In addition, the 2D and 1D Gaussian fittings for the segmental fringe achieve almost the same centroid results, which further confirm the feasibility and advantage of the theory and method.

US STRATEGY TOWARD MIDDLE EAST:CONFUSION IN TRANSFORMATION

During the Cold War,the United States implemented an"offshore balance"policy in the Middle East,on the one hand,competing with the Soviet Union for a sphere of influence,and on the other hand,not directly engaging in the war.After the end of the Cold War,the US,as the sole super power,switched to the strategy of direct intervention in the Middle East.

A Brief Discussion on the Causes and Countermeasures of College Students'Psychological Confusion and Mental Health Problems

College period is one of the most critical periods of one person’s life for it is an important period of establishing world concept,outlook on life and values.Various challenges and pressures have had a great impact on the mental health of college students.In that case,college students’psychological confusion and mental health problems occur frequently and the overall condition of college students’psychological problems is worrying.The mental health of college students has also attracted the attention of colleges and the society.Besides,the mental health education courses have been adopted to guide college students.This article aims to analyze the causes of college students’psychological confusion and mental health problems and also the obstacles or difficulties in solving them from the perspective of college students'cultivation of positive psychological quality and mental health education.And corresponding solutions from both the outside and personal perspectives.

The Reflection on Spiritual Confusion and the Desire for Return to Humanity in T.S.Eliot's The Love Song of J.Alfred Prufrock

Although in the name of love,The Love Song is actually a middle-aged man's internal monologue of his courage and cowardice.This essay will analyze the stream of consciousness of Prufrock and the world in which he lives,the author's ruthless exposure to society's emptiness and ultimate concern for human development,and poetic techniques he utilizes including metaphor,repetition,and allusion,to express his reflection on spiritual confusion and desire for return to human nature.

Text retrieval algorithm that decreases confusion

To overcome the problem that the confusion between texts limits the precision in text re- trieval, a new text retrieval algorithm that decrease confusion （DCTR） is proposed. The algorithm constructs the searching template to represent the user＇ s searching intention through positive and negative training. By using the prior probabilities in the template, the supported probability and anti- supported probability of each text in the text library can be estimated for discrimination. The search- ing result can be ranked according to similarities between retrieved texts and the template. The com- plexity of DCTR is close to term frequency and mversed document frequency （TF-IDF）. Its distin- guishing ability to confusable texts could be advanced and the performance of the result would be im- proved with increasing of training times.

Asymmetric pixel confusion algorithm for images based on RSA and Arnold transform

We propose a new asymmetric pixel confusion algorithm for images based on the Rivest-Shamir-Adleman(RSA)public-key cryptosystem and Arnold map.First,the RSA asymmetric algorithm is used to generate two groups of Arnold transform parameters to address the problem of symmetrical distribution of Arnold map parameters.Second,the image is divided into blocks,and the first group of parameters is used to perform Arnold confusion on each sub-block.Then,the second group of parameters is used to perform Arnold confusion on the entire image.The image correlation is thereby fully weakened,and the image confusion degree and effect are further enhanced.The experimental results show that the proposed image pixel confusion algorithm has better confusion effect than the classical Arnold map based confusion and the row-column exchange based confusion.Specifically,the values of gray difference are close to one.In addition,the security of the new confusion operation is dependent on RSA,and it can act as one part of a confusion-substitution structure in a cipher.

Transparency order versus confusion coefficient:a case study of NIST lightweight cryptography S-Boxes

Side-channel resistance is nowadays widely accepted as a crucial factor in deciding the security assurance level of cryptographic implementations.In most cases,non-linear components(e.g.S-Boxes)of cryptographic algorithms will be chosen as primary targets of side-channel attacks(SCAs).In order to measure side-channel resistance of S-Boxes,three theoretical metrics are proposed and they are revisited transparency order(VTO),confusion coefficients variance(CCV),and minimum confusion coefficient(MCC),respectively.However,the practical effectiveness of these metrics remains still unclear.Taking the 4-bit and 8-bit S-Boxes used in NIST Lightweight Cryptography candidates as concrete examples,this paper takes a comprehensive study of the applicability of these metrics.First of all,we empirically investigate the relations among three metrics for targeted S-boxes,and find that CCV is almost linearly correlated with VTO,while MCC is inconsistent with the other two.Furthermore,in order to verify which metric is more effective in which scenarios,we perform simulated and practical experiments on nine 4-bit S-Boxes under the non-profiled attacks and profiled attacks,respectively.The experiments show that for quantifying side-channel resistance of S-Boxes under non-profiled attacks,VTO and CCV are more reliable while MCC fails.We also obtain an interesting observation that none of these three metrics is suitable for measuring the resistance of S-Boxes against profiled SCAs.Finally,we try to verify whether these metrics can be applied to compare the resistance of S-Boxes with different sizes.Unfortunately,all of them are invalid in this scenario.

Transparency order versus confusion coefficient:a case study of NIST lightweight cryptography S‑Boxes

Side-channel resistance is nowadays widely accepted as a crucial factor in deciding the security assurance level of cryptographic implementations.In most cases,non-linear components(e.g.S-Boxes)of cryptographic algorithms will be chosen as primary targets of side-channel attacks(SCAs).In order to measure side-channel resistance of S-Boxes,three theoretical metrics are proposed and they are reVisited transparency order(VTO),confusion coefficients variance(CCV),and minimum confusion coefficient(MCC),respectively.However,the practical effectiveness of these metrics remains still unclear.Taking the 4-bit and 8-bit S-Boxes used in NIST Lightweight Cryptography candidates as concrete examples,this paper takes a comprehensive study of the applicability of these metrics.First of all,we empirically investigate the relations among three metrics for targeted S-boxes,and find that CCV is almost linearly correlated with VTO,while MCC is inconsistent with the other two.Furthermore,in order to verify which metric is more effective in which scenarios,we perform simulated and practical experiments on nine 4-bit S-Boxes under the nonprofiled attacks and profiled attacks,respectively.The experiments show that for quantifying side-channel resistance of S-Boxes under non-profiled attacks,VTO and CCV are more reliable while MCC fails.We also obtain an interesting observation that none of these three metrics is suitable for measuring the resistance of S-Boxes against profiled SCAs.Finally,we try to verify whether these metrics can be applied to compare the resistance of S-Boxes with different sizes.Unfortunately,all of them are invalid in this scenario.

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.

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.

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.

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.

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.