Exploration and Application of a Blended English Teaching Mode in Military University Based on Production-Oriented Approach

Aiming at solving the problems of outdated mode and single method of English teaching for sergeant students,as well as the separation of learning and applying in traditional English classes,this paper proposes a blended teaching mode guided by the production-oriented approach,carries out feasibility analysis,designs teaching activities according to the output-driven hypothesis,and tests the effectiveness of this teaching mode in Sergeant English class through practice.It has proved that this teaching mode can effectively stimulate students’motivation and interest in learning English,improve their English output and enhance their learning confidence,and significantly improve the teaching effectiveness.

Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence

This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission.The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission.The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data.This process effectively enhances the concealment and imperceptibility of confidential information,thereby improving the security of such information during transmission and reducing security risks.Furthermore,we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical environments.Through adversarial training,the algorithm is strengthened to enhance its resilience against attacks and overall robustness,ensuring better protection against potential threats.Experimental results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding capacity.Additionally,it ensures the quality and fidelity of the stego image.The method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information.

A Model for Detecting Fake News by Integrating Domain-Specific Emotional and Semantic Features

With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific domains.In order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed.This method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature fusion.The algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news text.Senta-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the text.It then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that domain.Finally,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for classification.The experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.

Constructive Robust Steganography Algorithm Based on Style Transfer

Traditional information hiding techniques achieve information hiding by modifying carrier data,which can easily leave detectable traces that may be detected by steganalysis tools.Especially in image transmission,both geometric and non-geometric attacks can cause subtle changes in the pixels of the image during transmission.To overcome these challenges,we propose a constructive robust image steganography technique based on style transformation.Unlike traditional steganography,our algorithm does not involve any direct modifications to the carrier data.In this study,we constructed a mapping dictionary by setting the correspondence between binary codes and image categories and then used the mapping dictionary to map secret information to secret images.Through image semantic segmentation and style transfer techniques,we combined the style of secret images with the content of public images to generate stego images.This type of stego image can resist interference during public channel transmission,ensuring the secure transmission of information.At the receiving end,we input the stego image into a trained secret image reconstruction network,which can effectively reconstruct the original secret image and further recover the secret information through a mapping dictionary to ensure the security,accuracy,and efficient decoding of the information.The experimental results show that this constructive information hiding method based on style transfer improves the security of information hiding,enhances the robustness of the algorithm to various attacks,and ensures information security.

Generative Trapdoors for Public Key Cryptography Based on Automatic Entropy Optimization

Trapdoor is a key component of public key cryptography design which is the essential security foundation of modern cryptography.Normally,the traditional way in designing a trapdoor is to identify a computationally hard problem,such as the NPC problems.So the trapdoor in a public key encryption mechanism turns out to be a type of limited resource.In this paper,we generalize the methodology of adversarial learning model in artificial intelligence and introduce a novel way to conveniently obtain sub-optimal and computationally hard trapdoors based on the automatic information theoretic search technique.The basic routine is constructing a generative architecture to search and discover a probabilistic reversible generator which can correctly encoding and decoding any input messages.The architecture includes a trapdoor generator built on a variational autoencoder(VAE)responsible for searching the appropriate trapdoors satisfying a maximum of entropy,a random message generator yielding random noise,and a dynamic classifier taking the results of the two generator.The evaluation of our construction shows the architecture satisfying basic indistinguishability of outputs under chosen-plaintext attack model(CPA)and high efficiency in generating cheap trapdoors.

A Highly Effective DPA Attack Method Based on Genetic Algorithm

As one of the typical method for side channel attack,DPA has become a serious trouble for the security of encryption algorithm implementation.The potential capability of DPA attack induces researchers making a lot of efforts in this area,which significantly improved the attack efficiency of DPA.However,most of these efforts were made based on the hypothesis that the gathered power consumption data from the target device were stable and low noise.If large deviation happens in part of the power consumption data sample,the efficiency of DPA attack will be reduced rapidly.In this work,a highly efficient method for DPA attack is proposed with the inspiration of genetic algorithm.Based on the designed fitness function,power consumption data that is stable and less noisy will be selected and the noisy ones will be eliminated.In this way,not only improves the robustness and efficiency of DPA attack,but also reduces the number of samples needed.With experiments on block cipher algorithms of DES and SM4,10%and 12.5%of the number of power consumption curves have been reduced in average with the proposed DPAG algorithm compared to original DPA attack respectively.The high efficiency and correctness of the proposed algorithm and novel model are proved by experiments.

Anchor-free Siamese Network Based on Visual Tracking

The Visual tracking problem can usually be solved in two parts.The first part is to extract the feature of the target and get the candidate region.The second part is to realize the classification of the target and the regression of the bounding box.In recent years,Siameses network in visual tracking problem has always been a frontier research hotspot.In this work,it applies two branches namely search area and tracking template area for similar learning to track.Some related researches prove the feasibility of this network structure.According to the characteristics of two branch shared networks in Siamese network,we also propos a new fully convolutional Siamese network to solve the visual tracking problem.Based on the Siamese network structure,the network we designed adopt a new fusion module,which realizes the fusion of multiple feature layers at different depths.We also devise a better target state estimation criterion.The overall structure is simple,efficient and has wide applicability.We extensive experiments on challenging benchmarks including generic object tracking-10k(GOT-10K),online object tracking benckmark2015(OTB2015)and unmanned air vehicle123(UAV123),and comparisons with state-of-the-art trackers and the fusion module commonly used in the past,Finally,our network performed better under the same backbone,and achieved good tracking effect,which proved the effectiveness and universality of our designed network and feature fusion method.

Identity-based threshold proxy re-encryption scheme from lattices and its applications

Threshold proxy re-encryption(TPRE)can prevent collusion between a single proxy and a delegatee from converting arbitrary files against the wishes of the delegator through multiple proxies,and can also provide normal services even when certain proxy servers are paralyzed or damaged.A non-interactive identity-based TPRE(IB-TPRE)scheme over lattices is proposed which removes the public key certificates.To accomplish this scheme,Shamir’s secret sharing is employed twice,which not only effectively hides the delegator’s private key information,but also decentralizes the proxy power by splitting the re-encryption key.Robustness means that a combiner can detect a misbehaving proxy server that has sent an invalid transformed ciphertext share.This property is achieved by lattice-based fully homomorphic signatures.As a result,the whole scheme is thoroughly capable of resisting quantum attacks even when they are available.The security of the proposed scheme is based on the decisional learning with error hardness assumption in the standard model.Two typical application scenarios,including a file-sharing system based on a blockchain network and a robust key escrow system with threshold cryptography,are presented.

Secure Scheme for Locating Disease-Causing Genes Based on Multi-Key Homomorphic Encryption

Genes have great significance for the prevention and treatment of some diseases.A vital consideration is the need to find a way to locate pathogenic genes by analyzing the genetic data obtained from different medical institutions while protecting the privacy of patients’genetic data.In this paper,we present a secure scheme for locating disease-causing genes based on Multi-Key Homomorphic Encryption(MKHE),which reduces the risk of leaking genetic data.First,we combine MKHE with a frequency-based pathogenic gene location function.The medical institutions use MKHE to encrypt their genetic data.The cloud then homomorphically evaluates specific gene-locating circuits on the encrypted genetic data.Second,whereas most location circuits are designed only for locating monogenic diseases,we propose two location circuits(TH-intersection and Top-q)that can locate the disease-causing genes of polygenic diseases.Third,we construct a directed decryption protocol in which the users involved in the homomorphic evaluation can appoint a target user who can obtain the final decryption result.Our experimental results show that compared to the JWB+17 scheme published in the journal Science,our scheme can be used to diagnose polygenic diseases,and the participants only need to upload their encrypted genetic data once,which reduces the communication traffic by a few hundred-fold.

An autoencoder-based model for forest disturbance detection using Landsat time series data

Monitoring and classifying disturbed forests can provide information support for not only sustainable forest management but also global carbon sequestration assessments.In this study,we propose an autoencoder-based model for forest disturbance detection,which considers disturbances as anomalous events in forest temporal trajectories.The autoencoder network is established and trained to reconstruct intact forest trajectories.Then,the disturbance detection threshold is derived by Tukey’s method based on the reconstruction error of the intact forest trajectory.The assessment result shows that the model using the NBR time series performs better than the NDVIbased model,with an overall accuracy of 90.3%.The omission and commission errors of disturbed forest are 7%and 12%,respectively.Additionally,the trained NBR-based model is implemented in two test areas,with overall accuracies of 87.2%and 86.1%,indicating the robustness and scalability of the model.Moreover,comparing three common methods,the proposed model performs better,especially for intact forest detection accuracy.This study provides a novel and robust approach with acceptable accuracy for forest disturbance detection,enabling forest disturbance to be identified in regions with limited disturbance reference data.