Numerical study on the blocking effect of skin on Flash-Ball Impact and damage assessment

To explore the response law of non-lethal large-size kinetic energy projectiles to blunt attack on skin tissue,and to evaluate the skin injury characteristics of the attacked personnel and the use safety of kinetic energy projectiles.Based on the LS-DYNA simulation software,a three-layer skin simulation model and a Flash-Ball rubber bullet model are established,and the force-time and deformation-time biomechanical corridors of the Flash-Ball rubber bullet impacting human skin tissue are obtained.The corridor curve and the energy transfer and diffusion are analyzed and compared.The safety evaluation of the damage caused by the rubber bullet shooting a human body at different distances is carried out using the empirical formula of the penetration limit.Finally,the safe shooting distance is obtained.The results show that the model used in the simulation has a good correlation with the experimental data,its biomechanical corridor characteristics are different from those of conventional vehicle impact and smallsize projectile response characteristics.The energy transfer and action time of medium and low-speed impact may cause greater damage.The fat layer is the largest energy absorption unit.The minimum safe shooting distance to ensure skin tissue from penetrating damage is 15.8 m,and the limit specific kinetic energy of skin damage is 7.88 J/cm^(2).This study can be extended to the study of biomechanical response law and safety evaluation under the impact of the same type of large kinetic energy projectile,which provides an important theoretical reference for the police to use large kinetic energy projectiles to conduct safe shooting in peacekeeping operations.

Research and Design of Reconfigurable Matrix Multiplication over Finite Field in VLIW Processor

Matrix multiplication plays a pivotal role in the symmetric cipher algorithms, but it is one of the most complex and time consuming units, its performance directly affects the efficiency of cipher algorithms. Combined with the characteristics of VLIW processor and matrix multiplication of symmetric cipher algorithms, this paper extracted the reconfigurable elements and analyzed the principle of matrix multiplication, then designed the reconfigurable architecture of matrix multiplication of VLIW processor further, at last we put forward single instructions for matrix multiplication between 4×1 and 4×4 matrix or two 4×4 matrix over GF(2~8), through the instructions extension, the instructions could support larger dimension operations. The experiment shows that the instructions we designed supports different dimensions matrix multiplication and improves the processing speed of multiplication greatly.

Anti-quantum Cryptography Scheme Based on the Improvement of Cubic Simple Matrix and LRPC Code

Coding cryptography can resist quantum computing attacks with high efficiency. It is similar to multivariate public key cryptography when constructing core mapping. Data compression is an advantage of coding cryptography. Therefore, combining the coding cryptography with the core mapping of multivariate public key cryptography to enhance the security of multivariate public key cryptography is a good choice. This paper first improved the Cubic Simple Matrix scheme in multivariate cryptography, and then combined the improved version scheme with the low rank parity check (LRPC) code to construct a new scheme. Compared with the Cubic Simple Matrix scheme, the ciphertext expansion rate is reduced by 50%, and the security of the scheme has been improved. The new solution is based on the improved version of the Cubic Simple Matrix, which reduces the dimensional constraints on the code when selecting LRPC codes.

Hierarchical Access Control Scheme of Private Data Based on Attribute Encryption

To solve the problems of data sharing in social network,such as management of private data is too loose,access permissions are not clear,mode of data sharing is too single and soon on,we design a hierarchical access control scheme of private data based on attribute encryption.First,we construct a new algorithm based on attribute encryption,which divides encryption into two phases,and we can design two types of attributes encryption strategy to make sure that different users could get their own decryption keys corresponding to their permissions.We encrypt the private data hierarchically with our algorithm to realize“precise”,“more accurate”,“fuzzy”and“private”four management modes,then users with higher permissions can access the private data inferior to their permissions.And we outsource some complex operations of decryption to DSP to ensure high efficiency on the premise of privacy protection.Finally,we analyze the efficiency and the security of our scheme.

Action Recognition and Detection Based on Deep Learning: A Comprehensive Summary

Action recognition and detection is an important research topic in computer vision,which can be divided into action recognition and action detection.At present,the distinction between action recognition and action detection is not clear,and the relevant reviews are not comprehensive.Thus,this paper summarized the action recognition and detection methods and datasets based on deep learning to accurately present the research status in this field.Firstly,according to the way that temporal and spatial features are extracted from the model,the commonly used models of action recognition are divided into the two stream models,the temporal models,the spatiotemporal models and the transformer models according to the architecture.And this paper briefly analyzes the characteristics of the four models and introduces the accuracy of various algorithms in common data sets.Then,from the perspective of tasks to be completed,action detection is further divided into temporal action detection and spatiotemporal action detection,and commonly used datasets are introduced.From the perspectives of the twostage method and one-stage method,various algorithms of temporal action detection are reviewed,and the various algorithms of spatiotemporal action detection are summarized in detail.Finally,the relationship between different parts of action recognition and detection is discussed,the difficulties faced by the current research are summarized in detail,and future development was prospected。

SFSDA:Secure and Flexible Subset Data Aggregation with Fault Tolerance for Smart Grid

Smart grid(SG)brings convenience to users while facing great chal-lenges in protecting personal private data.Data aggregation plays a key role in protecting personal privacy by aggregating all personal data into a single value,preventing the leakage of personal data while ensuring its availability.Recently,a flexible subset data aggregation(FSDA)scheme based on the Pail-lier homomorphic encryption was first proposed by Zhang et al.Their scheme can dynamically adjust the size of each subset and obtain the aggregated data in the corresponding subset.In this paper,firstly,an efficient attack with both theorems proving and experimentative verification is launched.We find that in a specific scenario where the encrypted data constructed by a smart meter(SM)exceeds the size of one Paillier ciphertext,the malicious fog node(FN)may use the received ciphertext to obtain the reading of the SM.Secondly,to avoid the possibility of privacy disclosure under certain circumstances,additional hash functions are added to the individual encryption process.In addition,fault tolerance is very important to aggregation schemes in practical scenarios.In most of the current schemes,once some SMs failed,then they will not work.As far as we know,there is no multi-subset aggregation scheme both supports flexible subset data aggregation and fault tolerance.Finally,we construct the first secure flexible subset data aggregation(SFSDA)scheme with fault tolerance by combining the fault tolerance method with the flexible multi-subset aggregation,where FN enables the control server(CS)to finally decrypt the aggregated ciphertext by recovering equivalent ciphertexts when some SMs fail to submit their ciphertexts.Experiments show that our SFSDA scheme keeps the efficiency in implementing a flexible multi-subset aggregation function,and only has a small delay in implementing fault-tolerant data aggregation.

A Robust Gaussian Mixture Model for Mobile Robots’ Vision-based Pose Estimation

In dynamic environments, the moving landmarks can make the accuracy of traditional vision-based pose estimation worse or even failure. To solve this problem, a robust Gaussian mixture model for vision-based pose estimation is proposed. The motion index is added to the traditional graph-based vision-based pose estimation model to describe landmarks’ moving probability, transforming the classic Gaussian model to Gaussian mixture model, which can reduce the influence of moving landmarks for optimization results. To improve the algorithm’s robustness to noise, the covariance inflation model is employed in residual equations. The expectation maximization method for solving the Gaussian mixture problem is derived in detail, transforming the problem into classic iterative least square problem. Experimental results demonstrate that in dynamic environments, the proposed method outperforms the traditional method both in absolute accuracy and relative accuracy, while maintains high accuracy in static environments. The proposed method can effectively reduce the influence of the moving landmarks in dynamic environments, which is more suitable for the autonomous localization of mobile robots.

APPLSS:Adaptive Privacy Preserved Location Sharing Scheme Based on Attribute-Based Encryption

Unauthorized access to location information in location-based service is one of the most critical security threats to mobile Internet.In order to solve the problem of quality of location sharing while keeping privacy preserved,adaptive privacy preserved location sharing scheme called APPLSS is proposed,which is based on a new hierarchical ciphertext-policy attribute-based encryption algorithm.In the algorithm,attribute authority sets the attribute vector according to the attribute tags of registration from the location service providers.Then the attribute vector can be adaptively transformed into an access structure to control the encryption and decryption.The APPLSS offers a natural hierarchical mechanism in protecting location information when partially sharing it in mobile networks.It allows service providers access to end user’s sensitive location more flexibly,and satisfies a sufficient-but-no-more strategy.For end-users,the quality of service is obtained while no extra location privacy is leaked.To improve service response performance,outsourced decryption is deployed to avoid the bottlenecks of the service providers and location information providers.The performance analysis and experiments show that APPLSS is an efficient and practical location sharing scheme.

A MV-Based Steganographic Algorithm for H.264/AVC without Distortion

H.264/AVC video is one of the most popular multimedia and has been widely used as the carriers of video steganography.In this paper,a novel motion vector(MV)based steganographic algorithm is proposed for the H.264/AVC compressed video without distortion.Four modules are introduced to eliminate the distortion caused by the modifications of motion vectors and guarantee the security of the algorithm.In the embedding block,the motion vector space encoding is used to embed a(2n+1)-ary notational number into an n-dimension vector composed of motion vectors generated from the selection block.Scrambling is adopted to disturb the order of steganographic carriers to improve the randomness of the carrier before the operation of embedding.The re-motion compensation(re-MC)block will re-construct the macroblock(MB)whose motion vectors have been modified by embedding block.System block plays the role of the generator for chaotic sequences and encryptor for secret data.Experimental results demonstrate that our proposed algorithm can achieve high embedding capacity without stego video visual quality distortion,it also presents good undetectability for existing MV-based steganalysis feature.Performance comparisons with other existing algorithms are provided to demonstrate the superiority of the proposed algorithm.

Deep Convolutional Feature Fusion Model for Multispectral Maritime Imagery Ship Recognition

Combining both visible and infrared object information, multispectral data is a promising source data for automatic maritime ship recognition. In this paper, in order to take advantage of deep convolutional neural network and multispectral data, we model multispectral ship recognition task into a convolutional feature fusion problem, and propose a feature fusion architecture called Hybrid Fusion. We fine-tune the VGG-16 model pre-trained on ImageNet through three channels single spectral image and four channels multispectral images, and use existing regularization techniques to avoid over-fitting problem. Hybrid Fusion as well as the other three feature fusion architectures is investigated. Each fusion architecture consists of visible image and infrared image feature extraction branches, in which the pre-trained and fine-tuned VGG-16 models are taken as feature extractor. In each fusion architecture, image features of two branches are firstly extracted from the same layer or different layers of VGG-16 model. Subsequently, the features extracted from the two branches are flattened and concatenated to produce a multispectral feature vector, which is finally fed into a classifier to achieve ship recognition task. Furthermore, based on these fusion architectures, we also evaluate recognition performance of a feature vector normalization method and three combinations of feature extractors. Experimental results on the visible and infrared ship (VAIS) dataset show that the best Hybrid Fusion achieves 89.6% mean per-class recognition accuracy on daytime paired images and 64.9% on nighttime infrared images, and outperforms the state-of-the-art method by 1.4% and 3.9%, respectively.

Dataflow Management in the Internet of Things: Sensing,Control, and Security

The pervasiveness of the smart Internet of Things(IoTs) enables many electric sensors and devices to be connected and generates a large amount of dataflow. Compared with traditional big data, the streaming dataflow is faced with representative challenges, such as high speed, strong variability, rough continuity, and demanding timeliness, which pose severe tests of its efficient management. In this paper, we provide an overall review of IoT dataflow management. We first analyze the key challenges faced with IoT dataflow and initially overview the related techniques in dataflow management, spanning dataflow sensing, mining, control, security, privacy protection,etc. Then, we illustrate and compare representative tools or platforms for IoT dataflow management. In addition,promising application scenarios, such as smart cities, smart transportation, and smart manufacturing, are elaborated,which will provide significant guidance for further research. The management of IoT dataflow is also an important area, which merits in-depth discussions and further study.

Review of Dynamic Task Allocation Methods for UAV Swarms Oriented to Ground Targets

Dynamic task allocation of unmanned aerial vehicle swarms for ground targets is an important part of unmanned aerial vehicle(UAV)swarms task planning and the key technology to improve autonomy.The realization of dynamic task allocation in UAV swarms for ground targets is very difficult because of the large uncertainty of swarms,the target and environment state,and the high real-time allocation requirements.Hence,dynamic task allocation of UAV swarms oriented to ground targets has become a key and difficult problem in the field of mission planning.In this work,a dynamic task allocation method for UAV swarms oriented to ground targets is comprehensively and systematically summarized from two aspects:the establishment of an allocation model and the solution of the allocation model.First,the basic concept and trigger scenario are introduced.Second,the research status and the advantages and disadvantages of the two allocation models are analyzed.Third,the research status and the advantages and disadvantages of several common dynamic task allocation algorithms,such as the algorithm based on market mechanisms,intelligent optimization algorithm,and clustering algorithm,are evaluated.Finally,the specific problems of the current UAV swarm dynamic task allocation method for ground targets are highlighted,and future research directions are established.This work offers important reference significance for fully understanding the current situation of UAV swarm dynamic task allocation technology.

Optimized Relinearization Algorithm of the Multikey Homomorphic Encryption Scheme

Multikey homomorphic encryption(MKHE) supports arbitrary homomorphic evaluation on the ciphertext of different users and thus can be applied to scenarios involving multiusers(e.g., cloud computing and artificial intelligence) to protect user privacy. CDKS19 is the current most efficient MKHE scheme, and its relinearization process consumes most of the time of homomorphic evaluation. In this study, an optimized relinearization algorithm of CDKS19 is proposed. This algorithm reorganizes the evaluation key during the key generation process, decreases the complexity of relinearization, and reduces the error growth rate during homomorphic evaluation. First, we reduce the scale of the evaluation key by increasing its modulus instead of using a gadget vector to decompose the user’s public key and extend the ciphertext of homomorphic multiplication. Second, we use rescaling technology to optimize the relinearization algorithm;thus, the error bound of the ciphertext is reduced, and the homomorphic operation efficiency is improved. Lastly, the average-case error estimation on the variances of polynomial coefficients and the upper bound of the canonical embedding map are provided. Results show that our scheme reduces the scale of the evaluation key, the error variance, and the computational cost of the relinearization process. Our scheme can effectively perform the homomorphic multiplication of ciphertexts.

Enhancement of phase conjugation degenerate four-wave mixing using a Bessel beam

We report on the enhancement of phase conjugation degenerate four-wave mixing(DFWM) in hot atomic Rb vapor by using a Bessel beam as the probe beam. The Bessel beam was generated using cross-phase modulation based on the thermal nonlinear optical effect. Our results demonstrated that the DFWM signal generated by the Bessel beam is about twice as large as that generated by the Gaussian beam, which can be attributed to the extended depth and tight focusing features of the Bessel beam. We also found that a DFWM signal with reasonable intensity can be detected even when the Bessel beam encounters an obstruction on its way, thanks to the selfhealing property of the Bessel beam. This work not only indicates that DFWM using a Bessel beam would be of great potential in the fields of high-fidelity communication, adaptive optics, and so on, but also suggests that a Bessel beam would be of significance to enhance the nonlinear process, especially in thick and scattering media.

Application of Polar Code-Based Scheme in Cloud Secure Storage

In view of the fact that the quantum computer attack is not considered in the cloud storage environment,this paper selects the code-based public key encryption scheme as the security protection measure in the cloud storage.Based on random linear code encryption scheme,it employs the structure of the RLCE scheme and Polar code polarization properties,using the Polar code as underlying encoding scheme,through the method of RLCEspad,putting forward a kind of improved public key encryption scheme which considers semantic security and is resistant to adaptively chosen ciphertext attacks.The improved scheme is applied to cloud storage to ensure that the storage environment will not be attacked by quantum computer while ensuring the confidentiality,availability and reliability.

High capacity reversible data hiding in encrypted images based on adaptive quadtree partitioning and MSB prediction

To improve the embedding capacity of reversible data hiding in encrypted images(RDH-EI),a new RDH-EI scheme is proposed based on adaptive quadtree partitioning and most significant bit(MSB)prediction.First,according to the smoothness of the image,the image is partitioned into blocks based on adaptive quadtree partitioning,and then blocks of different sizes are encrypted and scrambled at the block level to resist the analysis of the encrypted images.In the data embedding stage,the adaptive MSB prediction method proposed by Wang and He(2022)is improved by taking the upper-left pixel in the block as the target pixel,to predict other pixels to free up more embedding space.To the best of our knowledge,quadtree partitioning is first applied to RDH-EI.Simulation results show that the proposed method is reversible and separable,and that its average embedding capacity is improved.For gray images with a size of 512×512,the average embedding capacity is increased by 25565 bits.For all smooth images with improved embedding capacity,the average embedding capacity is increased by about 35530 bits.

The differential fault analysis on block cipher FeW

Feather weight(FeW)cipher is a lightweight block cipher proposed by Kumar et al.in 2019,which takes 64 bits plaintext as input and produces 64 bits ciphertext.As Kumar et al.said,FeW is a software oriented design with the aim of achieving high efficiency in software based environments.It seems that FeW is immune to many cryptographic attacks,like linear,impossible differential,differential and zero correlation attacks.However,in recent work,Xie et al.reassessed the security of FeW.More precisely,they proved that under the differential fault analysis(DFA)on the encryption states,an attacker can completely recover the master secret key.In this paper,we revisit the block cipher FeW and consider the DFA on its key schedule algorithm,which is rather popular cryptanalysis for kinds of block ciphers.In particular,by respectively injected faults into the 30th and 29th round subkeys,one can recover about 55/80~69%bits of master key.Then the brute force searching remaining bits,one can obtain the full master secret key.The simulations and experiment results show that our analysis is practical.

A Generative Method for Steganography by Cover Synthesis with Auxiliary Semantics

Traditional steganography is the practice of embedding a secret message into an image by modifying the information in the spatial or frequency domain of the cover image.Although this method has a large embedding capacity,it inevitably leaves traces of rewriting that can eventually be discovered by the enemy.The method of Steganography by Cover Synthesis(SCS)attempts to construct a natural stego image,so that the cover image is not modified;thus,it can overcome detection by a steganographic analyzer.Due to the difficulty in constructing natural stego images,the development of SCS is limited.In this paper,a novel generative SCS method based on a Generative Adversarial Network(GAN)for image steganography is proposed.In our method,we design a GAN model called Synthetic Semantics Stego Generative Adversarial Network(SSS-GAN)to generate stego images from secret messages.By establishing a mapping relationship between secret messages and semantic category information,category labels can generate pseudo-real images via the generative model.Then,the receiver can recognize the labels via the classifier network to restore the concealed information in communications.We trained the model on the MINIST,CIFAR-10,and CIFAR-100 image datasets.Experiments show the feasibility of this method.The security,capacity,and robustness of the method are analyzed.

Dynamic multicast sharing protection algorithm based on fuzzy game in multi-domain optical network

Aiming at the problem of dynamic multicast service protection in multi-domain optical network, this paper proposes a dynamic multicast sharing protection algorithm based on fuzzy game in multi-domain optical network. The algorithm uses the minimum cost spanning tree strategy and fuzzy game theory. First, it virtualizes two planes to calculate the multicast tree and the multicast protection tree respectively. Then, it performs a fuzzy game to form a cooperative alliance to optimize the path composition of each multicast tree. Finally, it generates a pair of optimal multicast work tree and multicast protection tree for dynamic multicast services. The time complexity of the algorithm is O(k3 m2 n), where n represents the number of nodes in the networks, k represents the number of dynamic multicast requests, and m represents the number of destination nodes for each multicast request. The experimental results show that the proposed algorithm reduces significantly the blocking rate of dynamic multicast services, and improves the utilization of optical network resources within a certain number of dynamic multicast request ranges.

An Improved LeNet-5 Model Based on Encrypted Data

In recent years,the problem of privacy leakage has attracted increasing attentions.Therefore,machine learning privacy protection becomes crucial research topic.In this paper,the Paillier homomorphic encryption algorithm is proposed to protect the privacy data.The original LeNet-5 convolutional neural network model was first improved.Then the activation function was modified and the C5 layer was removed to reduce the number of model parameters and improve the operation efficiency.Finally,by mapping the operation of each layer in the convolutional neural network from the plaintext domain to the ciphertext domain,an improved LeNet-5 model that can run on encrypted data was constructed.The purpose of using machine learning algorithmwas realized and privacywas ensured at the same time.The analysis shows that the model is feasible and the efficiency is improved.