Contrast and Comparison Between the Old and New Bar Code for Commodity Management Measures

With the development of socialism market economy, the former Bar Code ForCommodity Management Measures (being called Old Measures for short hereafter) issued by the NationalBureau of Quality and Technical Supervision can not adapt to the requirement of managing for barcode for commodity. Therefore, for the purpose of standardize bar code for commodity managementstandard, ensuring its quality, speeding up the application of it in electronic business andcirculating field and accelerating the course of being a well informed country, a new Bar Code ForCommodity Management Measures (New Measures for short) has been issued by the General Administratorof Quality Supervision, Inspection and Quarantine of P.R. China (AQSIQ) on May 30th, 2005, and putinto force in October, 2005. At the same time, the Old Measures issued on July 3rd, 1998 has beenabolished.

Solid Warehouse Material Management System Based on ERP and Bar Code Technology

This paper presents a manufacturing mat er ial management system based on ERP, which is combined with industrial bar code i nformation collection and material management, and carries out extensive researc h on the system structure and function model, as well as a detailed application scheme.

APPLICATION OF BAR CODE AND NOVELL NETWORK IN AUTOMATIC ANALYSIS OF CLINICAL CHEMISTRY

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An Efficient Bar Code Image Recognition Algorithm for Sorting System

In the sorting system of the production line,the object movement,fixed angle of view,light intensity and other reasons lead to obscure blurred images.It results in bar code recognition rate being low and real time being poor.Aiming at the above problems,a progressive bar code compressed recognition algorithm is proposed.First,assuming that the source image is not tilted,use the direct recognition method to quickly identify the compressed source image.Failure indicates that the compression ratio is improper or the image is skewed.Then,the source image is enhanced to identify the source image directly.Finally,the inclination of the compressed image is detected by the barcode region recognition method and the source image is corrected to locate the barcode information in the barcode region recognition image.The results of multitype image experiments show that the proposed method is improved by 5+times computational efficiency compared with the former methods,and can recognize fuzzy images better.

Lightweight Malicious Code Classification Method Based on Improved Squeeze Net

With the growth of the Internet,more and more business is being done online,for example,online offices,online education and so on.While this makes people’s lives more convenient,it also increases the risk of the network being attacked by malicious code.Therefore,it is important to identify malicious codes on computer systems efficiently.However,most of the existing malicious code detection methods have two problems:(1)The ability of the model to extract features is weak,resulting in poor model performance.(2)The large scale of model data leads to difficulties deploying on devices with limited resources.Therefore,this paper proposes a lightweight malicious code identification model Lightweight Malicious Code Classification Method Based on Improved SqueezeNet(LCMISNet).In this paper,the MFire lightweight feature extraction module is constructed by proposing a feature slicing module and a multi-size depthwise separable convolution module.The feature slicing module reduces the number of parameters by grouping features.The multi-size depthwise separable convolution module reduces the number of parameters and enhances the feature extraction capability by replacing the standard convolution with depthwise separable convolution with different convolution kernel sizes.In addition,this paper also proposes a feature splicing module to connect the MFire lightweight feature extraction module based on the feature reuse and constructs the lightweight model LCMISNet.The malicious code recognition accuracy of LCMISNet on the BIG 2015 dataset and the Malimg dataset reaches 98.90% and 99.58%,respectively.It proves that LCMISNet has a powerful malicious code recognition performance.In addition,compared with other network models,LCMISNet has better performance,and a lower number of parameters and computations.

Design and implementation of code acquisition using sparse Fourier transform

Code acquisition is the kernel operation for signal synchronization in the spread-spectrum receiver.To reduce the computational complexity and latency of code acquisition,this paper proposes an efficient scheme employing sparse Fourier transform(SFT)and the relevant hardware architecture for field programmable gate array(FPGA)and application-specific integrated circuit(ASIC)implementation.Efforts are made at both the algorithmic level and the implementation level to enable merged searching of code phase and Doppler frequency without incurring massive hardware expenditure.Compared with the existing code acquisition approaches,it is shown from theoretical analysis and experimental results that the proposed design can shorten processing latency and reduce hardware complexity without degrading the acquisition probability.

An Encode-and CRT-Based Scalability Scheme for Optimizing Transmission in Blockchain

Blockchain technology has witnessed a burgeoning integration into diverse realms of economic and societal development.Nevertheless,scalability challenges,characterized by diminished broadcast efficiency,heightened communication overhead,and escalated storage costs,have significantly constrained the broad-scale application of blockchain.This paper introduces a novel Encode-and CRT-based Scalability Scheme(ECSS),meticulously refined to enhance both block broadcasting and storage.Primarily,ECSS categorizes nodes into distinct domains,thereby reducing the network diameter and augmenting transmission efficiency.Secondly,ECSS streamlines block transmission through a compact block protocol and robust RS coding,which not only reduces the size of broadcasted blocks but also ensures transmission reliability.Finally,ECSS utilizes the Chinese remainder theorem,designating the block body as the compression target and mapping it to multiple modules to achieve efficient storage,thereby alleviating the storage burdens on nodes.To evaluate ECSS’s performance,we established an experimental platformand conducted comprehensive assessments.Empirical results demonstrate that ECSS attains superior network scalability and stability,reducing communication overhead by an impressive 72% and total storage costs by a substantial 63.6%.

Layered Coded Cache Placement and Cooperative Delivery with Sharing Links in Satellite-Terrestrial Integrated Networks

Cooperative utilization of multidimensional resources including cache, power and spectrum in satellite-terrestrial integrated networks(STINs) can provide a feasible approach for massive streaming media content delivery over the seamless global coverage area. However, the on-board supportable resources of a single satellite are extremely limited and lack of interaction with others. In this paper, we design a network model with two-layered cache deployment, i.e., satellite layer and ground base station layer, and two types of sharing links, i.e., terrestrial-satellite sharing(TSS) links and inter-satellite sharing(ISS) links, to enhance the capability of cooperative delivery over STINs. Thus, we use rateless codes for the content divided-packet transmission, and derive the total energy efficiency(EE) in the whole transmission procedure, which is defined as the ratio of traffic offloading and energy consumption. We formulate two optimization problems about maximizing EE in different sharing scenarios(only TSS and TSS-ISS),and propose two optimized algorithms to obtain the optimal content placement matrixes, respectively.Simulation results demonstrate that, enabling sharing links with optimized cache placement have more than 2 times improvement of EE performance than other traditional placement schemes. Particularly, TSS-ISS schemes have the higher EE performance than only TSS schemes under the conditions of enough number of satellites and smaller inter-satellite distances.

Reconfigurable mechanism-based metamaterials for ternary-coded elastic wave polarizers and programmable refraction control

Elastic metamaterials with unusual elastic properties offer unprecedented ways to modulate the polarization and propagation of elastic waves.However,most of them rely on the resonant structural components,and thus are frequency-dependent and unchangeable.Here,we present a reconfigurable 2D mechanism-based metamaterial which possesses transformable and frequency-independent elastic properties.Based on the proposed mechanism-based metamaterial,interesting functionalities,such as ternarycoded elastic wave polarizer and programmable refraction,are demonstrated.Particularly,unique ternary-coded polarizers,with 1-trit polarization filtering and 2-trit polarization separating of longitudinal and transverse waves,are first achieved.Then,the strong anisotropy of the proposed metamaterial is harnessed to realize positive-negative bi-refraction,only-positive refraction,and only-negative refraction.Finally,the wave functions with detailed microstructures are numerically verified.

A New Malicious Code Classification Method for the Security of Financial Software

The field of finance heavily relies on cybersecurity to safeguard its systems and clients from harmful software.The identification of malevolent code within financial software is vital for protecting both the financial system and individual clients.Nevertheless,present detection models encounter limitations in their ability to identify malevolent code and its variations,all while encompassing a multitude of parameters.To overcome these obsta-cles,we introduce a lean model for classifying families of malevolent code,formulated on Ghost-DenseNet-SE.This model integrates the Ghost module,DenseNet,and the squeeze-and-excitation(SE)channel domain attention mechanism.It substitutes the standard convolutional layer in DenseNet with the Ghost module,thereby diminishing the model’s size and augmenting recognition speed.Additionally,the channel domain attention mechanism assigns distinctive weights to feature channels,facilitating the extraction of pivotal characteristics of malevolent code and bolstering detection precision.Experimental outcomes on the Malimg dataset indicate that the model attained an accuracy of 99.14%in discerning families of malevolent code,surpassing AlexNet(97.8%)and The visual geometry group network(VGGNet)(96.16%).The proposed model exhibits reduced parameters,leading to decreased model complexity alongside enhanced classification accuracy,rendering it a valuable asset for categorizing malevolent code.

Enhancing Security in QR Code Technology Using AI: Exploration and Mitigation Strategies

The widespread adoption of QR codes has revolutionized various industries, streamlined transactions and improved inventory management. However, this increased reliance on QR code technology also exposes it to potential security risks that malicious actors can exploit. QR code Phishing, or “Quishing”, is a type of phishing attack that leverages QR codes to deceive individuals into visiting malicious websites or downloading harmful software. These attacks can be particularly effective due to the growing popularity and trust in QR codes. This paper examines the importance of enhancing the security of QR codes through the utilization of artificial intelligence (AI). The abstract investigates the integration of AI methods for identifying and mitigating security threats associated with QR code usage. By assessing the current state of QR code security and evaluating the effectiveness of AI-driven solutions, this research aims to propose comprehensive strategies for strengthening QR code technology’s resilience. The study contributes to discussions on secure data encoding and retrieval, providing valuable insights into the evolving synergy between QR codes and AI for the advancement of secure digital communication.

Several Classes of Two-weight or Three-weight Linear Codes and Their Applications

Recently,linear codes with a few weights have been extensively studied due to their applications in secret sharing schemes,constant composition codes,strongly regular graphs and so on.In this paper,based on the Weil sums,several classes of two-weight or three-weight linear codes are presented by choosing a proper defining set,and their weight enumerators and complete weight enumerators are determined.Furthermore,these codes are proven to be minimal.By puncturing these linear codes,two classes of two-weight projective codes are obtained,and the parameters of the corresponding strongly regular graph are given.This paper generalizes the results of[7].

适用2000 HSC CODE的高速船特点及总体设计要求

高速船由于其航速高、质量轻、营运特殊等特点,需要适用的规则与常规船型不同。2000年的《国际高速船安全规则》(简称2000 HSC CODE)作为较完备的适用于高速船的规则,被各海事主管机关广泛接受作为高速船的设计依据。文中简要介绍2000 HSC CODE的安全原理,以及该规则对各海事主管机关的适用程度;然后通过国内外高速船事故来分析该类船的事故类型及致因特点,并进一步介绍了适用高速船的风险控制及设计措施,包括营运限制、破舱稳性、防火要求和撤离分析等总体设计要求,以供理解高速船的安全原理并指导其设计。

Design of Protograph LDPC-Coded MIMO-VLC Systems with Generalized Spatial Modulation

This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain(SiD) symbols and the spatial-domain(SpD) light emitting diode(LED)-index patterns are coded by a protograph low-density parity-check(P-LDPC) code. Specifically, we propose a signal-domain symbol expanding and re-allocating(SSER) method for constructing a type of novel generalized spatial modulation(GSM) constellations, referred to as SSERGSM constellations, so as to boost the performance of the BICGSM-ID MIMO-VLC systems.Moreover, by applying a modified PEXIT(MPEXIT) algorithm, we further design a family of rate-compatible P-LDPC codes, referred to as enhanced accumulate-repeat-accumulate(EARA) codes,which possess both excellent decoding thresholds and linear-minimum-distance-growth property. Both analysis and simulation results illustrate that the proposed SSERGSM constellations and P-LDPC codes can remarkably improve the convergence and decoding performance of MIMO-VLC systems. Therefore, the proposed P-LDPC-coded SSERGSM-mapped BICGSMID configuration is envisioned as a promising transmission solution to satisfy the high-throughput requirement of MIMO-VLC applications.

Efficient unequal error protection for online fountain codes

In this paper,an efficient unequal error protection(UEP)scheme for online fountain codes is proposed.In the buildup phase,the traversing-selection strategy is proposed to select the most important symbols(MIS).Then,in the completion phase,the weighted-selection strategy is applied to provide low overhead.The performance of the proposed scheme is analyzed and compared with the existing UEP online fountain scheme.Simulation results show that in terms of MIS and the least important symbols(LIS),when the bit error ratio is 10-4,the proposed scheme can achieve 85%and 31.58%overhead reduction,respectively.

CodeScore-R:用于评估代码合成功能准确性的自动化鲁棒指标

评估指标在代码合成领域中至关重要.常用的代码评估指标可以分为3种类型:基于匹配、基于语义和基于执行.其中,基于执行的Pass@k指标通过执行测试用例,能够准确判断预测代码的功能准确性.然而,该指标的计算需要大量开销,因此亟需设计一种自动化评估指标,在无需测试用例时仍可评估预测代码的功能准确性.此外,好的评估指标应当具有鲁棒性,即预测代码发生微小改变时,评估指标仍能保持其准确性.为此,提出了一种基于UniXcoder和对比学习的自动化鲁棒指标CodeScore-R,用于评估代码合成的功能准确性. CodeScore-R采用草图化处理、语法等价转换和变异测试等技术手段,有效减轻了标识符、语法结构和运算符对评估结果的干扰.实验结果表明,在Java和Python语言上的代码生成和迁移任务中,CodeScore-R的表现优于其他无需测试用例的评估指标,且更接近Pass@k指标,并具有更强的鲁棒性.

Physical Layer Encryption of OFDM-PON Based on Quantum Noise Stream Cipher with Polar Code

Orthogonal frequency division multiplexing passive optical network(OFDM-PON) has superior anti-dispersion property to operate in the C-band of fiber for increased optical power budget. However,the downlink broadcast exposes the physical layer vulnerable to the threat of illegal eavesdropping. Quantum noise stream cipher(QNSC) is a classic physical layer encryption method and well compatible with the OFDM-PON. Meanwhile, it is indispensable to exploit forward error correction(FEC) to control errors in data transmission. However, when QNSC and FEC are jointly coded, the redundant information becomes heavier and thus the code rate of the transmitted signal will be largely reduced. In this work, we propose a physical layer encryption scheme based on polar-code-assisted QNSC. In order to improve the code rate and security of the transmitted signal, we exploit chaotic sequences to yield the redundant bits and utilize the redundant information of the polar code to generate the higher-order encrypted signal in the QNSC scheme with the operation of the interleaver.We experimentally demonstrate the encrypted 16/64-QAM, 16/256-QAM, 16/1024-QAM, 16/4096-QAM QNSC signals transmitted over 30-km standard single mode fiber. For the transmitted 16/4096-QAM QNSC signal, compared with the conventional QNSC method, the proposed method increases the code rate from 0.1 to 0.32 with enhanced security.

Improved Belief Propagation Decoder for LDPC-CRC-Polar Codes with Bit-Freezing

Though belief propagation bit-flip(BPBF)decoding improves the error correction performance of polar codes,it uses the exhaustive flips method to achieve the error correction performance of CA-SCL decoding,thus resulting in high decoding complexity and latency.To alleviate this issue,we incorporate the LDPC-CRC-Polar coding scheme with BPBF and propose an improved belief propagation decoder for LDPC-CRC-Polar codes with bit-freezing(LDPCCRC-Polar codes BPBFz).The proposed LDPCCRC-Polar codes BPBFz employs the LDPC code to ensure the reliability of the flipping set,i.e.,critical set(CS),and dynamically update it.The modified CS is further utilized for the identification of error-prone bits.The proposed LDPC-CRC-Polar codes BPBFz obtains remarkable error correction performance and is comparable to that of the CA-SCL(L=16)decoder under medium-to-high signal-to-noise ratio(SNR)regions.It gains up to 1.2dB and 0.9dB at a fixed BLER=10-4compared with BP and BPBF(CS-1),respectively.In addition,the proposed LDPC-CRC-Polar codes BPBFz has lower decoding latency compared with CA-SCL and BPBF,i.e.,it is 15 times faster than CA-SCL(L=16)at high SNR regions.

Doped low-density parity-check codes

In this paper,we propose a doping approach to lower the error floor of Low-Density Parity-Check(LDPC)codes.The doping component is a short block code in which the information bits are selected from the coded bits of the dominant trapping sets of the LDPC code.Accordingly,an algorithm for selecting the information bits of the short code is proposed,and a specific two-stage decoding algorithm is presented.Simulation results demonstrate that the proposed doped LDPC code achieves up to 2.0 dB gain compared with the original LDPC code at a frame error rate of 10^(-6)Furthermore,the proposed design can lower the error floor of original LDPC Codes.

A GAN-EfficientNet-Based Traceability Method for Malicious Code Variant Families

Due to the diversity and unpredictability of changes in malicious code,studying the traceability of variant families remains challenging.In this paper,we propose a GAN-EfficientNetV2-based method for tracing families of malicious code variants.This method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code images.The method includes a lightweight classifier and a simulator.The classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile,embedded,and other devices.The simulator utilizes an enhanced generative adversarial network to simulate different variants of malicious code and generates datasets to validate the model’s performance.This process helps identify model vulnerabilities and security risks,facilitating model enhancement and development.The classifier achieves 98.61%and 97.59%accuracy on the MMCC dataset and Malevis dataset,respectively.The simulator’s generated image of malicious code variants has an FID value of 155.44 and an IS value of 1.72±0.42.The classifier’s accuracy for tracing the family of malicious code variants is as high as 90.29%,surpassing that of mainstream neural network models.This meets the current demand for high generalization and anti-obfuscation abilities in malicious code classification models due to the rapid evolution of malicious code.