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Combined with the real-time and robust requirements of paper detection in the common paper-pen interaction, a fast paper detection method based on edge detection is proposed. In the edge detection stage, a fast paper edge detection method based on cross-layer feature fusion is advanced. The linear bottleneck inverted residual blocks and efficient channel B-ECA blocks are added to the HED backbone, which greatly reduce the numbers of parameters and increase the weight of salient features. The features of all stages and all layers are fused in order to retain the more edge features. The high-level features are upsampled and cross-layer fused with the low-level features to solve the problem of edge blur. Training and testing on the self-made MPDS data set shows that, compared with the original HED method, the proposal increases the ODS and OIS by 8.1% and 6.6% respectively, and improves the detection speed from 22.08 FPs to 39.02 FPS. In the paper extraction stage, a paper extraction method based on the paper structure is proposed. After thinning the paper edge based on non-maximum suppression, detection and filtering the line, and extraction paper vertex based on structural constraints, the image containing only paper is extracted. The experimental results show that the paper extraction method can quickly and accurately extract the entire paper image in various complex desktop environments and occlusion situations, which provides an interaction basis for the common paper-pen interaction method. [ABSTRACT FROM AUTHOR]

Aiming at low success rate and efficiency of exam paper generation for small exam data- base, the paper proposed a prediction calculation based intelligent exam paper generation algorithm without backtracking. Based on prediction calculation, according to the constraints, this algorithm firstly figures out the allocation matrix that meets the requirements of the exam database , and then accesses the exam database. This can improve the success rate of exam paper generation and there is no backtracking when the exam database is accessed. Besides , according some strategy , a batch extraction scheme is used to speed up exam paper generation. This algorithm has a certain universality and high research value for constructing intelligent exam system. [ABSTRACT FROM AUTHOR]

In order to improve the fitness of RMB paper currency number recognition algorithm, based on structural feature of characters, a novel RMB paper currency number recognition algorithm is proposed, which is based on multiple mutual compensation structure characteristics. And the image pre process of RMB paper currency number is studied including image positioning, image binarization, single character segmentation, character size normalization, etc. Algorithms of English letter arrangement, Arabia digital arrangement and their mixed arrangement of RMB paper currency number are designed. These algorithms are introduced by taking partial flow chain of mixed arrangement recognition program as an example. An ARM embedded system is used to validate three recogntion programs. Some suggestions are proposed to solve the issues such as font diference of characters, and character interference in the experiment. The statistical results of accuracy of three recognition programs show that the algorithm has high precision and meets the practical needs. [ABSTRACT FROM AUTHOR]

Thanks to the instruction set architecture (ISA), the software community and the hardware community has been developing independently for years. However, with the advent of multi-core accelerators, the sequential programming model based on the Von Neumann architecture is confronted with troubles. Based on sequential execution model, ISA lacks support for parallel multi-core hardware. Thus, merely using ISA cannot decouple software and hardware. A new program execution model (PXM) is required to accomplish end-to-end compilation from neural networks to interface with sequentially executed programming platforms and parallel multi-core hardware backends, further exploring the optimization opportunities provided by parallel hardware. This paper proposes a codelet model as a new PXM, providing a general abstraction for the process of downloading sequentially executed programs onto parallel hardware. It further decouples the software frontend and hardware backend based on the instruction set. To ensure the reusability of the project, this paper implements the codelet model in the form of a codelet dialect within the MLIR compiler framework proposed by Google. MLIR aims to integrate fragmented compiler ecosystems and improve the reusability of frontend-to-backend integration processes. The codelet model implemented in MLIR in this paper can further enhance the reusability of the MLIR system. [ABSTRACT FROM AUTHOR]

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Structured matrices play an important role in scientific computing and engineering applications, such as Cauchy, Toeplitz, Vandermonde, and Hankel matrices. Although these matrices are dense, they can be expressed with only O(n) parameters (generators), where n is the dimension of the matrix. The core idea of the algorithm in this paper is to use matrix generators to explicitly construct local matrix blocks of each process, thereby reducing communication overhead. Additionally, by leveraging the numerical low-rank property of these matrix blocks. This paper further minimize computational overhead. Consequently, the proposed parallel structured matrix multiplication algorithm framework can simultaneously reduce both computational and communication costs, making it suitable for matrix multiplication algorithms like Cannon, Fox, and PUMMA. Extensive numerical tests were conducted on the Tianhe-2 supercomputer, and the results demonstrate that the proposed algorithm achieves an 8.96x speedup compared to the PDGEMM function in ScaLAPACK. [ABSTRACT FROM AUTHOR]

Level2 market quotation data is the new generation of real-time market data products from the Shanghai and Shenzhen Stock Exchanges. Serving as an enhanced version of basic market data, it currently has the highest information density, the greatest amount of information, and the most insufficient mining in China. The data is of significant value in identifying potential risks in the securities market, but existing research lacks risk measurement and analysis based on it. Moreover, the scale of Level2 market quotation data in the entire market is large, and the deep learning models used to extract information are becoming increasingly complex. Although hardware computing power is constantly developing and improving, it still cannot solve problems such as long training time and low efficiency. Therefore, based on Level2 market quotation data of CSI 300, deep learning and other methods are used to mine high-frequency volatility factors, and builds a high-frequency volatility prediction model based on TabNet and LightGBM. At the same time, a distributed training algorithm Parallel_DE based on parallel differential evolution is proposed for parameter calculation in the process of model distributed training, its scene mapping scheme and overall process design are elaborated. The above two work are fully verified based on the proposed distributed training platform. The experimental results show that the high-frequency volatility prediction model can predict the realized volatility with high precision, and the effect has certain advantages compared with other methods; the Parallel_DE algorithm can effectively reduce the error of local parameters on the test set while retaining the diversity of parameters to a certain extent, so as to efficiently and distributedly train a deep learning model with excellent performance. This paper provides valuable technologies and methodologies for leveraging Level2 market quotation data in risk identification within the securities market. [ABSTRACT FROM AUTHOR]

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper explores the process of migrating CUDA programs to DPC++ using the GASAL2 sequence alignment software. The DPCT tool is utilized during the migration process to automatically convert CUDA APIs to DPC++ APIs. However, the migrated code still requires adaptation and modification to compile and run correctly. This paper evaluates the effectiveness of the DPCT tool in migrating CUDA programs to DPC++ and demonstrates the high-efficiency performance of DPC++ across different architectures. Experiments show that the migrated program maintains the accuracy of the original program and can run on heterogeneous devices with the Intel GPU architecture without code modification. At the same time, the migrated DPC++-based GASAL2 heterogeneous computing performance can reach approximately 90%-95% of the original CUDA-based GASAL2 computing performance, fully demonstrating the feasibility of DPC++ heterogeneous programming. The results provide a promising solution for cross-platform heterogeneous programming to fully utilize a wider range of hardware support [ABSTRACT FROM AUTHOR]

Traditional performance analysis methods for high performance applications have shortcomings such as additional overhead during the analysis process and inaccurate analysis results, resulting in users spending more time and domain knowledge. To address these issues, this paper transforms the problem of program performance analysis into a multi-classification problem of unbalanced small sample datasets under high-dimensional features. By collecting 500 pieces of performance data that include seven types of metrics such as the number of process switches, memory utilization, and disk I/O load during program runtime, after data preprocessing such as PCA dimensionality reduction, a program performance problem analysis model is trained using a random forest classifier. Experimental validation shows that the model can identify five types of performance issues, including excessive memory utilization and heavy disk I/O load. To evaluate the effectiveness of the model's guidance, this paper collects performance data generated by the HotSpot3D program and the LU-Decomposition program during runtime. Based on the model's output guidance, the two validation programs are optimized at the runtime level and the compilation level. Experimental results indicate that the proposed method can effectively guide the optimization of program performance, with speedup ratios of 1.056 and 5.657 for the two programs, respectively. [ABSTRACT FROM AUTHOR]

Sparse matrix-vector multiplication (SpMV) is one of the key operators in the field of high performance computing and also has significant applications in emerging deep learning domains. Existing research on SpMV often focuses on square sparse matrices, while there is still a lack of in-depth exploration for irregularly shaped sparse matrices (with unequal numbers of rows and columns). The characteristic of unequal numbers of rows and columns results in different storage features for these sparse matrices compared to square sparse matrices, leaving room for further optimization. Therefore, this paper establishes an SpMV performance model for irregularly shaped sparse matrices with unequal rows and columns, and analyzes that the performance bottleneck is caused by insufficient bandwidth for data exchange between cache and memory. At the same time, this paper carried out the following two optimization tasks: (1) Based on the commonly used CSR storage format for sparse matrices, a new RCSR storage format is proposed, which transforms and compresses a performance-limiting array in the CSR storage format, making SpMV more efficient; (2) An optimized SpMV algorithm based on the RCSR format is designed in conjunction with the SIMD instruction set extension of domestic processors. Thi paper tests regular and irregular sparse matrices on domestic Phytium processors. For regular sparse matrices, the comprehensive application of RCSR storage format, SIMD instructions, and OpenMP parallelization technology increases GFLOPS by 83.35% on average. For irregular sparse matrices, the performance improvement is related to the row-to-column ratio, and when the row-to- column ratio is not equal, the optimization effect is more obvious. [ABSTRACT FROM AUTHOR]

The development of large-scale models has led to the widespread application of model inference services. Constructing a stable and reliable architectural support for model inference services has become a focus for cloud service providers. Serverless computing is a cloud service computing paradigm with fine-grained resource granularity and high abstraction level. It offers advantages such as on-demand billing and elastic scalability, which can effectively improve the computational efficiency of model inference services. However, the multi-stage nature of model inference service workflows makes it challenging for independent serverless computing frameworks to ensure optimal execution of each stage. Therefore, the key problem to be addressed is how to leverage the performance characteristics of different serverless computing frameworks to achieve online switching of model inference service workflows and reduce the overall execution time. This paper discusses the switching problem of model inference services on different serverless computing frameworks. Firstly, a pre-trained model is used to construct model inference service functions and derive the performance characteristics of heterogeneous serverless computing frameworks. Secondly, a machine learning technique is employed to build a binary classifica- tion model that combines the performance characteristics of heterogeneous serverless computing frameworks, enabling online switching of the model inference service framework. Finally, a testing platform is established to generate model inference service workflows and evaluate the performance of the online switching framework prototype. Preliminary experimental results indicate that compared with the independent serverless computing framework, the online switching framework prototype can reduce the execution time of model inference service workflows by up to 57%. [ABSTRACT FROM AUTHOR]

Algebraic multi-grid (AMG) is one of the most commonly used algorithms for solving large-scale sparse linear algebra equations in the field of scientific engineering computing and industrial simulation. For each grid layer in the Setup phase, AMG needs to calculate the coarse grid matrix Ac=RAP through the product of three sparse matrix based on the restriction operator R, the current fine grid matrix A, and the interpolation operator P, which has become the main bottleneck in the parallel performance of AMG. This paper first discovers that the performance bottleneck of the RAP parallel algorithm in mainstream AMG solvers is caused by the quadratic complexity of branch judgments. Then, utilize the row-based order characteristics of the sparse matrix format CSR, and propose a RAP parallel algorithm called MiniBranRAP with linear complexity of branch judgment counts. The algorithm is integrated into the JXPAMG solver, and the effectiveness of the algorithm is verified through practical examples. The numerical test results show that, for 6 typical examples from practical applications, compared with the latest version of Hypre's Boomer AMG solver, the JXPAMG solver based on Mini- BranRAP can speed up the computation efficiency of the Setup phase by an average of 3.3 times and a maximum of 9.3 times on 28 processors. [ABSTRACT FROM AUTHOR]

For multi-attribute decision-making problems with decision information as picture fuzzy, this paper firstly defines the assignment of the degree of refusal membership with parameters based on the limitations of existing picture fuzzy distances. It also proposes a picture fuzzy distance that reflects decision-makers' risk preferences by combining consistency concepts. Through a numerical example, the new picture fuzzy distance is compared and analyzed with existing picture fuzzy distances to verify its superiority. Secondly, for attribute weights, this paper adopts the combination weighting method of game theory to combine the objective weights determined by entropy weighting with the subjective weights given by decisionmakers. On this basis, the new picture fuzzy distance is extended to evaluation based on distance from average solution(EDAS)method, and the weighted sum of positive and negative distances between each scheme and the average scheme is calculated using the new distance, resulting in comprehensive scores. Finally, numerical examples are used to verify the applicability and effectiveness of the proposed decision-making method. Sensitivity and comparative analysis results show that decisionmakers can adjust the parameter values according to their risk preferences to meet different decision-making needs. The method is more general and flexible compared to other existing decision-making methods, and the ranking results are more reasonable. [ABSTRACT FROM AUTHOR]

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Whale Optimization Algorithm (WOA) is a novel swarm intelligence optimization algorithm that converges based on probability. It features simple and easily implementable algorithm principles, a small number of easily adjustable parameters, and a balance between global and local search control. This paper systematically analyzes the basic principles of WOA and factors influencing algorithm performance. It focuses on discussing the advantages and limitations of existing algorithm improvement strategies and hybrid strategies. Additionally, the paper elaborates on the applications and developments of WOA in support vector machines, artificial neural networks, combinatorial optimization, complex function optimization, and other areas. Finally, considering the characteristics of WOA and its research achievements in applications, the paper provides a prospective outlook on the research and development directions of WOA. [ABSTRACT FROM AUTHOR]

CCGA devices have been widely used in many high-reliability fields due to the excellent ability of absorbing mismatch of thermal expansion. In order to resolve the problems including the preparation and high-precision assembly of CCGA devices, this paper studies the preparation of ceramic shells, design of high-precision column planting tools, depositing of solder paste, and column planting of CCGA devices. By using high temperature cofired ceramic technique, two types of CCGA ceramic shells with daisy chains are prepared successfully, which can be used to design complex electronic circuits flexibly. High-precision column planting tools with 1.6 mm height and 0.54 mm diameter holes are designed and prepared. Solder printing and solder paste jetting methods are compared, and the later one is adopted to obtain more accuracy paste volume. The relative deviations of paste volumes are less than 10%. Based on these conditions, high-precision column planting CCGA devices, with column tilt< 1°, coplanarity<0.1 mm, and tolerance of position better than ±0.02 mm, are then obtained. This method can effectively improve the column planting accuracy and solder column symmetry of CCGA devices, and help to ensure the accuracy and reliability of subsequent board-level soldering. [ABSTRACT FROM AUTHOR]

Aiming at the problems of small chaotic space and weak chaotic ability of existing chaotic mapping models, a new three-dimensional chaotic mapping model is designed by coupling two-dimensional Henon chaotic mapping model and Sine chaotic mapping model. Aiming at the problem that the security of encryption system will be reduced if the key is reused, this paper designs a new key generation method using Chebyshev mapping and plaintext image. Using the 3D chaotic mapping and key generation method designed in this paper, a color image encryption method is proposed. The results show that the method can encrypt images safely and effectively and protect the security of image information. [ABSTRACT FROM AUTHOR]

Code summarization can help developers understand the function and implementation of the code. The code summarization generation model can automatically identify the key information in the code and generate relevant summarization to improve the readability and maintainability of the code. Existing code summarization generation models usually only use abstract syntax tree structure information to represent code, resulting in low-quality mode--generated summarization. Aiming at this problem, this paper proposes a code summarization generation model that integrates multi-structure data. Firstly, the model adds data flow graph structure information to represent code on the basis of abstract syntax tree. Secondly, in order to capture the global information of the code, the model uses Transformers encoder to encode the abstract syntax tree sequence. In addition, the model uses the graph neural network to extract features from the data flow graph and provide information such as the computational dependences between variables. Finally, the model uses the cross-modal attention mechanism to fuse the two features of the abstract syntax tree and the data flow and generate corresponding summarization through the Transformer decoder. The experimental results show that, compared with the six mainstream models, the model improves the scores of BLEU, METEOR and ROUGEL on the Java and Python data-sets, and the generated summarization is also very readable. [ABSTRACT FROM AUTHOR]

In the era of big data, with the continuous popularization of social media, various text data are growing in the network and in life. It is of great significance to analyze and manage text data using text classification technology. Text classification is a basic research field in the field of artificial intelligence natural language processing. Under the given criteria, it classifies text according to content. The application scenarios of text classification are very extensive, such as sentiment analysis, topic classification, relationship classification, etc. Deep learning is a method of representation learning based on data in machine learning, and it shows good classification effect in text data processing. Chinese text and English text have differences in form, sound, and image. Focusing on the uniqueness of Chinese text classification, this paper analyzes and expounds the deep learning methods used for Chinese text classification, and finally sorts out commonly used datasets for Chinese text classification. [ABSTRACT FROM AUTHOR]

In the era of big data, the demand for high-density and large-capacity storage technology is increasing day by day. Unlike traditional storage technologies that record data bit by bit, holographic storage uses two-dimensional data pages as the link for reading and writing, adopting a three-dimensional volume storage mode. With advantages such as high storage density, fast data conversion rate, energy efficiency, safety, and ultra-long-term preservation, holographic storage is expected to become the strong competitor for mass cold data storage. This paper focuses on phase-modulated collinear holographic storage and analyzes the current research status of error correction codes for holographic storage. A detailed introduction is provided to an reference beam-assisted low-density parity check (LDPC) code scheme. [ABSTRACT FROM AUTHOR]

In industrial production, defects are treated differently based on their severity, so it is necessary to classify defects. However, in actual production, the classification accuracy is often insufficient due to the presence of few easily confused defects, which requires conservative treatment of all defects in production practice, resulting in significant human and economic costs. To solve this problem, this paper proposes a method for interactive separating easily confused defects. This method separates few easily confused defects from other defects, ensuring that the classification results of the remaining majority of defects can be directly used. This method selects easily confused defects from the training data as one or more new defect categories, called virtual defects, so that the trained network can distinguish between virtual defects and other defects. This paper designs a visual interface to assist users in interactively selecting easily confused defects to construct virtual categories. CMOS defect data from actual industrial sites are adopted for effectiveness verification, and the results show that the proposed method can quickly classify few confusing defects and ensure that the classification accuracy of remaining defects meets the requirements of industrial applications. [ABSTRACT FROM AUTHOR]

Satisfiability modulo theories (SMT) refers to the decidability problem of first-order logic formulas under specific background theories. SMT based on first-order logic have a stronger expressive capability compared to SAT, with higher abstraction ability to handle more complex issues. SMT solvers find applications in various domains and have become essential engines for formal verification. Currently, SMT is widely used in fields such as artificial intelligence, hardware RTL verification, automated reasoning, and software engineering. Based on recent developments in SMT, this paper first expounds on the fundamental knowledge of SMT and lists common background theories. It then analyzes and summarizes the implementation processes of Eager, Lazy, and DPLL(T) methods, providing further insights into the implementation processes of mainstream solvers Z3, CVC5, and MathSAT5. Subsequently, the paper introduces extension problems of the SMT as #SMT, the SMTlayer approach applied to deep neural networks (DNNs), and quantum SMT solvers. Finally, the paper offers a per spective on the development of SMT and discusses the challenges they face. [ABSTRACT FROM AUTHOR]

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Convolutional neural networks have achieved great success in the field of image analysis. With the development of deep learning, deep learning models are becoming more and more complex, and he amount of deep learning calculations is increasing rapidly. The sparse algorithm can effectively reduce the amount of deep learning calculations without reducing the accuracy. This paper uses three different pruning strategies under the ResNet18 model and GoogleNet model to reduce the calculation amount of the deep learning model. The results show that the global unstructured pruning strategy has a sparsity of 94% and 90% without reducing the accuracy respectively, the level unstructured pruning strategy has an average sparsity of 83% and 56% without basically reducing the accuracy respectively, and the level structured strategy has an average sparsity of 34% and 22% without basically reducing the accuracy respectively. Under the three pruning strategies, the delay and power consumption results obtained by running the sparse deep learning model in the Eyeriss deep learning accelerator shows that, compared with the unpruned strategy, under the ResNet model, the global unstructured pruning strategy has a 66.0% reduction in latency and a 60.7% reduction in power consumption, the level unstructured pruning strategy has a 66.0% reduction in delay and a 80.6% reduction in power consumption, and the level structured pruning strategy has a 65.6% reduction in latency and a 33.5% reduction in power consumption. Under the GoogleNet model, the global unstructured pruning strategy has a 4.5% reduction in latency and a 63.2% reduction in power consumption, the level unstructured pruning strategy has a 73.6% reduction in delay and a 55.0% reduction in power consumption, and the level structured pruning strategy has a 26.8% reduction in latency and a 5.8% reduction in power consumption. Therefore, this paper concludes that the global unstructured pruning strategy can greatly reduce he delay and energy consumption without reducing the accuracy. Under the level unstructured pruning strategy, the delay and energy consumption can be greatly reduced under the premise of slightly reducing the accuracy. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, digital images have been widely used in various fields. However, due to the appearance of image editing software, a large number of images have been tampered with maliciously, which seriously affects the authenticity of image content. Different from the general object detection, the study of image tampering detection needs to pay more attention to the tamper information of the image itself, which is often manifested in a weak form. Therefore, the detection model needs to focus on learning more abundant tamper features. This paper proposes a dual-stream Faster R-CNN model that combines gradient edge information and attention mechanism, and the model can realize detection and location of regions with different tampering types. One of the two streams is the color stream, which uses the attention mechanism to extract the surface features of the image, such as brightness contrast, visual difference of tampering with the boundary, etc. The second of the two streams is a gradient stream. A Gradient high-pass filter is used to enhance the anomaly edge features between the real area and the tampered area, making it easier for the model to find faint tampered traces in the tampered image. Finally, the features of color stream and gradient stream are fused by means of compact bilinear pooling. Due to the relatively small size of publicly available image tampering data sets, the Pascal VOC 2012 is used to create an image tampering detection data set which containing 10 010 images for model pre-training. The experimental results on COVER, Columbia, and CASIA data sets show that the model proposed in this paper improves the detection accuracy by 7.1% to 9.6% compared to the latest models, and exhibits higher robustness under JPEG compression and image blur attacks. [ABSTRACT FROM AUTHOR]

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For the existing path planning models, it is difficult to solve the problem of optimal path planning under different aircraft types and transportation time conditions with a single cost planning. This paper combines aircraft type configuration, transportation time and system cost, and establishes an optimization model of hub-and-spoke airline network through the location of hub cities, the flow distribution from non-hub city nodes to hub city nodes, the flight time of the fleet and the fleet size. The entropy method is used to establish the chromosome selection mechanism, and the adaptive crossover rate is introduced to improve the genetic algorithm. The improved algorithm (IGA) is used to optimize the location distribution of the best route and hub nodes. Our method is compared with the traditional genetic algorithm, bee colony algorithm and grey Wolf algorithm. The research indicates that combining different aircraft type configurations and transportation times is superior to single-cost path planning. By optimizing and solving the hub-and-spoke airline network model with the improved algorithm, the total system cost is reduced by 3.41x1010, providing a reference for the reasonable allocation of fleet resources [ABSTRACT FROM AUTHOR]

The neural network-enhanced factorization machine (FM) model, which can capture more high-order feature interactions and improve the accuracy of predictions, has become a research hotspot in the field of recommendation algorithms. Aiming at the problem that existing models do not comprehensively consider high-order interaction features and original low-order features when modeling the interactions between users and items, and in order to improve the model's ability to model user preferences, this paper proposes a new deep neural network-enhanced pairwise bilinear factorization machine model, DeepPRBFM, by combining depth neural networks and pairwise learning. This model adopts a bilinear structure with a pair of inputs containing positive and negative samples, utilizes multi-layer ResNet to preserve low-order features, enhances the interaction of high-order features with DNN, and employs a pairwise ranking-based loss function. Moreover, in the bilinear structure, increasing the proportion of negative samples can not only significantly alleviate the cold start problem of the recommendation system but also improve the prediction performance of the model. Experiments conducted on two realworld datasets show that the proposed model achieves higher recommendation accuracy and outperforms other models in objective metrics such as HR and NDCG. [ABSTRACT FROM AUTHOR]

Label classification aims to select the most relevant subset of labels from a set of labels to tag an instance, which has become a hot issue in the field of artificial intelligence. Traditional multilabel learning methods mainly focus on identifying single-modal data, with limited research on mining high-order correlation between multi-modal data. To address the issue of insufficient representation of high-order correlations between multi-modal data in multi-label scenarios, this paper proposed a multimodal multi-label classification method based on hypergraphs. The hypergraph model is introduced to model the high-order correlations of multi-modal data, and the fusion of multi-modal features and hyperedge convolution operation are utilized to achieve the mining of multi-modal data relationships and feature recognition, thus improving the performance of multi-modal multi-label classification. Experiments were conducted on the movie genre classification task, and the proposed method was compared with traditional methods. The experimental results show that the proposed method outperforms the comparison methods in terms of accuracy, precision, and F1 score, demonstrating the effectiveness of the method. [ABSTRACT FROM AUTHOR]

As computer systems grow in scale, complexity, and user demands for higher quality of service, the importance of logging systems has increased significantly. Logs are records of data or events generated during the operation of computer systems, and abnormal data in log entries often indicate performance fluctuations, anomalies, or failures within the system. Existing research on log anomaly detection mostly relies on a single feature, leading to issues such as inefficiency, incompleteness, and high misjudgment rates. This paper proposes a multi-feature-based approach for detecting anomalies in log events. Firstly, we define the multi-dimensional features of logs, including sequential, quantitative, semantic, and temporal features. Secondly, we utilize BERT combined with TF-IDF to obtain semantic feature vectors and integrate these features to form the input for our model. Finally, we establish a Bi- LSTM anomaly detection model based on an attention mechanism. Experiments show that the proposed anomaly detection model achieves a certain improvement in accuracy, providing a valuable reference for assisting in the discovery of log anomalie [ABSTRACT FROM AUTHOR]

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Electromagnetic method of geoelectrical frequency domain has a wide range of applications in exploring Earth's deep structure, petroleum exploration, environmental and engineering surveying, and its accuracy and efficiency of numerical simulation directly affect the interpretation results of data. However, there are currently issues with low accuracy and long computation time in three-dimensional numerical simulation of frequency domain electromagnetic fields, as well as limitations in computational scale. This paper proposes to use integral equation method and direct solution method to improve the solution accuracy, and adopt technologies such as hybrid parallel processing at multiple levels and multiple grain sizes, distributed storage, etc., to greatly reduce the computation time and expand the computational scale. This paper implements a fast, high-precision, and highly scalable three-dimensional numerical simulation method of frequency domain electromagnetic fields, which includes parallel processing between frequency points, parallel filling of impedance matrix, and parallel direct solution of equations. Firstly, the theoretical framework of integral equation method and its parallel implementation solution are introduced in detail. Then, typical cases are selected to verify the correctness of the program by comparing with previous calculation results. Finally, the scalability is tested for a large-scale example with 16 frequencies, 16×12 495 unknowns, and 861 observation points. Compared to a single node with 32 processes, when the computational scale reaches 256 nodes and 8 192 processes, the speedup ratio is 69.69 and the parallel efficiency is 27.22%. This large-scale parallel algorithm is applicable to both geomagnetic and controlled source audio geomagnetic integral equation methods. [ABSTRACT FROM AUTHOR]

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)