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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]

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.)

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]

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 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]

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]

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]

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, 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]

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.)

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]

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.)

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.)

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.)

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]

The single-stage object detection technology represented by YOLOv8 has significant optimizations in the backbone network, but fails to efficiently integrate contextual information in the neck network, leading to missed and false detections in small object detection. Additionally, the large number of algorithm parameters and high computational complexity make it unsuitable for end-to-end industrial deployment. To address these issues, this paper introduce the BiFormer attention mechanism based on the Transformer structure to enhance the detection performance for small objects and improve the algorithm's accuracy. At the same time introduce the GSConv module to reduce the algorithm size while ensuring no adverse impact on its performance, balancing the increase in computational and parametric costs brought by BiFormer. An object detection algorithm named Bi-YOLO is designed to achieve a balance between lightweight and algorithm performance. Experimental results show that compared to YOLOv8n, the Bi-YOLO object detection algorithm improves algorithm accuracy by 4.6%, increases the small object detection accuracy on the DOTA dataset by 2.3%, and reduces the number of parameters by 12.5%. Bi-YOLO effectively achieves a balance between algorithm lightweight and performance, providing a new approach for end-to-end industrial deployment. [ABSTRACT FROM AUTHOR]

To address the issues of insufficient receptive fields for extracting low-level features and the lack of reinforcement for local key features in lightweight networks, this paper proposed a multistage feature distillation-weighted lightweight image super-resolution network LMSWN. Firstly, a pyramid- like module is employed to expand the receptive field during shallow feature extraction, integrate feature information of different scales, and enrich the information flow of the network. Secondly, a multi-stage residual distillation-weighted module is designed to enhance the ability of square convolution to extract local key features, recover more detailed information, and improve reconstruction performance. At the same time, the combination of channel separation and 1x1 convolution realizes gradual distillation of features, reducing the number of network parameters. Finally, two adaptive parameters are introduced to jointly learn the features of the two branches of the multi-stage residual distillationweighted module, enhancing the attention to different levels of feature information and further enhancing the representation ability of the network. Experimental results show that the proposed network is fully validated on five benchmark datasets: Set 5, Set 14, BSDS 100, Urban 100, and Manga 109, and its performance exceeds the current mainstream lightweight network [ABSTRACT FROM AUTHOR]

To address the problem of data islands in Intelligent medical systems, achieve the goal of medical data security convergence, and simultaneously ensure the confidentiality, integrity and availability of medical data in transmission and storage process, this paper proposes a verifiable multi-dimensional medical ciphertexts cross domain aggregation scheme supporting transmission fault tolerance. The scheme integrates edge computing servers into the traditional cloud computing framework. By designing a homomorphic encryption algorithm and combining the Shamir secret sharing technology, this scheme realizes two-layer aggregation of multi-dimensional encrypted data with transmission fault tolerance. The scheme designs a digital signature algorithm based on elliptic curve, to ensure the integrity of medical encrypted data in the process of transmission and storage. In particular, the medical data analysis center can flexibly select target areas from the cloud server for cross domain aggregation, and exploit cloud storage audit mechanism to fulfil the lightweight integrity verification of the aggregation results. According to Horner's rule and the private key, the medical data analysis center can obtain the aggregation results of various dimensions of medical data from end users in the corresponding region. Security analysis and performance comparison demonstrate that this scheme can be securely and efficiently deployed in intelligent medical systems. [ABSTRACT FROM AUTHOR]

The distributed metadata management system utilizes multiple metadata servers (MDS) to store and manage a large amount of metadata. The system reduces the data load on each individual MDS by employing different mapping strategies to distribute the massive metadata across multiple MDS, thus minimizing the disk access frequency and improving the overall performance of the metadata management system. Typically, a hash function is used to map metadata keys to different MDS. However, when the feature values of the data are similar, the one-way nature of the hash function can result in data distribution imbalance, leading to performance degradation of the MDS. To address the performance degradation issue caused by uneven data distribution, this paper proposes a dynamic spatial partitioning and compressed Bloom filter-based metadata load balancing algorithm. The algorithm first constructs a hash bucket to organize the metadata keys, mapping the keys to different hash buckets using a hash algorithm. During the mapping process, the target hash bucket is dynamically adjusted based on the load condition of the MDS, and the mapping information of the metadata keys is sequentially stored within the corresponding hash bucket. When accessing metadata, the algorithm preprocesses the metadata keys using a compressed Bloom filter, and then performs a binary search within the specified hash bucket to retrieve the mapping information. Compared to recent metadata management algorithms, the proposed algorithm ensures load balancing of MDS even when key skewness occurs. Experimental results show that the algorithm achieves a 20% improvement in search performance compared to the optimal metadata management algorithm, with only a 2% increase in memory consumption. [ABSTRACT FROM AUTHOR]

Running deep neural networks on edge devices faces two challenges: model scaling and domain adaptation. Existing model scaling techniques and unsupervised online domain adaptation techniques suffer from coarse scaling granularity, limited scaling space, and long online domain adaptation time. To address these two challenges, this paper proposes a block-grained model scaling and domain adaptation training method called EdgeScaler, which consists of offline and online phases. For the model scaling challenge, in the offline phase, blocks are detected and extracted from various DNN and then are converted into multiple derived blocks. In the online phase, based on the combination of blocks and the connections between them, a large-scale scaling space is provided to solve the model scaling problem. For the domain adaptation challenge, a block-specific residual Adapter is designed, which is inserted into the blocks in the offline phase. In the online phase, when a new target domain arrives, all adapters are trained to solve the domain adaptation problem for all options in the block-grained scaling space. Test results on the real edge device, Jetson TX2, show that EdgeScaler can reduce the domain adaptation training time by an average of 85.14% and reduce the training energy consumption by an average of 84.1%, while providing a large-scale scaling option. [ABSTRACT FROM AUTHOR]

This paper proposes a low-power keyword spotting (KWS) system to overcome the problem of high-power consumption caused by deploying KWS algorithms on edge computing hardware, which can significantly impact the endurance of mobile devices. The proposed KWS system is based on computing-in-memory (CIM) technology and software-hardware co-design. In terms of algorithm, a ternary quantized MFCC-CNN joint algorithm based on the standard MFCC algorithm topology is proposed. All the general matrix multiplication (GEMM) in MFCC is mapped to the neural network accelerator. At the circuit level, the proposed system uses a computing-in-memory (CIM) core based on SRAM to overcome the power and memory walls in traditional von Neumann architecture accelerators. Additionally, a SRAM buffer circuit based on a look-up table is proposed to replace the register delay chain, which multiplexes the memory array in the CIM core. Both the SRAM-based CIM core and buffer are implemented using custom circuit units. At the system level, a low-power KWS system is proposed utilizing the two customized circuits discussed above. The system is implemented using ASIC and customized circuit design methods and synthesized using a 28 nm process library. The proposed system achieves a processing delay of 64 ms on 10 classification tasks, with a total power consumption of 645.28 μW. The dynamic power consumption of the MFCC pipeline accounts for 5.9% of the total dynamic power consumption, and the total power consumption accounts for only 1.3% of the system's power consumption. [ABSTRACT FROM AUTHOR]

In recent years, solid state drives (SSDs) have witnessed rapid development towards higher bandwidth and larger capacity. To expand SSD capacity, the size of flash memory pages has increased from 4 KB to 16 KB. However, operating systems still issue read/write requests to SSDs with a 4 KB memory page granularity, making it difficult for applications to fully utilize the high bandwidth of SSDs. Increasing the size of memory pages to align the granularity of I/O requests issued by the operating system with the SSD's flash memory read/write operations could be a potential solution. This paper delves into the effects of memory page size on system I/O performance and SSD lifetime for the first time, including setting the memory page size to 16 KB, running benchmark tests, and comparing the results with those obtained using 4 KB memory pages. The key findings are as follows: (1) 16 KB memory pages exhibit better read performance; (2) the write granularity of applications determines the performance of 16 KB memory pages; (3) 16 KB memory pages amplify the impact of invalid data within pages on SSD lifetime. [ABSTRACT FROM AUTHOR]

The high-speed serial interface chip is an important IP in high-performance interconnect network communication. This paper proposes a DSP design for 56 Gb/s high-speed Serdes receivers, in response to the problem of high bit error rate caused by severe channel attenuation over long transmission distances in high-performance interconnect network backplane communication using 56 Gb/s four pulse amplitude modulation (PAM4) signals. The DSP adopts a 64-channel parallel structure and processes the digitized signal from the receiver through a 16-Tap feed forward equalizer (FFE) and a decision feedback equalizer (DFE). By using the K-means clustering algorithm to generate dynamically changing DFE decision levels and combining it with the least mean square (LMS) algorithm, it can handle the equalization problem under different channel attenuation of 15-35 dB. To verify the performance of the algorithm, an experimental verification platform based on analog frontend chips and field programmable gate arrays (FPGA) was constructed. The experimental results indicate that the channel attenuation is 15-35 dB@14 GHz at a speed of 56 Gb/s, the error rate is less than 5e-10. [ABSTRACT FROM AUTHOR]

Short text semantic matching is a core issue in the field of natural language processing, which can be widely used in automatic question answering, search engines, and other fields. In the past, most of the work only considered the similar parts between texts, while ignoring the different parts between texts, making the model unable to fully utilize the key information to determine whether texts match. In response to the above issues, this paper proposes a short text semantic matching strategy based on BERT sentence vectors and differential attention. BERT is used to vectorize sentence pairs, BiLSTM is used, and a multi-header differential attention mechanism is introduced to obtain attention weights that represent intention differences between the current word vector and the global semantic information of the text. A one-dimensional convolutional neural network is used to reduce the dimension of the semantic feature vectors of the sentence pairs, Finally, the word sentence vector is spliced and sent to the full connection layer to calculate the semantic matching degree between the two sentences. Experiments on LCQMC and BQ datasets show that this strategy can effectively extract text semantic difference information, thereby enabling the model to display better results. [ABSTRACT FROM AUTHOR]

In modern network attacks, attackers often use various anti-forensics techniques to conceal their tracks. The harm of data erasure in anti-forensics technology is significant. Attackers can use this attack to delete or destroy data, thereby destroying attack evidence and disrupting the forensics process. Due to the concealment of the erasure activity itself, it is difficult to detect. This paper proposes an antiforensics check module (AFCM) using causal relationship based traceability technology. The model generates an alert traceability graph based on alert information, and calculates anomaly scores for each path in the graph through attack behavior characteristics. Through further filtering and aggregation calculations, the attack path is ultimately generated. The experimental results show that this model can effectively achieve traceability tracking of anti-forensics erasure activities and improve the identification between anti data erasure attack activities and normal activities. [ABSTRACT FROM AUTHOR]

In recent years, the pollution from diesel vehicle exhaust emissions in China has become increasingly severe. In order to improve the atmospheric environment, it is necessary to monitor diesel vehicles emitting black smoke. However, in urban traffic road scenarios, the detection of black smoke vehicles is often difficult to determine through rear-view videos due to factors such as mutual obstruction between vehicles. Additionally, the severe lack of relevant data greatly limits the effectiveness of the data. To address the above problems, this paper proposes a black smoke diesel vehicle re-identification model under the cross-camera scene. By introducing the IBN module to construct a feature extraction network, the adaptability of the network model to changes in the appearance of diesel vehicle images is enhanced. A loss function based on the Hausdorff distance metric learning is designed to measure the feature differences, increasing inter-class distance and reducing the impact of occluded samples during the optimization process. Then, benchmark datasets for diesel vehicle repositioning across multiple scenarios are constructed, and the proposed method is experimented on this dataset. The experimental results show that the proposed method achieves a relative accuracy of 83.79%, demonstrating high accuracy. [ABSTRACT FROM AUTHOR]

RISC-V instruction set has the advantages of flexibility and scalability, and vector extension is one of its extended instruction sets. When implementing vector extention, it is necessary to split the vector instruction into multiple microinstructions. If each microinstruction occupies a reordering buffer (ROB) entry, there will be certain information redundancy, and will reduce the number of instructions executed in parallel (in-flight instructions) in the CPU, affecting processor performance. Based on the method of decoupling the storage of instructions and microinstructions in ROB, a new queue RAB is used to store information such as the renaming mapping relationship of the destination register of each microinstruction, and each ROB stores only the common information of the microinstructions derived from its corresponding instruction. ROB and RAB respectively control the commit and walk of instructions and microinstructions, which reduces the redundancy of stored information and alleviates the problem caused by too many microinstructions for vector instruction splitting. On the basis of the above method, this paper implements the ROB compression of scalar instructions at the same time, increasing the maximum number of in-flight instructions with the same number of ROB entries. The final simulation results show that this method effectively improves the performance of the processor. [ABSTRACT FROM AUTHOR]