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Based on a polynomial basis, this paper constructs a finite field operation to optimize the S-box of the AES algorithm. By searching for the isomorphic mapping matrix between the fields, the inverse operation over finite field GF(28) is converted to the composite field GF((24)²). By using the SAT solver to search for the multiplication inverse operation over the finite field GF(24), two different polynomial basis optimization schemes are proposed. Scheme 1 uses the classic Class 3 gate circuit to construct the composite field. Based on Scheme 1, Scheme 2 uses a bit-level optimization method that introduces a MUX gate, and has less circuit area and smaller number of gates than those in Scheme 1. This paper uses the SMIC130 nm and SMIC65 nm processors to evaluate the circuit area. The evaluation shows that the results of this paper are better than the existing schemes using the same kind of gate circuits. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Fishery Modernization is the property of Fishery Modernization Editorial Office 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 Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Precise segmentation of lung field is a crucial step in chest radiographic computer-aided diagnosis system. With the development of deep learning, fully convolutional network based models for lung field segmentation have achieved great effect but are poor at accurate identification of the boundary and preserving lung field consistency. To solve this problem, this paper proposed a lung segmentation algorithm based on non-local attention and multi-task learning. Firstly, an encoder-decoder convolutional network based on residual connection was used to extract multi-scale context and predict the boundary of lung. Secondly, a non-local attention mechanism to capture the long-range dependencies between pixels in the boundary regions and global context was proposed to enrich feature of inconsistent region. Thirdly, a multi-task learning to predict lung field based on the enriched feature was conducted. Finally, experiments to evaluate this algorithm were performed on JSRT and Montgomery dataset. The maximum improvement of Dice coefficient and accuracy were 1.99% and 2.27%, respectively, comparing with other representative algorithms. Results show that by enhancing the attention of boundary, this algorithm can improve the accuracy and reduce false segmentation.

Bl factor is a key system parameter of the resonant blood viscoelastic sensor. In this paper, a dynamic measurement system for the spatial distribution of Bl factor based on velocity amplitude and motional impedance was designed. The system extracted the velocity amplitude and motional impedance of the coil under the dynamic condition of driving the sensor to generate simple harmonic oscillations using laser displacement and impedance analysis combined with in-phase/quadrature demodulation algorithm, and controlled the equilibrium position of the coil by adjusting the direct current component of the excitation current to realize the position scanning. In the position interval of [-240, 240] μm, the maximum coefficient of variation of the measurement results was 0.077 3%, and the maximum relative error to the simulation results was 2.937 9%, with a linear fitting correlation coefficient R 2 = 0.996 8. The system can be used to accurately measure the spatial distribution of Bl factor of the resonant blood viscoelastic sensor, which provides a technical support for the verification of the design of the sensor magnetic circuit.

In the process of moving target detection, the background is complex and the target is easily occluded. This paper proposes an autonomous detection algorithm for moving targets based on unsupervised missing value prediction. The missed targets are regarded as missing values in the tag data. According to the prior knowledge of the category and number of objects to be detected, the unsupervised generative adversarial imputation networks (GAIN) are used to predict the missing values through the acquired tag data, which greatly improves the recall rate at the expense of less accuracy. The experimental results on the small sample dataset of the characteristic parts of cattle show when the missing rate of tag data is less than 40%, the accuracy of missing value prediction is about 95%, and the average F1 score of detection is 0.92 for different degrees of occluded targets. This method has good detection performance for moving targets under the condition of small samples, which can reduce the uncertainty in practical application and the dependence of the algorithm on sample data, and improve the problem of missing detection in the process of moving target detection. [ABSTRACT FROM AUTHOR]

Objectives: Curve simplification is of importance in automated map generalization; nevertheless, the Douglas-Peucker (DP) algorithm popularly used in map generalization is not automatic, because a key parameter called distance tolerance (ε) must be given by experienced cartographers and needs to be input before execution of the algorithm. Methods: To solve the problem, this paper proposed a method to automatically calculate ε and by which the automation of the DP algorithm is achieved. The method consists of the following steps: (1) A formula is constructed by the Hausdorff distance for calculating the similarity degree (Ssim) between a curve at a larger scale and its simplified counterpart at a smaller scale. (2) 15 linear rivers are selected, and each of them is manually simplified to get their counterparts at seven different scales. The Ssim of each original river and each of its simplified counterpart at a smaller scale can be obtained using the formula constructed by the Hausdorff distance, and 15×7=105 coordinate pairs consisting of (S,Ssim) can be got, and a function between Ssim and S are constructed by the curve fitting using the coordinates. (3) In the meanwhile, the 15 rivers are simplified using a number of ε, and the Ssim of each original river and each of its simplified counterpart at a smaller scale can be calculated using the formula constructed by the Hausdorff distance. In this way, a number of coordinate pairs (ε,Ssim) are got, and a function between ε and Ssim is constructed by the curve fitting. (4) By the function between Ssim and S and that between ε and Ssim, a formula between ε and S can be deducted. Using the formula ε can be calculated automatically, because in a map generalization task S is usually known. After this step, automation of the DP algorithm is achieved. Results: The experiment results show that (1) The proposed DP algorithm can automatically simplify the rivers in a specific geographical area to get the results at different scales; and (2) the resulting river curves generated by the proposed DP algorithm have a high degree of similarity with the ones made by experienced cartographers. Their average similarity degree is 0.927. Conclusion: The proposed DP algorithm can simplify curve features on maps automatically, and the results are highly intelligent and credible. Although only river data is tested in this paper, the principle of the proposed method can be extended to other linear features on maps. Our future work will be on improving the accuracy of the proposed DP algorithm using more river data so that the algorithm can be used in practical map generalization engineering. [ABSTRACT FROM AUTHOR]

Aiming at the path planning problem of mobile robot in dynamic environment, this paper proposes a fusion algorithm (IHHO-IDWA) of Improved Harris Hawk Algorithm (IHHO) and Improved Dynamic Window Algorithm (IDWA）. First of all, this paper proposed a fusion adaptive chaos and core population dynamic division strategy, a fusion golden sine strategy and a dynamic cloud optimal disturbance resolution strategy to improve the performance of and solve the lack of search performance in the late stage of the Harris Eagle algorithm. In addition, in view of the problems that the dynamic window algorithm has a long planned path and easy to fall into deadlock, it proposed three improvement strategies to solve them: Firstly, add sub-functions to ensure that the algorithm can plan a shorter path; secondly, propose an adaptive weight strategy, balance the local obstacle avoidance ability and global search performance of the algorithm; thirdly, set the initial heading angle to avoid path redundancy. Finally, through numerical experiments of test functions, CEC2014 functions, and static and dynamic path planning experiments, it is verified that the performance of IHHO and IDWA in this paper has been significantly improved; through the 50×50 large-scale dynamic map, it is verified that the path planned by the fusion algorithm is shorter than that planned by the control group algorithm. 11.51%, proving the superiority of the method in this paper. [ABSTRACT FROM AUTHOR]

In the era of e-commerce, accurately predicting user purchase intentions has become a crucial factor for enhancing sales efficiency and optimizing the customer experience. Addressing the limitations of traditional ensemble strategies, which often suffer from subjective biases during the model design phase, this paper introduced an adaptive evolutionary ensemble learning model to predict user purchase intentions. This model adaptively selected the optimal base learners and meta-learners, incorporating both the predictive information from the base learners and the differential information between features to expand the feature dimensions, enhancing prediction accuracy. Moreover, to further refine the predictive capabilities of the model, this paper designed a binary adaptive differential evolution algorithm for feature selection, aiming to identify features that significantly influence the prediction outcome. Research results show that the binary adaptive differential evolution algorithm outperforms traditional optimization algorithms in global searches and feature selection. Compared to six common ensemble models and the DeepForest model, the proposed evolutionary ensemble model achieves a 2.76% and 2.72% increase in AUC value, respectively, and effectively mitigates the impacts of data imbalance [ABSTRACT FROM AUTHOR]

TANGRAM block cipher algorithm employs a bit-slice approach and is compatible with multiple software and hardware platforms. In response to TANGRAM-128/128 algorithm, this paper proposed a design plan which used Verilog HDL for FPGA implementation. This paper firstly provided an introduction to the characteristics and processed of TANGRAM algorithm and presented a scheme for reducing resource consumption using a finite-state machine for 44 rounds of encryption and decryption computation. Secondly, the engineering implementation of the FPGA algorithm was completed by the domestic Gaoyun platform and subjected to functional simulation and data correctness validation. Furthermore, relevant tests were performed on Quartus II 13.1.0 platform for comparison. Test results show that, based on Cyclone IV E EP4CE40F29C6 chip from Altera, TANGRAM block cipher algorithm has a maximum clock frequency of 138. 64 MHz and an encryption/decryption speed of 403.30 Mbps. While based on the GW2A-55 chip from Gaoyun, the maximum clock frequency is 96.537 MHz and the encryption/decryption speed is 280. 80 Mbps. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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.)

[Objective] With the rapid popularization of new energy vehicles and the vigorous development of autonomous driving technology, 3D object detection algorithms play a pivotal role in real road scenes. The LiDAR point clouds contain precise position and geometric structure information of the object, which can accurately describe the 3D space position of the target. Moreover, LiDAR makes environmental perception and route planning of unmanned vehicles a reality. However, cars in real scenes often fall into complex and difficult situations, such as occlusion and truncation of objects, which contribute to highly sparse clouds and incomplete contours. Therefore, the effective use of disordered and unevenly distributed point clouds for accurate 3D object detection has important research significance and practical value for the safety of autonomous driving. [Methods] This paper uses LiDAR point clouds in an autonomous driving scene to conduct in-depth research on a high-performance 3D object detection algorithm based on deep learning. The 3D object detection algorithm based on multibranch feature fusion (PSANet) is designed to improve the capacity and viability of autonomous driving technology. After the disordered point clouds are divided into regular voxels, the voxel feature coding module and convolutional neural network are used to learn the voxel features, and the sparse 3D data are compressed into a dense 2D bird's-eye view. Furthermore, the multiscale bird's-eye view features are deeply fused through the coarse and fine branches of the 2D backbone network. The splitting and aggregation feature pyramid module in the fine branch splits and aggregates the bird's-eye view features at different levels and realizes the deep fusion of semantic information, texture information, and context information of multiscale features to obtain more expressive features. The multiscale features in the coarse branch are fused after transposed convolution, and the precise original spatial location information is preserved. After the feature extraction of coarse and fine branches, the element-wise addition method can obtain more accurate and complete features for object classification, position regression, and orientation prediction. [Results] The experimental results on the KITTI dataset show that the average precision of PSANet in 3D object detection and bird's-eye view object detection tasks reach 81.72% and 88.25%, respectively. The inference speed on a single GTX 1080Ti GPU can reach 24 frames per second, and it shows strong robustness in complex scenes. Compared with the two-stage target detection algorithms MV3D, AVOD-FPN, F-PointNet, and IPOD, the average accuracy of 3D target detection of this algorithm increased by 18.21%, 5.89%, 8.94%, and 3.12%, respectively. Compared with the one-stage target detection algorithms VoxelNet, SECOND, PointPillars, and VoTr-SSD, the average accuracy of 3D target detection of this algorithm increased by 11.63%, 4.05%, 3.19%, 0.98%, 1.16%, and 0.7%, respectively. The detection speed of this algorithm improved by 14 frames per second compared with the two-stage algorithm PointRCNN with similar accuracy. [Conclusions] In comparison with other advanced algorithms, this algorithm exhibits strong performance, in which it can better balance the accuracy and speed of target detection in autonomous driving scenarios. Higher accuracy is required in the algorithm, whether one- or two-stage, for novel applications. The method with the highest efficiency must be used in an autonomous car. [ABSTRACT FROM AUTHOR]

In order to better cope with large-scale high-dimensional sparse data sets in today’s era, this paper combines the advantages of BB method, mini-batch algorithm and stochastic variance reduced gradient（SVRG）, and proposes a mini-batch sparse stochastic variance reduced gradient method（MSSVRG-R2BB） with randomly improved BarzilaiBorwein step size. First, on the basis of calculating the total gradient in the outer loop of SVRG, this article adds the subgradient of L1 norm, designs a sparse approximate gradient for the inner loop of SVRG, and obtains a sparse SVRG algorithm（SSVRG）. On this basis, this paper propose to use the improved BB method of random selection to automatically calculate and update the step size in the mini-batch sparse stochastic variance reduced gradient method, which solves the step size selection problem of the mini-batch algorithm, and expands the MSSVRG-R2BB algorithm. Numerical experiments show that MSSVRG-R2BB algorithm can not only reduce the operation cost and reach the convergence upper bound faster, but also achieve the same optimization level of other advanced small-batch algorithms, and perform stably and well for different initial parameter selection. [ABSTRACT FROM AUTHOR]

Copyright of Journal of China University of Petroleum is the property of China University of Petroleum 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 environmental perception of intelligent vehicles is an important part of achieving autonomous driving, and object detection is one of the crucial functions of the environmental perception systems. Accurate and rapid detection of targets nearby is essentail for the smart vehicles. The faster and more accurately the detection algorithm can detect surrounding targets, the better. However, the detection speed and accuracy of existing perception algorithms still need improving. The distance information between the surrounding objects and the vehicle itself during its operation should also be obtained. The widely used YOLO series of algorithms, as representatives of one-stage object detection methods, have achieved a satisfactory balance between speed and detection accuracy, but there is stiil great potential for further improvement. This paper proposes an end-to-end object detection algorithm based on YOLO algorithm improvement-dual YOLO, which detects and measures the major participants in road traffic ( cars, pedestrians, cyclists) . The attention mechanism acts in the same way as humans observe things, focusing on more important information. The basic idea of combining attention mechanism with convolutional neural networks is to allow the network to concentrate on effective feature information in the image and ignore invalid information, thus improving the overall detection precision. Its mechanism of action is achieved by changing the weights of neurons in the network. This paper is based on the improvement of YOLOV5 algorithm, adding a channel attention mechanism to the backbone network to increase the learning weight of the neural network for effective features, thereby enhancing detection accuracy. In the meantime, a distance measurement module for binocular cameras is introduced to obtain target distance information. The basic principle of binocular camera ranging is triangulation, which employs the visual difference between two cameras to determine the distance of an object. Firstly, the camera is calibrated and distortion is corrected, and then stereo matching using the Semi Global Block Matching (SGBM) algorithm is performed to obtain disparity. Finally, the distance information of the target is calculated. The test results show the dual YOLO object detection algorithm achieves a detection speed of 60 fps and an average accuracy of 85. 99% in identifying the major participants in road traffic, which are far more superior to other algorithms in detecting cyclists and pedestrians. The proposed algorithm well meets the real-time detection and distance measurement requirements of intelligent vehicles. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Railway Signalling & Communication Engineering is the property of Railway Signalling & Communication Engineering 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.)

Recommendation system can help users quickly find useful information and improve the retrieval efficiency of users effectively. However, the recommendation system has problems such as data sparsity and cold start, most of the existing recommendation algorithms that integrate social relations ignore the sparsity of social relations data, and there are few recommendation algorithms that integrate social relations and item attribute data at the same time. This paper proposes a recommendation model that is multi-task feature learning approach for social relationship and knowledge graph enhanced recommendation (MSAKR) in response to solve the above problems. Firstly, the algorithm extracts the user's social relations through the graph convolutional neural network to get the user's feature vector, then selects the neighbor by the graph centrality, and generates the virtual neighbor by the word2vec model, so as to alleviate the sparsity of the social data. This paper uses the attention mechanism to gather the neighbors. Secondly, multi-task learning and semantic-based matching model are used to extract the information of attribute knowledge graph to obtain the feature vector of the item. Finally, comprehensive recommendation is made to the user based on the obtained user and item feature vectors. In order to assess the performance of the recommendation algorithm, experiments are carried out on real datasets Douban and Yelp. Click-through rate prediction and Top-K recommendation are used to evaluate the performance of the model respectively. Experimental results show that the proposed model is superior to other benchmark models. [ABSTRACT FROM AUTHOR]

Aiming at the problem of incomplete location and fuzzy edge of salient region, this paper proposes a method of RGB-D salient target detection. Firstly, a cross-modal feature fusion module is designed to integrate RGB and depth information layer by layer. Six modal fusion feature outputs are obtained. This module reduces the discrepancy between RGB and depth information, providing more common and complementary deep features for the subsequent advanced semantic repair. Multiple levels of information is obtained based on the above modules. This paper uses the last three layer features to jointly extract richer high-level semantic information, and the initial salient map is obtained. After that, the network structure of U-Net is used to fuse from top to the bottom of the network. After upsampling, each layer is fused with the next layer in channel dimension. The first three layers of bottom features are guided by advanced semantic features before and after fusion, to complete the repair of the lowlevel features. Finally, the final salient map is obtained. The proposed cross-modal feature fusion module can adaptively fuse multi-modal features, highlight the commonness and complementarity of fusion features, and reduce the ambiguity of fusion. The proposed advanced semantic repair strategy is helpful to accurately detect the salient region and improve the edge clarity. Experimental results show that the proposed algorithm outperforms most excellent methods on five datasets, including NJU2K, NLPR, STERE, DES and SIP, which achieves relatively advanced performance. [ABSTRACT FROM AUTHOR]

TVM (tensor virtual machine) is a deep learning compiler, that translates the deep learning operators described by tensor expression to TVM IR (TVM intermediate representation) programs. After a series of operatorlevel optimizations on TVM IR, TVM generates the target code across diverse hardware back-ends. Tensor expression, a domain- specific language for tensor computation, performs loop transformation to operators. The result of loop transformation is a number of complicated expressions emerging in nested loop statements, which contain loop invariant code. However, in the context of deep learning applications, the traditional loop invariant code motion algorithm has severe limitations. Firstly, it's difficult to determine the extra-benefit of moving certain invariant code out of loops. Secondly, it's difficult to detect loop invariant code which has different orders of operands. Thirdly, it cannot process nested condition expressions well. Furthermore, there are conflicts with target hardware compiler optimizations. The application of loop invariant code motion technique is constrained by the aforementioned problems. In this paper, a new loop invariant code motion algorithm is proposed, which takes deep learning application characteristics into consideration in a heuristics way. The algorithm normalizes the program by manipulating the expression operands and simplifying the nested condition expression. This paper introduces a new cost model, which evaluates the cost of moving certain loop invariant code while the characteristics of TVM IR and target hardware back-ends are fully considered. The algorithm is implemented as a registered TVM pass on opensource compiler TVM version 0.7. To testify the effectiveness and correctness of this algorithm, experiments are conducted on TVM TOPI benchmark with 27 operators and 511 test cases under different input. Experimental results show that this algorithm improves 47.6% of operators'performance, and achieves speedups up to 40.0%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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 Precious Metals / Guijinshu is the property of Precious Metals Editorial Office 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 Railway Signalling & Communication Engineering is the property of Railway Signalling & Communication Engineering 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 Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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 Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition 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 Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office 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.)

In order to efficiently and correctly identify abnormal behaviors in Linux logs, this paper proposes a Linux log anomaly detection method based on the improved isolated forest algorithm. The method introduces an attention mechanism on the basis of the isolated forest algorithm, which can dynamically adjust the attention features and sample points when processing log data, and dynamically adjust the degree of attention according to the degree of abnormality of the samples. Experimental results show that the method achieves high efficiency in the Linux log anomaly detection task compared with traditional methods, and can effectively discover potential security threats and abnormal behaviors. [ABSTRACT FROM AUTHOR]

In the field of digital printing, accurately reproducing the color of computer images is a prerequisite for high-quality printing, where color characterization is a key step. Traditional polynomial regression models tend to amplify outliers in the characterization sample set due to high-order terms, causing model oscillation and affecting the accuracy of color characterization. Color characterization algorithms based on neural network have higher precision but significantly increase in algorithmic complexity, making them unsuitable for the efficiency requirements in industrial production. To address these issues, this paper proposed a color characterization method based on weighted polynomial regression algorithm with residual correction. This algorithm employed the Huber loss function, known for its strong robustness against outliers, as a substitute for mean squared error. It determined the weight of each sample through an adaptive mechanism and iteratively optimizes the residual values to obtain the optimal weight matrix, effectively reducing the impact of outlier samples on the system. Additionally, the correction module captured nonlinear scenarios that the initial model might miss, significantly improving the adjustment of the transformation results and thereby enhanced characterization precision. The results show that compared to conventional polynomial regression, this algorithm reduces the average color difference by 1.2. It achieves a precision close to that of deep belief network algorithms but with more than 99.37% reduction in inference time. [ABSTRACT FROM AUTHOR]

This paper proposed an aggregate signature scheme based on the SM9 algorithm to address the issue of excessive storage space occupied by the signatures of n messages generated by the conventional SM9 signature scheme. This scheme reduced the time cost of verifying multiple signatures compared to the original SM9 scheme, with a space cost of about 66.7% of the original SM9 scheme. Furthermore, the scheme introduced a locally verifiable approach based on the SM9 aggregate signature to tackle the problem where validators need only verify the correctness of specific messages when verifying signatures in current aggregate signature algorithms but still require knowledge of the complete message list. For the aggregated signatures S of a messages generated by a single user, the signer generated verification tags for a specific message m, enabling the verifier to verify the correctness of the message's signature without knowledge of the complete message list. Theoretical and experimental analysis confirm that the proposed scheme achieves a time complexity of O(1) for verifying specific messages given an aggregated signature. [ABSTRACT FROM AUTHOR]

This paper proposed a scheme to assist communication for vehicles undergoing uniform motion with variable speed, addressing issues such as Doppler spread, time delay spread, and received signal-to-noise ratio (SNR) through the utilization of a multi-intelligent RIS. Unlike traditional channel estimation, this scheme utilized channel gains and optimized RIS phase to minimize Doppler and delay spreads while maximizing instantaneous signal-to-noise ratio (SNR) for the moving object. The scheme studied the impact of RIS placement on the received signal by optimizing the RIS phase, exploring the distance between RIS units and the optimal positioning of RIS units when the distance between them was fixed to maintain a low range of reception SNR and delay spreading. It introduced relative motion theory for this problem and proposed respective algorithms to address both issues. Simulations show around a 10 dB power gain for vehicular communication receivers with multiple RIS assistance. Optimal RIS deployment suggests placing the first RIS near the base station and minimizing inter-RIS distances as much as practically feasible, showcasing the broad applicability of this multi-RIS communication approach in vehicular scenarios. [ABSTRACT FROM AUTHOR]

Extremely large-scale massive MIMO (XL-MIMO) emerges as a crucial technology for future 6G communication. Existing XL-MIMO hybrid-field channel models primarily model channels between uniform linear arrays and single-antenna users, employing the last-hop model of scatterers. However, if both transmitter and receiver are equipped with linear arrays, existing hybrid-field channel estimation schemes become inadequate. To address this, this paper proposed a channel modeling approach for XL-MIMO scenarios with both transmitter and receiver equipped with large-scale linear arrays, utilizing the SalehValenzuela model. Additionally, it introduced a hybrid-field channel estimation algorithm based on orthogonal matching pursuit (OMP). This algorithm initially estimated far-field components using the angle-domain transformation matrix, followed by estimating near-field components using the polar-domain transformation matrix. Furthermore, it introduced the Cramer-Rao lower bound (CRLB) to evaluate the proposed algorithm. Simulation results demonstrate approximately 0. 6 dB improvement in channel estimation performance compared to algorithms considering only far-field and near-field components. [ABSTRACT FROM AUTHOR]

In order to investigate vulnerabilities in stateful protocols, AFL-NET has put forward stateful protocol fuzz testing. In such fuzz testing, the selection of seeds makes a major contribution to the exploration of paths. However, current stateful protocol fuzz testers often repeatedly execute the same several seeds, resulting in an inability to effectively explore more paths. To alleviate this problem, starting from the gain of seeds, this paper proposed an effective seed dynamic scheduling algorithm based on stateful protocols. The algorithm utilized the potential gain, actual gain, and cost of seeds as the gain, using this gain to dynamically schedule seeds and allocate the number of times seeds. Experiments show that this method significantly improves the number of vulnerabilities found and also has a certain degree of improvement in increasing coverage, indicating that the definition of this gain and the seed scheduling algorithm can effectively select seeds and explore more paths and vulnerabilities. [ABSTRACT FROM AUTHOR]

To address the issue of calculation errors in neural network matrix-vector multiplication caused by manufacturing processes of RRAM cells, this paper modeled the characteristics of multiple faults in RRAM crossbar arrays and proposed a multi-fault tolerant algorithm. Firstly, it modeled the impacts of common transition fault and stuck at fault in RRAM crossbar arrays on the accuracy of neural network computations. Secondly, it partitioned the neural network and conducted partitioned training based on an improved knowledge distillation method. Lastly, it further optimized the algorithm by selecting an appro priate loss function and incorporating normalization layers. Experimental results on the MNIST and Cifar-10 datasets demonstrate that the proposed method can achieve a recovery rate of over 98% across multiple neural networks, indicating its effeetiveness in mitigating the impact of multiple faults in RRAM crossbar arrays on the accuracy of neural network computations. [ABSTRACT FROM AUTHOR]

Zero-knowledge succinct non-interactive proofs (zk-SNARK) have found extensive applications in various fields, including cryptocurrencies, due to their swift and efficient proof verification process. However, the computational intensity of the proof generation process poses a significant challenge, particularly at the number theoretic transform (NTT) stage. This paper proposed a GPU-based acceleration method for NTT computations, named PreNTT, to address this bottleneck. The method employed precomputation and optimization of twiddle factor powers to reduce the parallel computation overhead in NTT. It also introduced dynamic precomputation to enhance the efficiency of these computations. The algorithm made use of dynamic adaptive kernel scheduling, which allocated GPU resources on-chip according to the NTT input size, thereby boosting the computational efficiency for large-scale tasks. Additionally, the approach combined external global data shuffling with internal local data shuffling to avoid memory access conflicts. The use of CUDA multi-stream technology allowed for effective concealment of precomputation times during data transfer and computation processes. Experimental results indicate that the zk-SNARK system utilizing PreNTT achieves a speed-up ratio ranging from 1.7x to 9x in NTT module running times compared to Bellperson, the industryleading system. PreNTT effectively increases the parallelism of the NTT algorithm and reduces the computational overhead in zk-SNARK operations. [ABSTRACT FROM AUTHOR]

When dealing with dynamic economic dispatch problems involving electric vehicles (EVDED), HBO suffers from slow convergence and low accuracy. This paper proposed an improved heap-based optimizer algorithm called RDHBO. RDHBO adopted the optimal member region search and dual population interaction strategies. The former guided the optimal members to move to more promising regions, improving the convergence accuracy and convergence speed. The latter made full use of the climinated inferior individuals, enriching the diversity of the population, avoiding the algorithm falling into the local optimum. It investigated a 10-unit EVDED problem with four charging scenarios. The simulation results show that RDHBO is highly compe titive in generating low fuel cost and high stability compared with the existing representative methods. Finally, two improved strategies of RDHBO were subjected to ablation experiments, and the results show that combining the two strategies are effective. [ABSTRACT FROM AUTHOR]

This paper proposed an improved ant colony optimization named TSACO and an enhanced velocity obstacle (VO) to solve the UAV path planning problem. The TSACO incorporated non-uniform initial pheromone distribution, a turning heuristic function, and an elite ant system to improve the convergence speed and reduce the number of path corners during global planning. The enhanced VO integrated the UAV's dynamic equation, adaptive collision radius and emergency collision cone, along with an optimal velocity selection approach to increase real-time and safe local obstacle avoidance during local planning. Simulation experiments demonstrate that the proposed algorithms have better effectiveness in terms of path length, number of turns, and dynamic obstacle avoidance compared to other algorithms. [ABSTRACT FROM AUTHOR]

This paper introduced a real-time SLAM algorithm designed for mobile robots operating in environments characterized by low illumination and low texture. The algorithm leveraged the fusion of visual point-line features and IMU characteristics. It enhanced the EDlines algorithm by incorporating a leapfrog routing strategy and an adaptive threshold strategy, thereby improving the quality of line feature extraction and consequently enhancing feature tracking effectiveness. It established tight coupling constraints between visual and inertial features and employed a sliding window, along with a marginalization model, for nonlinear optimization. This enabled high-precision and real-time state estimation. Experimental results show that the proposed algorithm is superior effectiveness in online feature extraction when compared to traditional line segment extraction methods. Concurrently, the SLAM system achieves enhanced localization accuracy and robustness. [ABSTRACT FROM AUTHOR]

This paper proposed a new algorithm of the credibility evaluation concerning microblog users, based on their multiple interactive relationships and emotional tendencies. Firstly, the algorithm calculated the interaction degree and participation degree of users by analyzing the types and frequency of interactions among users. Secondly, this algorithm made the emotional scores of the textual contents including comments and replied among users by using a sentiment dictionary, and assessed users' emotional tendencies. Thirdly, based on the PageRank algorithm, the new algorithm extracted the features from user profiles and blog posts in order to compute the initial PageRank value for representing individual user credibility, adjusted the distribution of PageRank weights by integrating the user interaction degree and emotional tendencies, and modified the damping factor in accordance with the participation degree. Finally, the proposed algorithm made the credibility of the evaluated users after the iterative stabilization. The experimental results show that, compared to methods that overlook multi-interaction relationships and emotional tendencies, the new algorithm achieves better results with an improvement of up to 13.86% in terms of AUC value, precision, recall, and F₁-measure. The method that considers multiple interaction relationships and emotional tendencies can enhance the effectiveness of microblog user credibility evaluation with respect to microblog users. [ABSTRACT FROM AUTHOR]

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Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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.)

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[Objectives] The objective of this study was to address the problems of region non-closure, edge non-fitting and noise in the semantic segmentation of remote sensing images based on deep learning, and to further correct the recognition errors of semantic segmentation. [Methods] Taking Funan County of Anhui Province and Huai’an City of Jiangsu Province as the research sites, this paper built its own farmland plot dataset, introduced the idea of scale segmentation considering the multi-scale characteristics of images and the DESTIN (delineation by fusing spatial and temporal information) segmentation algorithm based on phenology, and proposed a semantic segmentation method for farmland plots based on multi-scale and DESTIN constraints. [Results] The field semantic segmentation based on the deep model under multi-scale and DESTIN-constrained effectively improved the problems of unenclosed region, non-fitting edges, noise and block-like blurring in the model, and corrected the misidentification area of the deep model semantic segmentation to a certain extent, and the IoU index reached 94.08% and 90.79% on the two test sets, respectively, and increased the semantic segmentation of the field by 1.65% and 2.32% compared with the remote sensing image of the deep model, respectively. The extraction area of the field in the study area was more complete and the accuracy was higher. [Conclusions] Multi-scale and DESTIN constraints further improved the semantic segmentation problem of fields, which was helpful to improve the accuracy of field recognition in high-resolution remote sensing images. [ABSTRACT FROM AUTHOR]

Aiming at the problem of insufficient representation capability for details and frequency information in traditional infrared and visible image fusion methods, this paper proposed a fusion algorithm based on spectral feature hybrid Transformer. The algorithm utilized the Fourier transform to map image domain features to the frequency domain. Then the novel complex Transformer extracted deep-frequency information from the source images and mixed them with the features of the image domain to enhance the representation capability for edges and details. Additionally, the algorithm used a token replacement module to evaluate the saliency of Transformer tokens and eliminate the tokens with lower scores to prevent the presence of artifacts in the fused image. Qualitative and quantitative experiments conducted on the MSRS dataset demonstrate that the proposed algorithm exhibits superior fusion performance compared to nine state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Addressing the issues of redundant exploration and inapplicability in dynamic environments inherent in fast marching tree algorithm (FMT), this paper proposed the APF-Dynamic FMT algorithm, which integrated artificial potential field method. This algorithm designed a sampling point guidance function based on artificial potential field method, which could dynamically adjust the sampling point generation range according to the environment information to reduce redundant exploration. Additionally, this algorithm designed a dynamic path tree adjustment mechanism, when the existing path was affected by the environment change, it could re-plan a new excellent path on the basis of the remaining path tree that is not affected, which was suitable for solving the path planning problem in dynamic environment. The results verify that the APF-Dynamic FMT algorithm can significantly improve the success rate and path quality of path planning while consuming the same computational resources, and when the existing path is affected by the dynamic environment, it can efficiently re-plan the passable excellent path. [ABSTRACT FROM AUTHOR]

The conventional cloud-end MCS system currently faces problems of excessive load, leading to a significant increase in delay and energy consumption during the data aggregation process, inevitably causing a decrease in data aggregation efficiency. To tackle this issue, this paper proposed a cloud-edge-end MCS computation offloading algorithm based on APDQN. Firstly, it established a utility function considering the balanced optimization of delay and energy consumption, with the maximization of system utility as an optimized goal. Secondly, improving the P-DQN algorithm, it proposed a computational offloading algorithm AP-DQN for combining resource allocation. This algorithm, leveraging the advantages of MCS, designated idle users as one of the offloading devices. Finally, the problem was solved using the proposed method. Experimental results show that, compared to existing algorithms, the proposed method significantly improves data aggregation efficiency and maintains excellent system stability. [ABSTRACT FROM AUTHOR]