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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]

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]

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]

Mainstream fully supervised deep learning segmentation models can achieve good results when trained on abundant labeled data, but the image segmentation in the medical field faces the challenges of high annotation cost and diverse segmentation targets, often lacking sufficient labeled data. The model proposed in this paper incorporates the idea of extracting labels from data through self-supervision, utilizing superpixels to represent image characteristics for image segmentation under conditions of small sample annotation. The introduction of multiple attention mechanisms allows the model to focus more on spatial features of the image. The position attention module and channel attention module aim to fuse multi-scale features within a single image, while the external attention module highlights the connections between different samples. Experiments were conducted on the CHAOS healthy abdominal organ dataset. In the extreme case of the 1-shot, DSC reached 0.76, which is about 3%higher than the baseline result. In addition, this paper explores the significance of few-shot learning by adjusting the number of N-way-K-shot tasks. Under the 7-shot setting, DSC achieves significant improvement, which is within an acceptable range of the segmentation effect based on full supervision based on deep learning. [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]

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

Fine-grained entity typing assigns fine-grained types to entities in the text, which can provide entities with rich semantic information through type information, and plays important roles in downstream tasks such as relation extraction, entity linking, and question answering systems. Since the length and position of entities in sentences are not uniform, the representation of entities in context can not be calculated. Existing fine-grained entity typing models process entity mentions and their contexts separately into individual feature representations, which separates the semantic relationship between them. This paper proposes a context-aware feature representation method in fine-grained entity typing, which places entities back into their contexts and solves the problem of computing entity feature representation when the entity length and position are not uniform. Experimental results demonstrate that this method can extract the feature representation of entities in their contexts, and significantly improve the performance of fine-grained entity typing. The Macro-F1 value of this method on the Chinese finegrained entity classification dataset CFET is improved by more than 10%. [ABSTRACT FROM AUTHOR]

The threshold-minimum dominating set problem in graph theory is a NP-hard problem in combinatorial optimization, which is essentially an extension of the minimum dominant set problem. This paper studies the threshold-minimum dominating set problem for the given undirected graph G = V, E) and threshold value r. Firstly, some mathematical properties and corresponding proof are studied, and these properties can be used to reduce the size of the given problem, thereby reducing the difficulty of the problem solving. Secondly, the upper and lower bound sub-algorithms and the reduced order subalgorithm are designed. Based on these sub-algorithms, a backtracking algorithm with reduction(BAR) is proposed, which can reduce the problem size and get the optimal solution. Finally, an example analysis and some random examples verifies that the algorithm can effectively reduce the difficulty of problem-solving. [ABSTRACT FROM AUTHOR]

Since the multi-person pose estimation in crowded scenes is still affected by the problems of small detection objects, resulting in low accuracy of pose estimation, this paper proposes a multiperson pose estimation correction algorithm based on improved YOLOv5. Firstly, in the backbone network of YOLOv5, a jump attention module is integrated to help the network find the region of interest in the image. Secondly, in the neck network, the weighted bidirectional feature pyramid is used to improve the feature fusion ability between feature maps of different scales, and the jump attention module and Transformer encoder are used jointly to enable the network to obtain global information and rich context information. Thirdly, a detection head is added to the detection part to make the network more sensitive to tiny objects. Finally, the key point object information obtained by network prediction is used to modify the attitude object information to obtain the final multi-person pose estimation result. Experimental results show that the proposed algorithm improves YOLOv5's AP50 by 2.2% and AP75 by 3.3% on the COCO dataset, validating the accuracy and robustness of this algorithm. [ABSTRACT FROM AUTHOR]

Research on drug-drug interaction (DDI) is conducive to clinical medication and new drug development. Existing research technologies do not fully consider the topological structure of drug entities and other entities such as drugs, targets, and genes in the drug knowledge graph, as well as the semantic importance of different relationships between entities. To solve these problems, this paper proposes a model based on neighborhood relation-aware graph neural network (NRAGNN) to predict DDI. Firstly, the graph attention network is utilized to learn the weights and feature representations of different relationship edges, which enhances the semantic features of drug entities. Secondly, neighborhood representations for different layers around the drug entity are generated to capture the topological structure features of drug entities. Finally, the drug-drug interaction score is obtained by element-wise multiplication of the two drug feature representation vectors. Experimental results show that the proposed NRAGNN model achieves 0.899 4, 0.944 4, 0.956 7, and 0.902 3 in ACC, AUPR, AUC-ROC, and F1 indicators on the KEGG-DRUG dataset, respectively, outperforming other current models. [ABSTRACT FROM AUTHOR]

In response to the limitation of existing point cloud classification and segmentation methods that use max pooling to aggregate local neighborhood features, which leads to the loss of important information beyond the maximum value, this paper proposes a point cloud classification and segmentation network that combines Adaptive Graph Convolution (AGConv) and Attention Pooling (AP). Firstly, a local graph structure of the point cloud is constructed using K-nearest neighbors algorithm, and adaptive convolution kernels are generated based on the features of the points, enabling flexible and accurate capturing of local neighborhood features. Secondly, to effectively enhance feature aggregation, attention pooling is utilized to define an energy function and obtain weight values, which are used to weight and aggregate more representative local features of the point cloud. Finally, adaptive graph convolution and attention pooling are stacked to extract global features layer by layer, thereby improving the accuracy of classification and segmentation. Experimental results demonstrate that compared with the benchmark network, the average class accuracy of point cloud classification is improved by 0.9%, and the average intersection over union of part segmentation and semantic segmentation is improved by 0.8% and 0.3% respectively. This demonstrates that the algorithm can effectively improve the accuracy of point cloud classification and segmentation, and has high robustness. [ABSTRACT FROM AUTHOR]

Network intrusion detection systems are crucial for maintaining network security, and there is currently limited research on intrusion detection scenarios with only a few abnormal markers of network data. This paper designs a weakly-supervised learning intrusion detection model, called WIDSAPL, based on the anomaly retention of data. The detection model consists of four parts: data transformation layer, representation learning layer, transformation classification layer, and anomaly discrimination layer. By using a set of learnable encoders to map samples to different regions and compress them into a hypersphere, the label information of abnormal samples is used to learn the classification boundaries of normal and abnormal samples, and the abnormal score of the samples is obtained. Testing the WIDS-APL system on four datasets demonstrates the effectiveness and robustness of the system, with improvements in the AUC-ROC values of 4.80%, 5.96%, 1.58%, and 1.73% respectively compared to other mainstream methods. Furthermore, there are enhancements of 15.03%, 2.95%, 4.71%, and 9.23% in AUC-PR performance. [ABSTRACT FROM AUTHOR]

Aiming at the inaccurate phoneme segmentation problem caused by the lack of consideration of Lao language tone changes and audio diversity in existing methods, this paper proposes an unsupervised phoneme segmentation method for Lao language with multi-feature interaction fusion. Firstly, self-supervised features, spectral features and pitch features are independently coded to avoid the insufficiency of a single feature. Secondly, multiple independent features are gradually fused based on the attention mechanism, so that the model can more comprehensively capture the information of Lao language tone changes and phoneme boundaries. Finally, a learnable framework is adopted to optimize the phoneme segmentation model. The experimental results show that the proposed method improves the R-value by 27.88% on the Lao phoneme segmentation task compared with the baseline methods. [ABSTRACT FROM AUTHOR]

The rapid development of Internet technology has enabled mobile devices to play a more significant role in people's lives. A user may possess multiple devices to meet various needs such as office work, socializing, and entertainment. In practical applications, multiple devices owned by the same user also face many data synchronization requirements to support distributed applications across devices (e.g., cross-device video playback continuation). However, there is currently limited research on endto- end data synchronization across multiple devices. Therefore, this paper proposes a data push strategy, called Push After Delay (PAD), suitable for application synchronization between multiple mobile terminals of users. The strategy adopts a differentiated approach to active and sleeping devices and flexibly delays the transmission to sleeping devices. The delay decision is based on the dynamic adaptive adjustment method of AIMD (Additive Increase Multiplicative Decrease) and the scheduling enhancement mechanism oriented to application access frequency, which adaptively pushes synchronization according to user usage habits. Experimental results show that compared with the undifferentiated push synchronization scheme, the PAD push strategy can significantly reduce the number of wake-ups of data synchronization on mobile devices while ensuring a low data access error rate and achieving a balance between consistency and synchronization overhead. [ABSTRACT FROM AUTHOR]

Visual odometry endows robots with the ability of autonomous positioning and building environmental maps, and is widely used in various unmanned devices. Visual odometry involves a large amount of image processing and calculation, but most of its deployment platforms only have extremely limited computational resources, limiting its application scope. In response to the I/O bottleneck of existing low-power visual odometry, this paper proposes a high-speed low-power visual odometry, named ELPVO, based on RGB-D cameras for the STM32F7 embedded platform. ELPVO fully considers the hardware resources of the STM32F7 platform, improves the processor utilization efficiency through DMA transmission, and further enhances the processing speed without changing the algorithm accuracy. On the STM32F767 embedded platform equipped with a 216 MHz ARM Cortex®-M7 processor, with the TUM RGB-D dataset as the testing benchmark, ELPVO can achieve a processing speed of 26 frames per second for images with a resolution of 320 x 240, with an overall run speed improved by 84% and run power consumption maintained at 0.7 watts. [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]

The sequence recommendation algorithm based on self-attention mechanism utilizes users' interactive sequences to model their dynamic preferences and predict their future behaviors. However, directly inputting the interactive sequences into the self-attention layer will limit the effective utilization of local association information in the sequences. In add-on, most of the existing recommendation algorithms use the dot product of the representation of the users' recent behaviors and the target items to calculate the item scores, which will weaken the impact of previous interactive items on the recommendation results. This paper proposes a date-aware sequential recommendation algorithm that fuses local information of sequences. It uses multiple vertical filters to fuse multiple local association information of each interactive item in the interactive sequence, and uses cross-attention mechanism to capture the relationships between all historical interactive items and target items. It also abandons the traditional position embedding method and replaces it with absolute time embedding based on the date of interaction occurrence. Experimental results on multiple public datasets show that the algorithm has certain improvement compared with the baseline algorithms in different evaluation indicators. [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]

The three-way concept analysis is a combination of three-way decision and formal concept analysis. The greatest progress of this theory compared with formal concept analysis is that it can simultaneously study the information that is "commonly shared" and "not commonly shared" in the formal context. Attribute granulation is a theory based on the decomposition of attributes into sub-attributes using a granularity tree and pruning, forming a new set of attributes. However, due to the numerous prunings on the same granularity tree, the key issue to ensure the efficiency of attribute granulation is how to choose the pruning and determine the optimal direction for further operations to achieve optimal granulation results. In this paper, through theoretical derivations, t is proved that there is a close internal relationship between the original three-way concepts and the new three-way concepts obtained from attribute granulation, which can be used as the basis for measuring the efficiency of attribute granulation. Firstly, based on the relationship of attribute granulation levels, the attribute granulation levels are divided into attribute granulation levels with partial order relationships and attribute granulation levels without partial order relationships. Furthermore, the definition of refinement coefficients is given, and the measurement roles of refinement coefficients in the two types of attribute granulation levels are respectively explained, so as to achieve the purpose of measuring the efficiency of different attribute granulation directions. [ABSTRACT FROM AUTHOR]

Previous researches on implicit discourse relationship recognition (IDRR) usually focus on designing effective discourse encoders. Different from theirs, this paper proposes a novel approach which introduces contrastive learning into IDRR so as to obtain representations of discourse units (DUs) with more differentiation. Specifically, a lightweight IDRR classification model is firstly adopted. Then, to better learn representations of DUs, the application of three different contrastive learning methods in IDDR are explored from multiple views, including instance-level, batch-level, and group-level. Finally, three multi-view contrastive learning objectives are combined for better IDRR. Our proposed method only slightly increases training time and introduces small additional parameters. Experimental results on PDTB 2. 0 show that our method achieves the state-of-the-art performance. [ABSTRACT FROM AUTHOR]

The lack of large parallel corpora is one of the key issues in low-resource neural machine translation. This paper proposes a neural machine translation method based on language model distillation, which regularizes neural machine translation training by using a monolingual language model. This method introduces prior knowledge contained in the language model to improve translation results. Specifically, we draw on the idea of knowledge distillation, and use the target-side language model (teacher model) trained on rich monolingual data to construct the regularization factor of the low-resource neural machine translation model (student model), allowing the translation model to learn highly generalized prior knowledge from the language model. Unlike traditional monolingual language models that participate in the decoding process, the language model in this method is only used during training and does not participate in the inference stage, so it can effectively improve decoding speed. Experimental results on two low-resource translation datasets of Uyghur-Chinese and Tibetan-Chinese from the 17th national machine translation conference (CCMT2021) show that compared with the current state-of-the-art language model fusion baseline system, BLEU can be improved by 1. 42 points (Tibetan-Chinese) to 2.11 points (Chinese-Uyghu). [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]

With the integration of edge computing frameworks and federated learning protocols, an increasing number of copyright protection methods for deep learning models have been proposed. However, solely verifying ownership from the sender's perspective does not provide assistance to the receiver. Numerous studies have indicated that in client-edge-cloud federated learning systems, malicious users attempt to gain access to pubic models without contributing or even poison the pubic models. Therefore, it is necessary to provide a model ownership verification scheme for the receiver. Building upon existing neural network watermarking schemes, this paper proposes a dual-verification model watermarking scheme based on certification files. It introduces a certification file generation step and implements dual ownership verification of the model through adjustments in the model structure. The feasibility, robustness, and improvement in watermark embedding rate of the scheme are verified through experiments. [ABSTRACT FROM AUTHOR]

With the rapid development of big data and Internet of Things (IoT) technologies, the application scope of multidimensional time series data has become increasingly widespread. Faced with a large amount of complex time series data characterized by non-linearity and high-dimensional redundant features, traditional time series analysis methods struggle to effectively address the complexity of multi-dimensional time series with high-dimensional features, resulting in suboptimal predictive performance. To address these issues, this paper proposes the GGInformer model, which improves upon the Generic Algorithm and Informer model while incorporating the GRU network. This model not only efficiently extracts key features from multidimensional time series but also effectively addresses longterm dependency issues. To validate the predictive capability of the model, experiments are conducted on two real datasets and three pubic benchmark datasets, all of which demonstrated superior performance compared to the baseline models. Specially, compared to the Informer baseline model, the GGInformer model achieves reductions in Mean Squared Error (MSE ) values of 22 %, 13 %, 20%, 23 %, and 38 % across the five datasets. The experimental results indicate that the GGInformer model can effectively address the complex feature extraction challenges of multidimensional time series data and further enhance time series prediction capabilities. [ABSTRACT FROM AUTHOR]

With the growth and expansion of high-performance computing businesses, external factors such as data center space and power supply capacity often become constraints on cluster expansion and upgrading, resulting in the need for the construction of multi-site high-performance computing (HPC) clusters. Multi-site HPC cluster can break through the geographical limitations of a single cluster and provide more computing resources. Based on the practice of SJTU-computing platform, this paper summarizes the unified management methods of infrastructure and system software, as well as the high availability design of cluster remote disaster tolerance, including: adaptive Slurm job scheduling system and Open OnDemand visual portal site, extending high availability capabilities for LDAP and other basic services, and building a hierarchical aggregation monitoring system. Finally, this paper demonstrates the effectiveness of remote supercomputing cluster solutions from three dimensions: data transmission, user experience, and platform high availability. [ABSTRACT FROM AUTHOR]

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]

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

With the popularity of the Internet, information on the web is spreading to the public at an astonishing speed. However, false information and rumors are also rapidly spreading due to the cascade effect, causing great harm to society. Finding the source of rumor spread on social networks plays a crucial role in suppressing the spread of rumors. Most of the traditional rumor source localization methods fail to integrate multi-source features and the accuracy of localization still needs to be further improved. Therefore, this paper proposes a deep learning-based rumor source localization method that identifies the rumor source based on multi-source features observed in nodes affected by rumors. This method first obtains the influence vectors of nodes based on the similarity of influence between the node and observed nodes. Then, it uses autoencoder networks to encode the influence vectors of nodes, obtaining new embedding representations that contain node information, diffusion paths, and propagation time information. Finally, it calculates the probability of nodes being the source based on their new influence vectors to locate the rumor source. Experimental results on two simulated datasets and four real datasets show that compared with other methods, the proposed method can locate the rumor source at a faster speed and improve the accuracy of rumor source localization by more than 25%. [ABSTRACT FROM AUTHOR]

At present, many research teams have conducted in-depth research on minority language machine translation for South and Southeast Asia. However, as the official language of Pakistan, Urdu has limited data resources and a significant gap from Chinese, resulting in a lack of targeted research on Chinese-Urdu machine translation methods. To address this issue, this paper proposes a Chinese-Urdu neural machine translation model based on Transformer and incorporating Urdu part-of-speech sequence prediction. Firstly, Transformer is used to predict the part-of-speech sequence of the target language Urdu. Then, the translation model's prediction results are combined with the part-of-speech sequence prediction model's results to jointly predict the final translation, thereby integrating language knowledge into the translation model. Experimental results on a small-scale Chinese-Urdu dataset show that the proposed method has a BLEU score of 0.13 higher than the baseline model on the dataset, achieving significant improvement. [ABSTRACT FROM AUTHOR]

Event argument role classification is a subtask in event extraction, which aims to assign corresponding roles to candidate arguments in the event. Event corpus labeling rules are complicated and labor-intensive, and there is a lack of relevant labeling texts in many languages. Zero-shot cross-lingual event argument role classification can use source-side corpus with rich annotations to build a model, and then apply it directly to a target-side counterpart task where the labeled corpus is scarce. Focusing on the commonalities of the dependency structure of event texts between different languages, this paper further proposes a method that uses the BiGRU network to encode dependency paths connecting trigger words to candidate arguments. The proposed encoder can be flexibly integrated into several mainstream models in a deep learning framework for event argument role classification. The experimental results demonstrate that the proposed method is more effective in completing cross-lingual migration and improving the classification performance of multiple baselines. [ABSTRACT FROM AUTHOR]

In order to minimize the processing latency and energy consumption for user tasks in Mobile Edge Computing (MEC) and enhance user experience, this paper focuses on the computation offloading problem in a multi-user, multi-MEC server scenario under constraints on computational resources. With the objective of minimizing the weighted sum of user completion time and energy consumption, the problem is tackled by first decoupling it into two sub-problems: offloading decision and computation resource allocation. The Whale Optimization Algorithm is employed to solve the offloading decision problem, enhancing convergence speed by introducing a nonlinear convergence factor and inertial weight. A feedback mechanism is introduced to prevent local optima, yielding offloading decisions with higher probability of feasibility. The resource allocation problem is addressed using the Lagrange multiplier method to obtain the optimal computation resource allocation for each offloading decision. Finally, stable converged solutions are obtained through multiple iterations. Simulation results demonstrate that, compared to other benchmark solutions, the proposed approach reduces the system overhead by up to 44.6%. [ABSTRACT FROM AUTHOR]

The increase of IC design complexity and the continuous reduction of process feature size bring new severe challenges to static timing analysis (STA) and chip design cycle. In order to improve the efficiency of STA and shorten the chip design cycle, this paper fully considers the FinFET process characteristics and the principle of STA, and predicts the timing characteristics of another part of corners by introducing machine learning methods based on the timing characteristics of some corners. The experiment is based on an industrial design, and the results show that the proposed method uses 5 corners to predict the timing of other 31 corners, which can achieve an average absolute error of less than 2 ps, far better than the 21 process angles required by traditional methods. Thus, the proposed method significantly improves the prediction accuracy and significantly reduces the workload of static time series analysis. [ABSTRACT FROM AUTHOR]

As a tool to alleviate "information overload", recommendation system provides personalized recommendation services for users to filter redundant information, and has been widely used in recent years. However, in actual recommendation scenarios, there are often issues such as cold start and difficulty in adaptively selecting different recommendation algorithms based on the actual environment. Meta-learning, which has the advantage of quickly learning new knowledge and skills from a small number of training samples, is increasingly being applied in research related to recommendation systems. This paper discusses the main research on using meta-learning techniques to alleviate cold start problems and adaptive recommendation issues in recommendation systems. Firstly, it analyzes the relevant research progress made in meta-learning-based recommendations in these two areas. Then, it points out the challenges faced by existing meta-learning recommendation research, such as difficulty in adapting to complex task distributions, high computational costs, and a tendency to fall into local optima. Finally, it provides an outlook on some of the latest research directions in meta-learning for recommendation systems. [ABSTRACT FROM AUTHOR]

The maneuver identification of fighter aircraft is the basis for judging their tactical intentions, but the existing maneuver identification methods have weak real-time performance and lack interpretability, which cannot meet the real-time requirements in air combat and are not conducive to humanmachine trust. This paper designs a real-time maneuver identification algorithm based on early timeseries classification, which divides the complete maneuver into maneuver units and uses ensemble learning algorithm to recognize and monitor the maneuver units in real-time, in order to achieve real-time requirements and obtain high recognition accuracy. The algorithm uses interpretable models and explains the model through feature contribution, making the model more transparent and reducing the decision risk for air combat decision-makers. Nine different maneuvers, such as hovering and jackknifing, are selected for simulation experiments, which proves that the algorithm can complete the identification with only the first 20% of the sample data of the time series observed, and the identification accuracy can reach 93%. [ABSTRACT FROM AUTHOR]

Distant supervision relation extraction uses the automatic alignment of natural language texts and knowledge bases to generate labeled training datasets, solving the problem of manual sample labeling. In the current research, most distant supervision does not pay attention to the long-tail data, so most of the sentence bags obtained by distant supervision contain too few sentences. These sentence bags cannot truly and comprehensively express the data itself. This paper proposes a distant supervised relation extraction model (PG+PTATT) based on position-type attention mechanism and graph convolutional network. According to the similarity between sentence bags, Graph Convolutional Networks (GCN) aggregate the implicit high-level features of similar sentence bags to optimize the sentence bags and obtain more prosperous and more comprehensive feature information of the sentence bags. At the same time, an attention mechanism, Position-Type Attention (PTATT) is constructed, which can solve the problem of wrong labels in distant supervision relation extraction: using the position relationships between entity words and non-entity words and type relationships are modeled to reduce the impact of noisy words. The proposed model is experimentally verified on the dataset New York Times (NYT), and the experimental results show that the proposed model can effectively solve the problems existing in distant supervision relation extraction; and it can effectively improve the accuracy of relation extraction.. [ABSTRACT FROM AUTHOR]

The large amount of medical information carried in the electronic medical record can help doctors better understand the situation of patients and assist doctors in clinical diagnosis. As the two core tasks of Chinese electronic medical record (EMR) information extraction, named entity recognition and entity relationship extraction have become the main research directions. Its main goal is to identify the medical entities in the EMR text and extract the medical relationships between the entities. This paper systematically expounds the research status of Chinese electronic medical record, points out the important role of named entity recognition and entity relationship extraction in Chinese electronic medical record information extraction, then introduces the latest research results of named entity recognition and relationship extraction algorithm for Chinese electronic medical record information extraction, and analyzes the advantages and disadvantages of each model in each stage. In addition, the current problems of Chinese EMR are discussed, and the future research trend is prospected. [ABSTRACT FROM AUTHOR]

With the development of educational informatization, educational data presents characteristics such as high feature counts and high redundancy, resulting in the classification accuracy of current classification algorithms not being ideal on educational data. Therefore, this paper proposes a hybrid feature selection algorithm (RF-ATPSO) that integrates feature weighting algorithm with improved particle swarm optimization algorithm. The algorithm first uses the RELIEF-F algorithm to calculate the weights of each feature, removes redundant features, and then uses the improved particle swarm optimization algorithm to search for the optimal feature subset in the filtered feature set. Experimental results show that on 6 UCI public datasets, after feature selection using the RF-ATPSO algorithm, the average accuracy is improved by 10.04%, and the average feature subset size is the smallest and the convergence speed is the fastest. In the student academic performance portrait feature dataset, the algorithm achieves high classification accuracy with a smaller feature subset size, with an average accuracy of 94.77%, which is significantly better than other feature selection algorithms. The experiment fully demonstrates the practical application significance of this algorithm. [ABSTRACT FROM AUTHOR]

In the development process of SoC, how to efficiently and accurately perform functional verification and performance analysis is an urgent problem to be solved. Aiming at the current limited monitoring means of Network-on-Chip protocols on FPGA prototype platforms, this paper proposes an efficient monitoring and analysis method with hardware-software mixture for CHI protocols. By connecting C code through the DPI of SystemVerilog, the synthesizable hardware part provides a shared function body, while the non-synthesizable software part captures CHI messages in the SoC under test from various channels of the Network-on-Chip protocol through the shared function body for offline storage or online inspection. Experimental results show that this method has the advantages of low hardware resource occupation and high reusability. The offline mode has little impact on simulation speed, while the online mode can detect problems while the SoC under test is running, enabling efficient monitoring of CHI protocol messages on the prototype platform and effectively accelerating the localization and performance analysis of SoC problems. [ABSTRACT FROM AUTHOR]

This paper designs a fast, scalable and accurate architecture simulator (MVSim) for VLIW multi-core vector processors. The scalable VLIW multi-core vector processor model, multi-level storage architecture model and multi-core performance model are designed in MVSim. It implements cycle-accurate simulation of instruction set architectures, efficient functional simulation of Cache, DMA and multi-core synchronization, and uses multi-threading to achieve efficient and scalable simulation of multi-core processors. The experimental results show that MVSim can accurately simulate the program execution of the target multi-core processor, the simulation results are completely correct, and it has good scalability. The average simulation speed of MVSim is 227 times and 5 times faster than RTL simulator and CCS, respectively, and the average performance error is about 2.9%. [ABSTRACT FROM AUTHOR]

The expression of sentiment texts in different domains are different, so it is usually necessary to train the corresponding sentiment analysis model for each domain. In order to solve the problem that one model cannot be used for multi-domain sentiment analysis, this paper proposes a multi-domain text sentiment analysis method based on prompt tuning, called MSAPT. With the help of hard prompts, indicating the domain of the emotional text and the selected emotional labels, the model is prompted to draw on its knowledge of different domain sentiment analysis. Then, a unified "generalized model" is pretrained for sentimental analysis. In downstream learning of various domain texts, the model is frozen and prompt tuning is used to make the model learn the characteristics of emotional text in each downstream domain. MSAPT only requires saving a model and some prompts with far fewer parameters than the model for multi-domain sentiment analysis. Experiments were conducted using multiple datasets of emotional text in different fields, and the results show that MSAPT outperforms model fine-tuning when only prompted tuning is applied. Finally, the length of prompt tuning, hard prompt adapted to specific domains, soft prompt and the size of intermediate training dataset are ablated respectively, to prove their impact on the effectiveness of sentiment analysis. [ABSTRACT FROM AUTHOR]

The traditional express terminal distribution mode has problems such as redundant construction of express outlets and overlapping delivery paths, and the joint distribution model can effectively solve these problems. Therefore, this paper studies the location path of express terminal outlets in the case of simultaneous receiving and dispatching and uncertain receiving demand under the joint distribution model. Firstly, a two-stage mathematical optimization model is established to deal with the problem of uncertain receipt volume by introducing random chance constraints. Secondly, a hybrid heuristic algorithm based on genetic algorithm and adaptive large neighborhood search algorithm is designed. Finally, numerical experiments show that the designed hybrid algorithm has a faster convergence speed and better solution quality than the traditional genetic algorithm. Too high or low risk acceptance of the optimization scheme in the random demand environment will lead to the increase of cost. With the increase of the ratio of customer receiving and dispatching volume, the cost of express terminal distribution first decreases and then increases, The nearest outlet return strategy can effectively reduce the distribution cost of enterprises. [ABSTRACT FROM AUTHOR]

AMR (Abstract Meaning Representation) abstracts the semantic features of a given text into a single-root directed acyclic graph. Due to the lack of non-English language AMR datasets, crosslingual AMR parsing aims to parse non-English text into the corresponding AMR graph of its English translation. Current cross-lingual AMR parsing methods rely on large-scale English-target language parallel corpora or high-performance English-target language translation models to build (English, target language, AMR) triplet parallel corpora for target language AMR parsing. In contrast to this assumption, this paper explores the possibility of achieving cross-lingual AMR parsing with only large-scale monolingual English and target language corpora. To this end, we propose cross-lingual AMR parsing based on unsupervised pretraining. Specifically, during pretraining, we integrate unsupervised neural machine translation tasks, English AMR parsing tasks, and target language AMR parsing tasks. During fine-tuning, we use an English AMR2.0-based target language AMR dataset for single-task fine-tuning. Experimental results on AMR2.0 and a multilingual AMR test set show that our method achieves Smatch F1 scores of 67.89, 68.04, and 67.99 in German, Spanish, and Italian, respectively. [ABSTRACT FROM AUTHOR]

Clustering is the main task of machine learning, and is also the core work of granular computing, namely information granulation. At present, most of granular computing based clustering algorithms only utilize the granule features without taking the granule structure into account, especially in the information field where algebraic structure is widely used. From the perspective of granular computing, this paper proposes a clustering method based on algebraic granularity (CMAG). Firstly, the algebraic granularity is newly formulated with the granule structure of an algebraic binary operator. Secondly, the CMAG is proposed with granules of incorporating congruence partition and granule structure of homeomorphic projection. Finally, the CMAG is experimentally compared with the tolerance domain model and the quotient space model, and the results show that the CMAG has better structural completeness and practical robustness. The CMAG can enrich and extend the granular computing theory from granule structure, and will provide a theoretical basis for the combination of granular computing methods and machine learning theory. [ABSTRACT FROM AUTHOR]

Aiming at the problems of a large number of small targets in UAV video stream detection, insufficient contextual semantic information due to low image transmission quality, slow inference speed of traditional algorithm fusion features, and poor training effect caused by unbalanced dataset category samples, this paper proposes a vehicle object detection algorithm based on improved Deformable DETR for UAV video streaming. In terms of model structure, this method designs a cross-scale feature fusion module to increase the receptive field and improve the detection ability of small objects, and adopts the squeeze-excitation module for object_query to improve the response value of key objects and reduce the missed or false detection of important objects. In terms of data processing, online difficult sample mining technology is used to improve the problem of uneven distribution of class samples in the data set. The experimental results show that the improved algorithm improves the average detection accuracy by 1.5% and the small target detection accuracy by 1.2% compared with the baseline algorithm without detection speed degradation. [ABSTRACT FROM AUTHOR]

The development of medical imaging technology has generated a massive amount of medical image data, which reflects the internal structural features of the human body. Medical image segmentation technology can improve the efficiency of medical diagnosis, making it an important assistive tool for modern medical diagnosis. However, noise or artifacts that are inevitably present in the imaging process bring great challenges to the segmentation work. In existing segmentation models, single-frame medical image semantic segmentation models do not consider the relationship between image frames, while video semantic segmentation models utilize temporal information but have some limitations in edge extraction. To address these issues, this paper proposes a U-Net-based temporal semantic segmentation model modified by optical flow. This model can extract optical flow information between consecutive frames and perform feature extraction and weight allocation on the current frame and optical flow for correction. Experiments show that the model obtains optimal results on three evaluation metrics, namely Dice similarity, pixel accuracy and cross-merge ratio, on different types of datasets, namely Drosophila electron micrographs, combined healthy abdominal organ segmentation and coronary angiogram, which validate the effectiveness and generalization of the proposed model. [ABSTRACT FROM AUTHOR]

The maximum mean discrepancy (MMD), as a representative distribution metric between source domain and target domain, has been widely applied in unsupervised domain adaptation (UDA), where both domains follow different distributions, and the labels from source domain are merely available. However, MMD and its class-wise variants possibly ignore the intra-class compactness and interclass separability, thus reducing discriminability of feature representation. This paper proposes a focally discriminative loss for unsupervised domain adaptation. This method endeavors to improve the discriminative ability of MMD from two aspects: (1) the weights are re-designed for MMD in order to align the distribution of relatively hard classes across domains; (2) a focally contrastive loss is explored to tradeoff the positive sample pairs and negative ones for better discrimination. The integration of both losses can not only make the intra-class features close, but also push away the inter-class features far from each other. Moreover, the improved loss is simple yet effective, and it can be extended to the network structure of the attention mechanism. Experiments on several domain adaptation datasets verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Intelligent vehicular ad hoc networks (VANETs) are the core of intelligent transportation systems, in recent years, it has received increasing attentions from the academic community. However, due to the openness and fragility, VANETs are confronted with many security problems. In order to solve the problems such as two-way authentication between intelligent vehicles and nearby RSUs, exchange of session keys and anonymity of intelligent vehicles, this paper proposes an anonymous authentication and key exchange protocol in the intelligent vehicle networks. In the protocol, an identity-based digital signature algorithm is designed to enable the intelligent vehicle to send authentication information to the nearby road side unit (RSU) in a completely anonymous manner. After the RSU validates the authentication information, a message authentication code will be calculated and sent to the intelligent vehicle as the response to realize two-way authentication. In addition, during the anonymous authentication process, the session key can be negotiated for subsequent secure communication. The protocol is designed based on the identity cryptosystem, which does not need complex certificate management. The performance evaluation shows that this protocol can be effectively deployed in intelligent vehicle application scenarios with highly sensitive information. [ABSTRACT FROM AUTHOR]

With the increasing awareness of citizen copyright, more and more images containing watermarks are appearing in daily life. However, existing research shows that images with watermarks can cause neural network misclassification, posing a significant threat to the popularization and application of neural networks. Adversarial training is one of the defensive methods to solve this problem, but it requires a large number of watermark adversarial samples as training data. To address this issue, this paper proposes a visible watermark adversarial attack method based on intelligent evolutionary algorithm to generate high-intensity watermark adversarial samples. This method can not only quickly generate watermark adversarial samples, but also maximize the attack on the neural network. In addition, this method incorporates image quality evaluation metrics to constrain the visual loss of the image, making the watermark adversarial samples more visually appealing. The comprehensive experimental results show that the proposed method has lower time complexity than the benchmark watermark attack method, and has a higher attack rate on neural networks compared to the benchmark black box attack. [ABSTRACT FROM AUTHOR]