Abstract:Fuzzing is outstanding in detecting vulnerabilities in real-world programs. In recent years, researchers have paid considerable attention to fuzzing improving techniques, and large numbers of optimized fuzzers were proposed. These fuzzers are usually combinations of more than one improving technique for better performance. However, systematic evaluation of individual fuzzing improving techniques is still to be conducted. In this study, we establish an evaluation system for such techniques according to four metrics and used it to evaluate individual fuzzing improving algorithms integrated into recently proposed advanced fuzzers. Multiple groups of experiments are conducted to evaluate the effectiveness of each individual technique in each category of improving techniques, and the experimental data are comprehensively analyzed with the actual algorithm design and code implementation. We hope the evaluation of individual fuzzing improving techniques could help researchers develop more effective fuzzers in the future.

Abstract:Facial expression recognition (FER) has various applications in human-computer interaction scenarios. However, existing FER methods are not that effective for blurred and occluded expression. To cope with facial expression blur and occlusion, this study proposes a novel network based on local manifold attention (SPD-Attention), which uses manifold learning to obtain the second-order statistical information with a stronger descriptive ability for strengthening the learning of facial expression details and suppressing the influence of irrelevant features in the occlusion area on the network. At the same time, in view of the disappearance and explosion of gradient caused by logarithmic calculation, this study proposes corresponding regular constraints to accelerate network convergence. The effect of the algorithm is tested on public expression recognition data sets, which is significantly improved compared with those of classic methods such as VGG. The accuracy is 57.10%, 99.01%, 69.51%, 87.90%, 86.63%, and 49.18% on AffectNet, CK+, FER2013, FER2013plus, RAF-DB, and SFEW, respectively. In addition, compared with state-of-the-art methods such as Covariance Pooling, the proposed method has an accuracy improved by 1.85% on a special blurred and occluded expression data set.

Abstract:Load forecasting methods emerge one after another to maintain the stability of power grids. However, due to the characteristic difference in the generalization ability of algorithms and model complexity, the applicability of these methods to load forecasting differs. This study discusses and summarizes the research status of short-term power load forecasting both at home and abroad in the past five years from multiple dimensions, such as experimental data sets, data preprocessing, forecasting algorithms, optimization models, and evaluation methods. Meanwhile, we also present a summary of the advantages, disadvantages, and applicability of various forecasting algorithms, and the development trend of the short-term power load forecasting system is expounded and predicted. This study is expected to provide a reference for the forecasting model selection of power system loads in the future.

Abstract:As the basis of human motion recognition, two-dimensional human pose estimation has become a research hotspot with the popularity of deep learning and neural networks. Compared with traditional methods, deep learning can achieve deeper image features and express the data more accurately, thus becoming the mainstream of research. This study mainly introduces two-dimensional human pose estimation algorithms. Firstly, according to the number of people detected, the algorithms are divided into two categories for single-person and multi-person pose estimation. Secondly, the single-person pose estimation methods are divided into two groups based on coordinate regression and heat map detection. Multi-person poses can be estimated by top-down and bottom-up methods. Finally, the study introduces commonly used data sets and evaluation indexes of human pose estimation and compares the performance indexes of some multi-person pose estimation algorithms. It also expounds on the challenges and development trends of human pose estimation.

Abstract:With the development of 3D digital virtual humans, speech-driven 3D facial animation technology has become one of the important research hotspots in virtual human interaction. The key parts of the speech-driven 3D facial animation technology include the construction of a speech-visual mapping model and the synthesis of 3D facial animation. Specifically, the characteristics of phoneme-viseme matching methods and speech-visual parameter mapping methods are described. Next, the current methods of building 3D facial models are expounded, and the advantages and disadvantages of different motion control methods are analyzed according to the different representation methods of 3D facial models. Then, the subjective and objective evaluation methods for speech-driven 3D facial animation are expounded. Finally, the future development directions of speech-driven 3D facial animation technology are summarized.

Abstract:Unknown malicious network traffic detection is one of the core problems to be solved in anomaly detection as the traffic data obtained from high-speed network data flow are often unbalanced and changeable. Although there have been many studies on feature processing and detection methods of unknown malicious network traffic detection, these methods have shortcomings in simultaneously solving data imbalance and variability as well as detection performance. Considering the difficulty in unknown malicious network traffic detection, this study proposes an unknown malicious traffic detection model based on integrated SVM and bagging. Firstly, in view of the imbalance of network traffic data, a traffic processing method based on Multi-SMOTE oversampling is put forward to improve the feature quality upon traffic processing. Secondly, considering the distribution diversity of network traffic data, an unknown traffic screening method based on semi-supervised spectral clustering is presented to screen unknown traffic from mixed traffic with a diverse distribution. Finally, with the idea of Bagging, an unknown malicious traffic detector based on integrated SVM is trained. The experimental results reveal that the proposed detection model is superior to the current similar methods in comprehensive evaluation (F1 value), and it also has good generalization ability on different data sets.

Abstract:The structure of cars determines that there are a large number of blind spots around them, and thus a driver cannot make accurate judgments on the surrounding environment, which is not conducive to safe driving. The holographic transparent image can provide the driver with information on all blind spots around and under the vehicle to assist in safe driving. To solve the problem of obvious seams in image stitching, a distance-based Alpha image stitching algorithm is proposed, and a three-dimensional (3D) model is redesigned for the stitching algorithm. The transparent chassis function is optimized in the following three aspects. The bicycle model algorithm is improved to reduce the computational complexity on the premise of no image effect. The table look-up method is used to improve the accuracy of the conversion of the steering wheel angle into the wheel angle, and the problem of stitching dislocation between the transparent chassis and the surroundings is solved. The method of layered rendering is adopted to optimize the seam problem of the transparent chassis function. The experiments indicate that this technology can effectively improve the rendering effect.

Abstract:Regarding the power system, front-end technologies are diverse at present, whereas the manual coding is inefficient and cannot meet the rapid growth of demand. In response, this study designs a low-code visualization page editing engine for the power system with visualization and virtual DOM technologies in light of the component-based design idea. The virtual DOM technology is applied to the high-performance rendering of most scenes during page building. A unified data model is designed to integrate heterogeneous data and share data among components. The idea of a multi-type template for page editing is proposed to meet the requirement of the business system for diversified integration. The practice indicates that the system can provide agile, efficient, and low-code development under low barriers, which significantly improves the development efficiency of front-end pages.

Abstract:For the privacy leakage during the data collection of fog-assisted smart grids, this study proposes a novel privacy-preserving data aggregation scheme with fault tolerance. Firstly, the BGN homomorphic encryption algorithm and the Shamir secret sharing scheme are combined to protect data privacy. At the same time, an efficient signature authentication method is constructed based on the elliptic curve discrete logarithm problem to ensure data integrity. In particular, the scheme has two fault-tolerant measures. When some smart meter data cannot be sent normally or some cloud servers fail to work because of attacks, the scheme can still perform aggregate statistics. The security analysis proves that the scheme meets the security requirements of the smart grid. The performance experiments show that the proposed scheme has better computational and communication performance than the existing schemes.

Abstract:A data association model and a mathematical motion simulation model are built for the real-time motion process of ground moving targets in ground support of airports. The simulation program is improved on the basis of the GIS geographic data, graphics device interface (GDI+), multithreading technology, and concurrent synchronization mechanisms. In the simulation process, the improved A* algorithm is used to determine the best driving path of moving targets at airports, and the moving states of the targets are monitored in real time using data visualization technology. Finally, the repetition method is employed to carry out multiple tests for statistical analysis of the simulations. The simulation model has been applied to many fields, such as the deduction of the ground support process of airports, airport emergency plan verification, and flight transportation decision-making. It is of great significance to improve the ability of airports to support flight transit.

Abstract:Mobile edge computing (MEC) enables mobile devices (MDs) to offload tasks or applications to MEC servers for processing. As a MEC server consumes local resources when processing external tasks, it is important to build a multi-resource pricing mechanism that charges MDs to reward MEC servers. Existing pricing mechanisms rely on the static pricing of intermediaries. The highly dynamic nature of tasks makes it extremely difficult to effectively utilize edge-cloud computing resources. To address this problem, we propose a Stackelberg game-based framework in which MEC servers and an aggregation platform (AP) act as followers and the leader, respectively. We decompose the multi-resource allocation and pricing problem into a set of subproblems, with each subproblem only considering a single resource type. First, with the unit prices announced by MEC servers, the AP calculates the quantity of resources for each MD to purchase from each MEC server by solving a convex optimization problem. Then, each MEC server calculates its trading records and iteratively adjusts its pricing strategy with a multi-agent proximal policy optimization (MAPPO) algorithm. The simulation results show that MAPPO outperforms a number of state-of-the-art deep reinforcement learning algorithms in terms of payoff and welfare.

Abstract:The security and efficiency of cloud data storage are urgent issues to be solved in cyberspace security. Therefore, a new ciphertext retrieval model is proposed in the study, and on this basis, the ElGamal homomorphic cipher algorithm and SM4 block cipher algorithm are used to design a cloud ciphertext storage and retrieval solution based on hybrid homomorphic encryption. The retrieval solution can ensure data security during data uploading, retrieving, and downloading and can be applied to personal cloud USB drives and other application scenarios. Moreover, the correctness and safety of the scheme are analyzed and proved through experiments. The experimental results reveal that the scheme can assure correct retrieval results with high efficiency while ensuring data security.

Abstract:To tackle the problem that traditional container vector detection is limited to manual detection, this study designs a visual search system for container vectors based on machine vision. The system collects real-time video and captures the activity of vectors through a smart car under remote control. Then, it recognizes the vectors in the video returned by the car through deep learning and inter-frame detection. The system takes the YOLOv5 model as the training core and adopts a modular structure to realize the visual detection of container vectors. Machine vision helps improve detection efficiency and lays the foundation for the further use of robots to detect vectors.

Abstract:The cutting-edge technology in deep learning is applied to surface defect detection of strip steel for the accuracy improvement in surface defect detection of industrial hot-rolled strip steel. Therefore, a surface defect detection algorithm for hot-rolled strip steel is proposed, which takes Swin Transformer as the backbone feature extraction network and cascaded multi-threshold structure as the output layer. Compared with the deep learning target detection algorithm based solely on convolutional networks, the detection algorithm using the Transformer structure can achieve more accurate detection results. Specifically, first, Swin Transformer is used as the backbone feature extraction network to replace the conventional residual network structure and thus enhance the ability of the feature network to capture the deep semantic information implicit in an image. Secondly, a multi-cascade detection structure is designed, and step-by-step IoU thresholds are set to achieve the balance between detection accuracy and threshold improvement. Finally, training strategies such as soft non-maximum suppression (Soft-NMS), FP16 mixed precision training, and SGD optimizers are employed to accelerate model convergence and improve model performance. The experimental results reveal that the proposed algorithm has better detection performance on the industrial hot-rolled strip steel data set (NEU-DET) than the deep learning algorithms such as YOLOv3, YOLOF, DeformDetr, SSD512, and SSDLit. Additionally, the training speed and detection accuracy are significantly improved in the surface defect detection of crazing (Cr), inclusion (In), patches (Pa), pitted surface (PS), polled-in scales (RS), scratches (Sc), and other surface defects, and the missed detection rate is greatly reduced.

Abstract:This study proposes an improved U-Net for precise segmentation of bone data to solve the problems of low contrast, indistinct features, and insufficient extraction of bone features by existing algorithms in bone computed tomography (CT) images. In the network coding stage, the densely connected dilated convolution module is used to enhance the extraction of bone features; in the network decoding stage, the attention-based fusion module is adopted to make full use of spatial information and semantic information and thereby avoid the loss of bone information. When the improved algorithm is applied to a CT dataset of human lower limb bones, the Dice coefficient is 89.44%, and the intersection over union (IoU) coefficient is 80.55%. Compared with those obtained with the U-Net model, the Dice coefficient is increased by 5.1%, and the IoU coefficient is improved by 7.63%. The experimental results show that the proposed optimization algorithm can be employed to achieve precise segmentation of CT images of lower limb bones. It also provides a reference for the preoperative planning for orthopedic diseases and subsequent treatment.

Abstract:This study proposes a new flower pollination algorithm by incorporating the improved teaching-learning-based optimization strategy and dynamic Gaussian mutation to enhance the optimization performance. The algorithm first speeds the convergence through the promotion effect between the optimal individual and other individuals obtained by the improved teaching factor in the teaching mechanism. At the same time, the mutual learning mechanism between individuals is adopted to maintain the diversity of the population, thereby improving the optimization accuracy. Then, when it is detected that the algorithm falls into prematurity, the dynamic Gaussian mutation is carried out on the middle individuals of the population to increase the differences between individuals. In this way, it avoids the prematurity of the algorithm and then improves the comprehensive optimization ability. The optimization results of 16 standard functions are checked by the nonparametric statistical test to prove the effectiveness of the algorithm. Compared with other improved pollination algorithms, this algorithm has significant advantages. Finally, the new algorithm is applied to solve the application problems of telescopic rope, and good optimization results are achieved.

Abstract:This study proposes a deep Q-network (DQN) algorithm based on the K-nearest neighbor (KNN) algorithm (K-DQN) for the energy consumption prediction of buildings. When using the Markov decision process to model the energy consumption of buildings, the K-DQN algorithm shrinks the original action space to improve the prediction accuracy and convergence rate considering large-scale action space problems. Firstly, the original action space is evenly divided into multiple sub-action spaces, and the corresponding state of each sub-action space is regarded as a class to construct the KNN algorithm. Secondly, actions of the same sequence in different classes are denoted by the KNN algorithm to shrink the original action space. Finally, state class probabilities and original states are combined by K-DQN to construct new states and help determine the meaning of each action in the shrunken action space, which can ensure the convergence of the K-DQN algorithm. The experimental results indicate that the proposed K-DQN algorithm can achieve higher prediction accuracy than deep deterministic policy gradient (DDPG) and DQN algorithms and take less network training time.

Abstract:Considering the low signal-to-noise ratio (SNR) and image detail loss caused by additive white Gaussian noise (AWGN), an image denoising model based on the convolutional neural network (CNN) with residual dense blocks is proposed on the basis of the existing CNN algorithms. By introducing a multi-stage residual network and dense connections and using the Leaky ReLU activation function on the whole network, the model can better retain the effective information of images and effectively avoid feature loss while removing the noise of different levels of intensity. Compared with the residual learning model of the denoising CNN (DnCNN), the proposed model has an improved peak SNR by about 0.12 dB on the Set12 and Bsd68 test sets and improved structural similarity by about 0.008 6 on average. The test results reveal that the proposed model can fully extract image features, retain image details, and reduce the computational complexity of the network.

Abstract:Heart rate is an important physiological parameter for measuring human cardiovascular health and emotional stress. However, video-based non-contact heart rate detection techniques can degrade the detection accuracy in real scenarios due to facial movements and lighting changes. To solve the problem, this study proposes a new method of heart rate detection based on adaptive superpixel segmentation and multi-region integrated analysis depending on the high correlation between the selection of the region of interest (ROI) in a heart rate detection algorithm and its detection accuracy. Firstly, a face detection and tracking algorithm is used to crop the face image. Then the ROI is divided into non-overlapping sub-blocks by an adaptive superpixel segmentation algorithm. The original blood volume pulse matrix of each sub-block is constructed by chromaticity feature extraction. Finally, the pulse matrix is analyzed using multiple indicators, and the best region is selected for heart rate estimation. The experimental results show that the heart rate detection accuracy can be effectively improved by adaptive superpixel segmentation and optimal selection through multi-region analysis. The accuracy reaches 99.1% and 95.6% under stationary and motion disturbance conditions, respectively, and the accuracy is improved by up to 8.2% under illumination disturbance conditions compared with that of the traditional method. The proposed method enhances the robustness of heart rate detection in real scenarios.

Abstract:To improve the efficiency and accuracy of rail surface defect detection, a rail surface defect detection algorithm based on background difference and maximum entropy is proposed. Firstly, the background model of the rail images is built, and the original images are differentiated from the background images to avoid the influence of illumination change and uneven reflection and accurately highlight the defect area. Then, the improved genetic algorithm is combined with the maximum entropy method to seek the best segmentation threshold and binarize the difference graph. The operational speed of the maximum entropy method is accelerated by the improved genetic algorithm. Finally, the binary images are filtered to complete the segmentation of rail surface defects. The simulations indicate that this method can segment defects quickly and accurately, and the precision, recall, and accuracy are 88.6%, 93.4%, and 90.6%, respectively.

Abstract:This study is conducted to trace the source of sudden river pollution. Specifically, the coupling relationship between forward and reverse mass probability density is used to realize the decoupling of the location, discharge time, and discharge intensity of pollution sources; then, given the one-dimensional water body diffusion model and the monitoring data from the tracer experiment in Truckee River of the United States, a method for tracing the source of sudden river pollution is established on the basis of the improved firefly algorithm (FA). In the method, the monitoring data are divided into a training set and an experimental set, and by the training set data, the improved FA is employed to adjust the hydrological parameters of the river. Then, the adjusted hydrological parameters are used in the experimental set, and data from different monitoring sections are used independently for solutions. Finally, the results are analyzed by variance to exclude the data with large source-tracing errors. The results reveal that the source-tracing results have high accuracy and the ability to correct the monitoring data, which is of certain guiding significance for the actual sudden river pollution.

Abstract:Federated learning protects user privacy by aggregating trained models of the client and thereby keeping the data local on the client. Due to the large numbers of devices participating in training, the data is non-independent and identically distributed (non-IID), and the communication bandwidth is limited. Therefore, reducing communication costs is an important research direction for federated learning. Gradient compression is an effective method of improving the communication efficiency of federated learning. However, most of the commonly used gradient compression methods are for independent and identically distributed data without considering the characteristics of federated learning. For the scene of non-IID data in federated learning, this study proposes a sparse ternary compression algorithm based on projection. The communication cost is reduced by gradient compression on the client and server, and the negative impact of non-IID client data is mitigated by gradient projection aggregation on the server. The experimental results show that the proposed algorithm not only improves communication efficiency but also outperforms the existing gradient compression algorithms in convergence speed and accuracy.

Abstract:As imbalanced data are exposed to problems such as intra-class imbalance, noise, and small coverage of generated samples, an adaptive denoising hybrid sampling algorithm based on hierarchical density clustering (ADHSBHD) is proposed. Firstly, the clustering algorithm HDBSCAN is introduced to perform clustering on minority classes and majority classes separately; the intersection of global and local outliers is regarded as the noise set, and the original data set is processed after noise samples are eliminated. Secondly, according to the average distance between clusters of samples in minority classes, the adaptive sampling method with broader coverage is used to synthesize new samples. Finally, some points that contribute little to the classification of majority classes are deleted to balance the dataset. The ADHSBHD algorithm is evaluated on six real data sets, and the results can prove its effectiveness.

Abstract:In large industrial plants, due to a wide variety and a large number of equipment control switches, the complexity of operating procedures and the subjectivity of human judgment may lead to operational errors and cause serious consequences in the daily operation and maintenance process. To assist operators in accurately judging whether the state of an equipment switch is correct, an improved Faster R-CNN algorithm is proposed for state recognition of equipment switches. Firstly, the dilated residual network (ResNet) is used as the feature extraction network, and the multi-branch dilated convolution is introduced into ResNet50 to fuse the information of different receptive fields. Secondly, the feature pyramid network is improved by the addition of a bottom-up feature enhancement branch to the original network, which is used to integrate multi-scale feature information. Then, the K-means++ algorithm is applied to cluster bounding boxes of switches, and the size of proposals for equipment switches is designed. Finally, the non-maximum suppression (NMS) algorithm is replaced with Soft-NMS to reduce the influence of switch overlap on the detection effect and enhance the performance of suppressing the overlapping proposals. On a switch state dataset, the mean average precision (mAP) of the improved Faster R-CNN reaches 91.5%. Moreover, it has been applied to assist state recognition of equipment switches in the daily operation and maintenance of pumped-storage power stations to meet the needs of intelligent supervision in complex scenarios.

Abstract:Multi-attribute data privacy publication fails to balance the difference in attribute sensitivity and computational efficiency. For this reason, HMPrivBayes, a heterogeneous multi-attribute data publishing method with differential privacy based on attribute segmentation, is proposed. Firstly, the spectral clustering algorithm satisfying differential privacy is designed to segment the original data set, in which the similarity matrix is generated by the attribute maximum information coefficient. Secondly, with the help of attribute information, this method uses an improved Bayesian network construction algorithm to build Bayesian networks for each data subset. Finally, HMPrivBayes adds heterogeneous noise disturbance to the attribute joint distribution extracted from the Bayesian network to realize the protection of heterogeneous multi-attribute data, in which privacy budget is allocated based on the normalized risk entropy of attribute. The experimental results show that HMPrivBayes not only reduces the added noise but also improves the computational efficiency of synthetic data.

Abstract:Accurate prediction of sea surface temperature (SST) is vital for marine fishery production and the prediction of marine dynamic environment information. The traditional numerical prediction methods have high calculation costs and low time efficiency. However, the existing data-driven SST prediction methods mainly target the single observation point and fail when it comes to a sea region composed of multiple observation points. The existing regional SST prediction methods still have a long way to go in prediction accuracy. Therefore, we propose a regional SST prediction method based on XGBoost and PredRNN++ (XGBoost-PredRNN++). The method firstly converts SST data into gray images and then extracts the time characteristics of each point by the XGBoost model. On this basis, the CNN network is utilized for fusing the time characteristics into the original SST data, and the spatial dependence is extracted at the same time. Finally, the latest time series prediction model PredRNN++ is adopted to extract the temporal and spatial correlations among SST data to achieve the high-precision prediction of regional SST. The experimental results show that the high prediction accuracy and efficiency of the proposed method are superior to those of the existing methods.

Abstract:Traditional terminology standardization schemes based on template matching, artificially constructed features, semantic matching, etc., are often faced with problems such as low terminology mapping accuracy and difficult alignment. Given the colloquial and diverse expression of terminology in medical texts, modules of multi-strategy recall and implication semantic score ranking are used to improve the effect of medical terminology standardization. In the multi-strategy recall module, the recall method based on the Jaccard correlation coefficient, term frequency-inverse document frequency (TF-IDF), and historical recalls is employed. In the implication semantic scoring module, RoBERTa-wwm-ext is adopted as the scoring semantic model. The usability of the proposed method is validated for the first time on a Chinese dataset that is based on the systematized nomenclature of medicine-clinical terms (SNOMED CT) standard and annotated by medical professionals. Experiments show that in the processing of medical knowledge features, the proposed method can achieve favorable results in practical applications of medical terminology standardization and has high generalization and practical value.

Abstract:A billet is dispatched from the inventory to the bench by a crane and then from the bench to the front of the furnace through a track. In the past, the billet was pushed onto the track by the manual control of machinery. The automation of this process requires knowledge of the real-time position distribution of billets on the bench for automatic control of the pusher. In this study, the real-time positioning of billets on the bench is achieved by the machine vision method. Specifically, with the U-Net as the basic network, the residual blocks in classic ResNet are used to achieve the accurate segmentation of transverse positions of billets. The experimental results and field application tests indicate that the segmentation accuracy of this method can meet the control requirements of industrial fields.

Abstract:Recently, the research on skeleton-based action recognition has attracted a lot of attention. As the graph convolutional networks can better model the internal dependencies of non-regular data, the spatio-temporal graph convolutional network (ST-GCN) has become the preferred network framework in this field. However, most of the current improvement methods based on the ST-GCN framework ignore the geometric features contained in the skeleton sequences. In this study, we exploit the geometric features of the skeleton joint as the feature enhancement of the ST-GCN framework, which has the advantage of visual invariance without additional parameters. Further, we integrate the geometric feature of the skeleton joint with earlier features to develop ST-GCN with geometric features. Finally, the experimental results show that the proposed framework achieves higher accuracy on both NTU-RGB+D dataset and NTU-RGB+D 120 dataset than other action recognition models such as ST-GCN, 2s-AGCN, and SGN.

Abstract:In the process of drilling, the speed at which a drill bit breaks through rock and deepens the drill hole is called the rate of penetration (ROP), which is an important index reflecting drilling efficiency. In recent years, machine learning methods have been applied to the ROP prediction. However, it is found in practice that the prediction accuracy of ROP based on existing machine learning methods is significantly reduced when applied to new oil fields, and the main reason is that the data available for learning and training in these new fields are usually scarce or even completely missing. Therefore, improving the prediction performance of ROP in new oil fields is an important issue to be solved. Considering this, a cross-oilfield ROP prediction method based on transfer learning is proposed, and a boosting transfer regression model with physical constraints is constructed to predict ROP of new oil fields. The experiments on real drilling datasets indicate that the proposed method is effective, and the prediction accuracy is significantly better than that of the current mainstream ROP prediction methods.

Abstract:As medical informatization is constantly improving, electronic medical records have been more and more widely used, of which the unstructured text contains massive measurable quantitative information including patient clinical conditions. Due to the complexity of entities and quantitative information, it is a challenge to accurately extract measurable quantitative information. In this study, we propose the RPA-GRU model combining the relative position feature and attention mechanism based on a bi-directional gated recurrent unit. It incorporates the relative position feature into the attention mechanism to identify entities and quantity information. Meanwhile, the GATM model is proposed according to the reconstructed dependency tree-based graph attention network to learn graph-level representation, thus achieving the association between entities and quantity information. The experiment is based on 1359 electronic medical records from the burn injury department of a three-A hospital. The results show that the F1 values of RPA-GRU model and GATM model are 97.58% and 97.86% respectively in terms of identification and association of measurable quantitative information, up by 2.17% and 1.74% compared with the best-performing baseline model. In this way, the effectiveness of the proposed models is validated.

Abstract:In light of the structural characteristics of the displacement layer and the basic idea of differential fault, this study proposes a differential fault attack method for the eight-sided fortress (ESF) algorithm. In the 30^th round, a 1-bit fault is injected multiple times. Various input and output differential pairs are used to obtain different input sets for the S-box according to the differential characteristics of the S-box. Taking the intersection of the sets is a quick way to determine the only possible inputs for the S-box. The round key of the last round can then be obtained through analysis. Similarly, a 1-bit fault is injected in the 29^th and 28^th rounds many times. With the round key of the last round, the differential characteristics of the S-box are leveraged again to obtain the round keys of the last but one and last but two rounds. About 10 fault ciphertexts are required. After the round keys of three rounds are recovered, the computational complexity of recovering the master key is reduced to 2²².

Abstract:Text similarity matching is the basis of many natural language processing tasks. This study proposes a text similarity matching method based on a Siamese network and char-word vector combination. The method adopts the idea of the Siamese network to model the overall text so that the text similarity can be determined. First, when text feature vectors are extracted, BERT and WoBERT models are used to extract character-level and word-level sentence vectors which are then combined to have richer text semantic information. If the dimension is too large during feature information fusion, the principal component analysis (PCA) algorithm is employed for the dimension reduction of high-dimensional vectors to remove the interference of redundant information and noise. Finally, the similarity matching result is obtained through the Softmax classifier. The experimental results on the LCQMC dataset show that the accuracy and F1 score of the model in this study reach 89.92% and 88.52%, respectively, which can better extract text semantic information and is more suitable for text similarity matching tasks.

Abstract:Currently, the target detection algorithm based on depth neural network is mostly used for pallet positioning and a rectangular box is generally utilized. The positioning accuracy of the pallet center point is not high enough, and the horizontal direction of the pallet cannot be estimated effectively. To solve this problem, this study proposes a pallet positioning method based on keypoint detection, which locates the pallet by detecting the four corners of the front outer outline. Firstly, due to the shortage of large-scale pallet data sets, the human posture estimation of CenterNet is introduced by transfer learning. Then the keypoint grouping method is improved, and the adaptive compensation is proposed for keypoint regression to improve the keypoint detection accuracy. According to the location of pallet keypoints, a method of pallet center point calculation and pallet horizontal direction estimation based on geometric constraints is proposed. Compared with the original CenterNet, the proposed method raises the positioning index AP^kp of pallet keypoint from 0.352 to 0.728, and the positioning accuracy ALP of pallet center point to 0.946. Meanwhile, it can effectively estimate the pallet horizontal direction and is of high practical value.

Abstract:The mainstream all-mappers of next-generation sequencing mostly use the seed-and-extend method. Due to high storage costs or long retrieval time of the long-seed index, most of these algorithms use short seeds, which results in redundant candidate positions and increases the time cost of alignment. We, therefore, propose an all-mapper based on long seeds, and a long-seed hash index with low storage costs and moderate retrieval time is designed. The long-seed hash index limits the hash space through modular operation and uses the Bloom filter to identify different seeds at the same storage location. Long seeds significantly reduce the number of candidate locations and thus lower the time cost in the verification phase. The experiments on human gene sequencing datasets reveal that the proposed all-mapper has higher time efficiency than the existing mainstream all-mappers while maintaining the same accuracy.

Abstract:To address the problem of a small accepting neighborhood range during the node embedding of traditional graph convolutional networks, this study proposes a hyperspectral image classification network based on an improved GraphSAGE algorithm. Firstly, the original image is preprocessed by using the super-pixel segmentation algorithm to reduce the number of image nodes. This not only conserves the local topology information of the original image to the largest extent but also reduces algorithm complexity and thus shortened operation time. Secondly, the average sampling of the target node is carried out by the improved GraphSAGE algorithm, and the neighbor nodes are aggregated by the average aggregation function to reduce spatial complexity. Finally, the proposes approach is compared with other models on the public hyperspectral image datasets Pavia University and Kenndy Space Center. The experiment proves that the hyperspectral image classification network based on the improved GraphSAGE algorithm can achieve good classification results.

Abstract:For the cold start, sparse user feedback, and poor accuracy of similarity measurement in traditional article recommendation methods, this study proposes contextualized topic BERT (ctBERT), an article similarity calculation method that combines BERT with the topic model. The algorithm calculates the similarity scores between the given query and the related articles. The preprocessed articles are input into separate sub-modules for feature extraction and similarity score calculation. The similarity score is combined with the personalization score of the support set to obtain the final score. The algorithm is further improved by integrating single-sample learning into the recommendation framework. The experimental results from three different datasets show that the proposed method improves the NDCG criteria on all three datasets. For example, the NDCG@3 and NDCG@5 criteria improve by 6.1% and 7.2% respectively compared with other methods on the Aminer dataset, which validates the effectiveness of the method.

Abstract:The combination of kernel principal components analysis (KPCA) and control limits (CLS) based on Gaussian distribution will undermine the performance. The fault detection and identification method for nonlinear process based on kernel principal components analysis-kernel density estimation (KPCA-KDE) is proposed. kernel density estimation (KDE) technology is adopted to estimate the CLS based on KPCA for nonlinear process monitoring. According to the detection rate of all 20 faults in KPCA and KPCA-KDE, KDE has a higher fault detection rate than the corresponding method based on Gaussian distribution. In addition, KDE-based detection delay is equal to or lower than other methods. By changing the bandwidth and the number of reserved pivots during the fault detection, KPCA records a larger FAR while the KPCA-KDE does not record any false alarms. The application on the Tennessee Eastman (TE) process shows that KPCA-KDE has better monitoring performance in sensitivity and detection time than KPCA based on Gaussian CLS.

Abstract:With the continuous development of science and technology, medical diagnosis technology also makes continuous progress. Ultrasound technology, as a means of medical diagnosis, has been widely used in various medical fields. It has been widely recognized by doctors and patients because it is harmless to the human body and can dynamically and clearly show the health state of human tissues and organs. With the continuous development of ultrasound technology, people have higher requirements for the quality of ultrasound imaging. Due to the limitations of the materials of ultrasonic probes, such as for the manufacturing of ceramic transducer, and the compromise scheme of low-channel scanning adopted to reduce the cost and frame rate, the caused noises and artifacts will block the useful information of human tissues and organs, which will seriously affect doctors’ auxiliary diagnosis. In the field of ultrasound, how to enhance images and videos and suppress artifacts has become an important challenge. This study describes several filtering algorithms for artifact suppression in the spatial domain and their limitations and proposes an artifact suppression algorithm based on the frequency domain, which can well suppress the periodic artifact in real-time ultrasonic imaging. Firstly, this study simulates the periodic artifact with a sine wave to highlight its characteristics in the frequency domain. Then, the ultrasonic image is subjected to a two-dimensional Fourier transform into the frequency domain to suppress these artifacts. Because these artifacts are periodic, they have obvious characteristics in the frequency domain. The set corresponding to these artifacts in the frequency domain is found through the algorithm model of sliding window scanning combined with a threshold. Next, according to the dynamic range of the frequency domain and the given threshold, the points of these suspected artifacts in the set are depressed. Finally, the ultrasonic image is transformed into the spatial domain by inverse Fourier transform to obtain the processed image. This method can improve the suppression of periodic artifacts in ultrasonic images and retain useful information, thus able to enhance the accuracy of doctors’ judgment regarding human organ conditions.

Abstract:In view of the problem that the traditional current protection method cannot be applied when distributed generation (DG) is connected to the distribution network, this paper takes the double-feeder distribution network line as the research object. Firstly, when three-phase short-circuit faults occur at different locations of the line, the influence of DG connected to a busbar of feeder ends or a non-end busbar on the short-circuit current flowing through each protection device in the line is analyzed. Then, a distribution network model is built by PSCAD software for simulation analysis. Since it is difficult to set the action value of short-circuit faults in the distribution network containing DG, a matrix algorithm based on intelligent electronic devices (IEDs) for fault information uploading is proposed, and the accuracy of the algorithm is verified by an example. The results reveal that when DG is connected to a busbar of feeder ends or a non-end busbar, the fault that occurs in the downstream of DG will cause maloperation of the protection device in the fault section, and the protection device in the upstream section may encounter operation failure, which is not conducive to fault positioning and removal. The proposed matrix algorithm is applicable to the distribution network with DG, and regardless of single or multiple faults, the fault area can be accurately located to ensure the safe and reliable operation of the distribution network.

Abstract:Electroencephalography (EEG) has dynamic, nonlinear and numerically highly random signals. To break the limitations of traditional artificial neural network models in feature extraction and recognition accuracy during EEG recognition, this study proposes a new recognition method, which is based on the KIV model to recognize EEG signals. First, the dynamic characteristics of the KIV model under different stimuli are analyzed through simulation experiments. Then, the KIV model is used to recognize epileptic EEG signals and emotional EEG signals. Without feature extraction during the experiment, multi-channel raw EEG signals are directly input into the KIV model for recognition. The recognition accuracy is about 99.50% and 90.83% on BONN and GAMEEMO datasets, respectively. The results show that the KIV model outperforms existing models in the ability to recognize EEG signals and can provide help for EEG recognition.

Abstract:Retinal vessel segmentation is vital for assisting doctors in diagnosing ophthalmic diseases, including diabetic retinopathy, macular atrophy, and glaucoma. The attention mechanism is widely used in U-Net and its variants to improve the vessel segmentation performance. For more accurate retinal vessel segmentation and exploration of high-order and global context information, we propose a multi-scale high-order attention network (MHA-Net). The multi-scale high-order attention (MHA) module first extracts multi-scale and global features from the high-level feature maps to compute the initial attention map, enabling the model to handle medical image segmentation with variable scales. Then the high-order attention constructs the attention map through graph transduction followed by the extraction of high-level features at high order. We further embed the proposed MHA module into an efficient encoder-decoder structure for retinal vessel segmentation. Comprehensive experiments are conducted on the color fundus image dataset DRIVE, which indicates that the proposed method improves the accuracy of retinal vessel segmentation effectively.

Abstract:Accurate named entity recognition is the basis of structured electronic medical records and plays an important role in the standardized writing of electronic medical records. However, current word segmentation tools cannot completely and correctly distinguish professional medical terms, making it difficult to achieve structured electronic medical records. As for problems in medical entity recognition, this study proposes an improved deep learning model based on BiLSTM-CRF in the field of named entity recognition. The model combines text and labels as input, which makes the model focus on more useful information in the multi-head attention mechanism. BiLSTM performs feature extraction on the input and obtains the probability of each text on all labels. CRF learns the constraints of the data set during the training and improves the accuracy of the results after decoding. The experiment uses 1 000 manually labeled electronic copies as the data set and the BIO for labeling. Compared with the traditional BiLSTM-CRF model, the proposed model raises the F1 value in the entity category by 3%–11%, verifying its effectiveness in named entity recognition of medical records.

Abstract:Financial institutions are currently grappling with the growth of non-performing assets (NPAs). The prediction accuracy of credit overdue directly determines the size of NPAs. For better prediction of repayment ability, data modeling methods are often introduced, which may cause over-fitting for new businesses with small data samples. This study performs case studies and enriches the small data samples by similarity with random forest, LightGBM, XGBoost, DNN, and TrAdaBoost transfer learning. It aims to provide an effective solution to insufficient samples during the model establishment for small sample businesses. The results show that the area under curve (AUC) of the five machine learning models is greater than 80 for small data samples after similar financial business data are integrated. The AUC of TrAdaBoost is at least 2 points higher than that of LightGBM, XGBoost, DNN, and random forest models on the prediction set. In addition, TrAdaBoost stands out with the highest precision (88%) and recall (73%).

Abstract:Air quality prediction is of great importance for people’s daily travel. As a new recurrent neural network (RNN) of deep learning, the long short-term memory (LSTM) network demonstrates good prediction ability for time sequence data. However, neural network models generally rely on experience for parameter selection during training and have a long training period, low prediction accuracy, and unreliable prediction results. Considering this, this study proposes a bidirectional LSTM model based on the whale optimization algorithm (WOA), namely, the WOA-BiLSTM model. Specifically, the BiLSTM network can enhance the memory capability of sequence data information by its forward and backward network structure, and WOA can assist the BiLSTM model in finding the optimal network parameters during the training process on the basis of the bubble-net hunting strategy of whales. The model is applied for air quality index (AQI) prediction in Shaanxi Province and compared with BiLSTM and LSTM models separately, and it is found that the proposed model registers the best prediction result with the MAE value of 6.543 3 and R² value of 0.989 9. Therefore, the model is of solid theoretical and practical significance for applications in air quality prediction.

Abstract:Designing and utilizing good image prior knowledge is an important way to enable image inpainting. A generative adversarial network (GAN) is an excellent generative model, and its generator can learn rich image semantic information from large datasets. Thus, it is a good choice to use a pre-trained GAN model as an image prior. Making use of multiple hidden variables, this study adds adaptive weights to the channels and feature maps at the same time in the middle layer of the pre-trained generator and fine-tunes generator parameters in the training process. In this way, the pre-trained GAN model can be used for better image inpainting. Finally, through the contrast experiment of image reconstruction and image inpainting and the combination of qualitative and quantitative analysis, the proposed method is proved effective to mine the prior knowledge of the pre-trained model, thus finishing the task of image inpainting with high quality.