Abstract:It is an important step for quantum computing to outperform classical computing by evaluating quantum chips’ performance during their development to calibrate the degree of fit between the actual execution results and theoretical results of quantum algorithms. However, at present, there is no unified benchmark for evaluating the performance of quantum chips both in China and abroad, and the evaluation standards for local indicators of quantum chips can easily lead to misunderstandings about the overall performance of the chips. In view of this, this study first briefly describes performance indicators of existing quantum chips, then reviews current quantum chip evaluation methods by classifying evaluation technologies, and finally summarizes the existing problems of quantum chip evaluation technologies and looks forward to the future evaluation technology. In addition, the review can be easily sourced by those working in the relevant fields.

Abstract:Cataract is an ocular disease that mainly causes visual impairment and blindness, and early intervention and cataract surgery are the primary ways of improving the vision and the life quality of cataract patients. Anterior segment optical coherence tomography (AS-OCT) is a new type of ophthalmic image featuring non-contact, high resolution, and quick examination. In clinical practice, ophthalmologists have gradually used AS-OCT images to diagnose ophthalmic diseases such as glaucoma. However, none of the previous works have focused on automatic cortical cataract (CC) classification with such images. For this reason, this study proposes an automatic CC classification framework based on AS-OCT images, and it is composed of image preprocessing, feature extraction, feature screening, and classification. First, the reflective region removal and contrast enhancement methods are employed for image preprocessing. Next, 22 features are extracted from the cortical region by the gray level co-occurrence matrix (GLCM), grey level size zone matrix (GLSZM), and neighborhood gray-tone difference matrix (NGTDM) methods. Then, the Spearman correlation coefficient method is used to analyze the importance of the extracted features and screen out redundant ones. Finally, the linear support vector machine (linear-SVM) method is utilized for classification. The experimental results on a clinical AS-OCT image dataset show that the proposed CC classification framework achieves 86.04% accuracy, an 86.18% recall rate, 88.27% precision, and 86.35% F1-score respectively and obtains performance comparable to that of the advanced deep learning-based algorithm, indicating that it has the potential to be used as a tool to assist ophthalmologists in clinical CC diagnosis.

Abstract:In recent years, digital signal modulation recognition has gradually become an important line of research in the field of wireless communication owing to its high information confidentiality and anti-noise ability. As one of the important features of modulation recognition, the constellation diagram has obvious advantages in feature extraction because it does not need to receive prior information on the signal during feature extraction. For the above reasons, this study presents an overview of digital signal modulation methods based on the constellation diagram. In particular, it starts by analyzing the basic principle of the constellation diagram. Then, the characteristics of the constellation diagram in various lines of research are analyzed by summarizing the existing digital signal modulation recognition schemes based on the constellation diagram. Finally, the development trend and future expectations of digital modulation recognition schemes based on the constellation diagram are presented.

Abstract:As the detection result lacks interpretability, the Android malware detection is analyzed in terms of interpretability. This study proposes an interpretable Android malware detection method (multilayer perceptron attention method, MLP_At) comprehensively using the multilayer perceptron and attention mechanism. By extracting permissions and application programming interface (API) features from Android malware, it performs data preprocessing on the proposed features to generate feature information, and multilayer perceptrons are utilized for learning features. Finally, the learned data is classified by the BP algorithm. The attention mechanism is introduced in the multilayer perceptron to capture sensitive features and generate descriptions based on the sensitive features to explain the core malicious behavior of the application. The experimental results show that the proposed method can effectively detect malware and the accuracy is improved by 3.65%, 3.70%, and 2.93% compared with that of SVM, RF and XGBoost, respectively. The method can accurately reveal the malicious behavior of the software and can also explain the reasons why samples are misclassified.

Abstract:In vehicular named data network (VNDN), interest flooding attack (IFA) occupies or even exhausts network resources by sending a large number of malicious interest packets, which results in the failure to meet the requests of legitimate users and seriously endangers the operation safety of Internet of Vehicles (IoV). To solve the problems, this study proposes an IFA detection method based on traffic monitoring. Firstly, a distributed network traffic monitoring layer based on RSU is constructed, where each RSU monitors the network traffic within its communication range, and the communication interconnection between RSUs forms the RSU network traffic monitoring layer. Secondly, a fixed time window is set, and the network traffic in each window is analyzed from three dimensions, i.e., information entropy, network self-similarity, and singularity. Additionally, a new field is added to the interest packet, and thus information entropy can be used to reflect the distribution of interest packet sources. Finally, the above three indicators are comprehensively employed to judge the existence of attack. The simulation results indicate that the proposed method effectively improves the accuracy of IFA detection and reduces the misjudgment rate.

Abstract:Intelligent disinfection robots are a highly effective way of daily disinfection as it becomes regular. Robots usually perceive the surrounding environment through vision, but object detection based on supervised learning usually requires a large amount of labeled data for training. When the amount of labeled data is large, the cost of labeling is very high, and when the amount of labeled data is small, the model is prone to overfitting. Therefore, few-shot object detection is an effective solution. On the basis of the SimDet Model, this study proposes the SimDet+ model. First, according to the characteristics of the object detection task in a disinfection scene, the process of self-supervised pre-training is added. Second, as there are query images for reference, the classification layer is improved, where the cosine similarity instead of the fully connected layer is employed for confidence level calculation, and thus the overfitting phenomenon is effectively avoided through non-parametric calculation. For the disinfection scene, a 22-minute video dataset and a detection dataset containing eight categories of objects are produced and used in two stages separately for training. Through self-supervised pre-training, the cost of data labeling is effectively reduced, and the mAP of downstream tasks is increased from 0.216 2 to 0.530 2.

Abstract:The promotion of digital braille in the information age can help to improve the cultural quality and living standards of blind people in China. This study implements a Chinese-braille conversion system based on the national general braille (NGB) tone rules, which can quickly generate a large number of digital resources in line with the NGB rules and make visually impaired people obtain information without barriers. This system processes Chinese text according to the NGB tone rules and converts it into braille that conforms to tone rules and abbreviation rules. The test results show that the system can accurately process the tone rules and abbreviation rules and obtain accurate digital braille that is in line with the NGB tone rules. In addition, the coverage rate of tonal and final abbreviations and the increase in length are all comparable to the theoretical values of the NGB tone rules. The system can quickly process long-form corpus files and execute programs efficiently. Furthermore, it has practical value and can be used to promote the NGB and promote the barrier-free construction of digital braille in China.

Abstract:Given the problem that in the field of medical plastic surgery, customers can not directly grasp the postoperative effect of plastic surgery before the surgery, this study proposes a three-dimensional (3D) face reconstruction and editing system for medical plastic surgery. Specifically, the system marks the feature points of the pictures uploaded by the user, aligns the input images with the 3D morphable model (3DMM), and then inputs the processed images into the pre-trained 3D face reconstruction network to obtain the 3D face model corresponding to the input images. After the system loads and renders this model, the user can edit the cheeks, bridge of the nose, and chin of the model to simulate the plastic surgery, and he/she can also save the model to view the diagnosis results. Finally, the reliability of the reconstruction effect, plastic surgery effect, and diagnosis results are tested. The experimental results show that the system is effective in reconstructing young and middle-aged faces, offering reconstructed models highly similar to the input images. The parts of the model remain smooth and natural after the plastic surgery, which means the effect of simulating the plastic surgery is achieved. After the correct face size is determined, the plastic surgery recommendations suggested by the diagnosis results are at the mm level, indicating that the plastic surgery results are highly reliable.

Abstract:Whether the lung is infected by COVID-19 can be effectively detected from lung computed tomography (CT) images by the computer-aided diagnosis system whose training is based on deep learning. However, the main problem is the lack of high-quality labeled CT images available for training. This study proposes a method of augmenting lung CT images with the generative adversarial network (GAN). Specifically, labels of different infected areas are generated, and Poisson fusion is performed to enhance the diversity of the generated images. Then, image transformation and generation are implemented by training the GAN model to fulfill the purpose of augmenting the CT image. Segmentation experiments based on the augmented data are also carried out to verify the effectiveness of the data generated. The results of the image generation and segmentation experiments both show that the proposed image generation method achieves favorable effects.

Abstract:To solve the security problem of Hyperledger Fabric caused by the use of fixed endorsement nodes for endorsement, this study proposes an optimization scheme based on a verifiable delay function for the endorsement policy of Hyperledger Fabric. Considering that the verifiable delay function cannot be calculated in parallel but can be efficiently verified, a Fabric transaction model that anonymously and randomly selects endorsement nodes is designed to enhance the security of Fabric transaction endorsement. The experimental comparison between the optimized scheme and the original one verifies that the optimized scheme not only enhances security but also improves efficiency.

Abstract:To minimize the performance difference between neural machine translation (NMT) and human translation and solve the problem of insufficient training corpora, this study proposes an improved NMT method based on the generative adversarial network (GAN). First, the sentence sequence of the target end is added with small noise interference, and then the original sentence is restored by the encoder to form a new sequence. Secondly, the results of the encoder are presented to the discriminator and decoder for further processing. In the training process, the discriminator and the bilingual evaluation understudy (BLEU) objective function are employed to evaluate the generated sentences, and the results are fed back to the generator to instruct its learning and optimization. The experimental results demonstrate that compared with the traditional NMT model, the GAN-based model greatly improves the generalization ability and translation accuracy of the model.

Abstract:To realize the information-based management of geological archives, this study constructs an information system for geological archives, which is based on Spring Boot microservices architecture and is in systematic combination with the service Gateway and Consul registry. In the research and development process, the development mode of front and rear end separation is employed, and the main part of the front page is developed through Layui. By the Spring Boot framework, a back-end microservice example is built. With both the relational database MySQL and non-relational database Redis as the storage carrier of system data, functional modules are established, such as user management, archive warehousing, archive lending and return, and OCR image recognition. In this system, the geological archives are stored electronically, which promotes the sharing and unified transfer of resources, reduces the maintenance workload of personnel, improves work efficiency, and provides a reference for the data fusion of geological archive information.

Abstract:The current deep learning models in the field of compilation optimization generally perform single-task learning and fail to use the correlation among multiple tasks to improve their overall compilation acceleration effect. For this reason, a compilation optimization method based on multi-task deep learning is proposed. This method uses the graph neural network (GNN) to learn program features from the abstract syntax trees (ASTs) and control data flow graphs (CDFGs) of the C program and then predicts the initiation interval and loop unrolling factor for the software pipelining of the HX digital signal processor (HXDSP) synchronously according to program features. Experimental results on the DSPStone dataset show that the proposed multi-task method achieves a performance improvement of 12% compared with that of the single-task method.

Abstract:In order to quickly drive accident vehicles away from the scene and ensure a clear road during a minor traffic accident, this study proposes a vehicle collision detection and liability determination model. First, the study combines the SSD (single shot multibox detector) target detection algorithm and the MobileNet lightweight deep network model to make improvements and obtain the position and size information of the moving target in each frame of video images, so as to identify and detect the vehicle. Secondly, the study employs a Kalman filter to establish a corresponding matching relationship between moving targets in consecutive image frames, predict their motion states, and judge their positions and motion trend, in a bid to track the vehicle. Then, the study determines whether there is a collision by the intersection over union of the vehicle target detection frame. Finally, according to the speed and direction information of the vehicle on a straight road, the liability of the accident vehicle is determined under the road safety regulations and the fast method of motor vehicle accidents. The results show that the research can help to detect and determine the liability during vehicle collisions caused by rear-end collisions and lane changes on straight roads.

Abstract:In the application scenario of autonomous driving, YOLOv5 is applied to target detection, and the performance is significantly improved compared with that of previous versions. However, the detection accuracy is still low in the case of high running speed. This study proposes a vehicle-side target detection method based on improved YOLOv5. In order to address the issue of manually designing the initial anchor box size in training different datasets, an adaptive anchor box calculation is introduced. In addition, a squeeze and excitation (SE) module is added to the backbone network to screen the feature information for channels and improve the feature expression ability. In order to improve the accuracy of detecting objects of different sizes, the attention mechanism is integrated with the detection network, and the convolutional block attention module (CBAM) is integrated with the Neck part. As a result, the model can focus on important features when detecting objects of different sizes, and its ability in feature extraction is improved. The spatial pyramid pooling (SPP) module is used in the backbone network so that the model can input any image aspect ratio and size. In terms of the activation function, the Hardswish activation function is adopted for the entire network model after the convolution operation. In terms of the loss function, CIoU is used as the loss function of detection box regression to solve the problems of low positioning accuracy and slow regression of the target detection box during training. Experimental results show that the improved detection model is tested on the KITTI 2D dataset, and the precision of target detection, the recall rate, and the mean average precision (mAP) are increased by 2.5%, 5.1%, and 2.3%, respectively.

Abstract:Considering the problems of low segmentation efficiency of traditional image segmentation methods, complex and diverse features of remote sensing images, and limited segmentation performance in complex scenes, an improved U-Net model is proposed on the basis of the U-Net network architecture, which can satisfactorily extract the features of remote sensing images while maintaining efficiency. First, EfficientNetV2 is used as the encoding network of U-Net to enhance the feature extraction ability and improve the training and inference efficiency. Then, the convolutional structural re-parameterization method is applied in the decoding network and is combined with the channel attention mechanism to improve the network performance without increasing the inference time. Finally, the multi-scale convolution fusion module is employed to improve the feature extraction ability of the network for objects with different scales and the utilization of context information. The experiments reveal that the improved network can not only improve the segmentation performance of remote sensing images but also promote segmentation efficiency.

Abstract:Given the poor robustness and low security of traditional watermark-based copyright protection of digital images, a zero watermarking algorithm based on multiple features and chaotic encryption is proposed to improve the differentiability of the zero watermarks of different images. Specifically, global and local perspectives are utilized to extract the five-dimensional features of the image, including its mean feature, variance feature, skewness feature, kurtosis feature, and histogram of oriented gradients (HOG) feature. Then, the watermarked image is encrypted by the proposed block scrambling method based on chaotic mapping. Finally, zero watermarking information is constructed with the multiple features extracted and the scrambled watermarks. The copyright authentication process starts by extracting the multiple features. Then, the encrypted watermarks are obtained according to the zero watermarking information and decrypted, thereby achieving copyright authentication. The experimental results show that the proposed method has high efficiency, high security, and strong anti-attack capability. Integrating various properties of the digital image as the features, the proposed zero watermarking algorithm based on multiple features and chaotic encryption is stable and more robust. In addition, the proposed block scrambling method based on chaotic mapping improves the security of the watermarked images. The proposed algorithm and method effectively solve the problems of the poor robustness and low security of image watermarks.

Abstract:A lightweight object detection network YOLOv5-tiny is given on the basis of YOLOv5 for real-time target detection tasks in drone-captured scenarios. The replacement of the original backbone network CSPDarknet53 with MobileNetv3 reduces the parameters of the network model and substantially improves the detection speed. Furthermore, the detection accuracy is improved by the introduction of the CBAM attention module and the SiLU activation function. With the characteristics of the aerial photography task dataset VisDrone, the anchor size is optimized, and data augmentation methods such as Mosaic and Gaussian blur are used to further improve the detection effect. Compared with the results of the YOLOv5-large network, the detection efficiency (FPS) is improved by 148% at the expense of a 17.4% reduction in mAP. Moreover, the network size is only 60% of that of YOLOv5 when the detection results are slightly superior.

Abstract:With the rapid development of high-tech with each passing day, the cross fusion and deep correlation among the Internet of Things, big data, and artificial intelligence are implemented. The Internet of Things is fully integrated into all aspects of our life and work as well as social development. At present, the most widely used and mainstream protocol of the Internet of Things is the message queuing telemetry transport (MQTT) protocol, whose inherent advantages of low overhead and low bandwidth have contributed to the access of a large number of Internet of Things devices to the network. However, in the era of the Internet of Everything, “freedom, controllability, safety, and credibility” are the concepts and criteria of industrial development. Many researchers have proposed MQTT-based design schemes for security algorithms. Regarding the paper titled “Data encryption transmission algorithm Based on MQTT”, however, its core algorithm is found to be at risk of key leakage. Therefore, this study points out the defects of this core algorithm and proposes three MQTT-SE algorithms respectively based on symmetric encryption, public key, and mutual verification of public key certificates. These algorithms can achieve the purpose of high-performance and safe encryption transmission even in a low performance MQTT transmission environment.

Abstract:The existing news recommendation system fails to sufficiently consider the semantic information of news, and modeling factors for news body suffers from unity problems. Attention-BodyTitleEvent (Attention-BTE), a news recommendation algorithm based on fusion of attention and multi-perspectives, is proposed in this study. The BERT model and attention mechanism are applied to vectorize the body, title, and event in the news respectively. The three parts are combined to represent news vectorization, and then the candidate news and user browsing news data are processed respectively to obtain the corresponding candidate news vectorization and user vectorization. Finally, dot multiplication is conducted to obtain the probability of users clicking on the candidate news, namely the news recommendation result. Experimental data demonstrate that Attention-BTE improves the index by about 6% compared with the other news recommendation algorithm.

Abstract:Given the problem that no image datasets of defective cloth with defect location information are available for the training of the automatic detection model for cloth defects in industrial production, this study proposes an image generation model EC-PConv with defect location information for defective cloth, and it uses an improved partial convolutional network as its basic framework. This model adopts a feature extraction network for small-scale defects, splices the extracted defect texture features with the blank mask to obtain a mask with position information and defect texture features, and generates an image with defect position information in a repaired way for the defective cloth. Furthermore, a hybrid loss function integrating the mean squared error (MSE) loss is proposed to generate clearer defect textures. The experimental results show that compared with the latest generative adversarial network (GAN) generation model, the proposed model reduces the Frechet inception distance (FID) score by 0.51 and improves the precision P and mean average precision (MAP) values of the generated image of the defective cloth in the cloth defect detection model by 0.118 and 0.106, respectively. This method is more stable than other algorithms in generating images of defective cloth and can generate images of defective cloth that contain defect location information and have higher quality. Therefore, it can effectively solve the problem that no training datasets are available for the automatic detection model for cloth defects.

Abstract:Vehicle detection is an important research direction for intelligent transportation systems. In terms of vehicle detection from the monitoring perspective, a vehicle detection method based on an improved YOLOX algorithm is proposed. The YOLOX_S model with a smaller network depth is used to improve the network structure. The GHOST depthwise separable convolution module is adopted to replace some traditional convolutions, and model parameters are reduced with the model detection accuracy ensured. The CBAM attention module is integrated into a feature extraction network, and a feature enhancement structure is added to enhance the semantic information of feature maps obtained by the network and strengthen the ability of the network in detecting targets. By using the CIoU_loss to optimize the loss function, this study finds that the positioning accuracy of the bounding box of the model is improved. The test results show that the detection accuracy of the improved network is increased by 2.01%, reaching 95.45%, which proves the feasibility of the improved method.

Abstract:Object images in the real world often have large intra-class variations, and thus using a single prototype to describe an entire category will lead to semantic ambiguity. Considering this, a multi-prototype generation module based on superpixels is proposed, which uses multiple prototypes to represent different semantic regions of objects and employs the context to correct prototypes among the generated prototypes by a graph neural network to ensure the orthogonality of the sub-prototypes. To obtain a more accurate prototype representation, a Transformer-based semantic alignment module is designed to mine the semantic information contained in the features of the query images and the background features of the supporting images. In addition, a multi-scale feature fusion structure is proposed to instruct the model to focus on features that appear in both the supporting images and the query images, which can improve the robustness to changes in object scales. The proposed model is tested on the PASCAL-5ⁱ dataset, and the mean intersection over union (mIoU) is improved by 6% compared with that of the baseline model.

Abstract:Relevant information of railway accidents, existing in the form of accident overview texts, is of great significance to railway safety work. However, due to the lack of effective information extraction methods, the knowledge of railway accidents scattered in the texts has not been fully utilized. Named entity recognition is an important subtask of information extraction, and there are few studies on named entity recognition of accidents. A named entity recognition model fused with character position features is proposed for the named entity recognition of railway accidents. The model obtains the character position features through a fully connected neural network. It merges them with the character vectors at the semantic level as the final vector representation of the characters, which is then input to the BiLSTM-CRF model to obtain the optimal label sequence. The experimental results show that the accuracy, recall, and F1 value of the model on the named entity recognition of railway accident texts are 93.29%, 94.77%, and 94.02% respectively. This model yields better effects than traditional models and lays a foundation for the construction of a railway accident knowledge graph.

Abstract:Considering the insufficient feature extraction in hyperspectral remote sensing image classification under limited training samples, a multi-scale 3D capsule network is proposed to improve hyperspectral image classification. Compared with the traditional convolutional neural network, the proposed network is equivariant, and its input and output forms are neurons in the form of vectors rather than scalar values in the convolutional neural network. It is conducive to obtaining the spatial relationship between objects and the correlation between features and can avoid problems such as overfitting under limited training samples. Specifically, the network extracts the features of an input image through the convolution kernel operation on three scales to obtain the features of different scales. Then, the three branches are connected to different 3D capsule networks to obtain the correlation between spatial spectrum features. Finally, the results of the three branches are fused, and the classification results are obtained by the local connection and margin loss function. The experimental results reveal that this method has good generalization performance on the open-source hyperspectral remote sensing data set and has higher classification accuracy than other advanced hyperspectral remote sensing image classification methods.

Abstract:Block diagonalization (BD) belongs to a traditional linear precoding algorithm with multiple inputs and outputs, and its core idea is to find the orthogonal basis of the null space in interference matrixes through singular value decomposition (SVD), so as to eliminate the multiuser interference (MUI). However, as the number of transmitters and receivers increases, the BD precoding algorithm faces more complex computation, which has become one of the key factors restricting its development. Therefore, this study proposes an optimal low-complexity BD algorithm. The algorithm is based on the combination algorithm of Schmidt orthogonalization inversion and lattice reduction operation in orthogonal decomposition, and it replaces the SVD of two high-complexity operations on the traditional BD algorithm by Schmidt orthogonalization inversion and lattice reduction operation and thus reduces the algorithm complexity. The results show that the computational complexity of the optimal algorithm is reduced by 46.7%, and the system and capacity are increased by 2–10 bits/Hz. Furthermore, the bit error rate is improved by two orders of magnitude.

Abstract:The mining of network public opinion information is an important subject of public opinion research. To quickly discover useful public opinion information among a large amount of information and support public opinion analysis and corresponding decision-making, this study proposes a method of ranking the usefulness of public opinion information for specific viewpoints to achieve the purpose of quickly discovering useful public opinion information under specific viewpoints. This method analyzes and calculates the specific viewpoints of public opinion information and assigns scores to the information by a ranking method according to its credibility and attention and the influence of the disseminator with due consideration of the timeliness of the information and other factors. Then, the public information is ranked in terms of usefulness according to its score. The experimental results show that the proposed method performs well in the recommendation ranking of public opinion information. This research theoretically supplements the research theory of public opinion information mining. Concerning practical significance, it can well assist public opinion managers as it provides a boost to the guidance of network public opinions.

Abstract:In this study, an improved model based on you only look once version 5 (YOLOv5) is proposed to solve the problem of difficult detection of small targets in images. In the backbone network, a convolutional block attention module (CBAM) is added to enhance the network feature extraction ability. As for the neck network, the bi-directional feature pyramid network (BiFPN) structure is used to replace the path aggregation network (PANet) structure and thereby strengthen the utilization of low-level features. Regarding the detection head, a high-resolution detection head is added to improve the ability of small target detection. A number of comparative experiments are conducted, respectively, on a facial blemish dataset and an unmanned aerial vehicle (UAV) dataset VisDrone2019. The results show that the proposed algorithm can effectively detect small targets.

Abstract:The variable scales of objects and the use of feature fusion have been the challenges for popular object detection algorithms. Considering the problems, this study proposes a multi-path feature fusion module, which strengthens the connection between input and output features and alleviates the dilution of feature information in transmission by adopting cross-scale and cross-path feature fusion. Meanwhile, the study also proposes a scale-aware module by refining the attention model, which allows the model to easily recognize multi-scale objects by selecting the size of the receptive field corresponding to the scale of the objects independently. After the scale-aware module is embedded into the multi-path feature fusion module, the feature extraction and utilization abilities of the model are improved. The experimental results reveal that the proposed method achieves 82.2 mAP and 38.0 AP on PASCAL VOC and MS COCO datasets, respectively, an improvement of 1.3 mAP and 0.6 AP over the baseline FPN Faster RCNN, respectively, with the most significant improvement in detection of small-scale objects.

Abstract:Map matching is the process of mapping the original global positioning system (GPS) trajectory data of vehicles into the actual road network, and retrieving candidate road sections for GPS trajectory points is the primary link of this process. However, retrieval methods directly affect the accuracy and efficiency of map matching. In this study, a road section retrieval method based on the floating grid is proposed for GPS trajectory data sampled at a low frequency in an urban road network environment. This method resorts to GeoHash grid encoding and floating GeoHash grid to retrieve candidate road sections for trajectory points. Then, to verify the feasibility of the method, this study applies the hidden Markov model, the incremental method, and the Viterbi algorithm to calculate the local optimal solution, with due consideration of the topological structure of the road network and the time-space constraints on the trajectory. Finally, the greedy strategy is employed to obtain the global optimal matching path from the local optimal solution through successive extension.

Abstract:In many practical applications of data mining, instances for each cluster are often required to be balanced in number. However, the entropy-weighted K-means algorithm (EWKM) for independent subspace clustering leads to unbalanced partitioning and poor clustering quality. Therefore, this study defines a multi-objective entropy that takes balanced partitioning and feature distribution into account and then employs the entropy to improve the objective function of the EWKM algorithm. Furthermore, the study designs the solution process by using the iterative method and alternating direction method of multipliers and proposes the entropy-based balanced subspace K-means algorithm (EBSKM). Finally, the clustering experiments are conducted in public datasets such as UCI and UCR, and the results show that the proposed algorithm outperforms similar algorithms in terms of accuracy and balance.

Abstract:This study aims to detect traffic signs accurately and in real time, reduce traffic accidents, and promote intelligent transportation. An improved YOLOv5s detection algorithm, YOLOv5s-GC, based on computer vision technology is designed to solve the problems of insufficient accuracy, large weight files, and slow detection speed of existing detection models for road traffic signs. Firstly, data is enhanced by copy-paste and then sent to the network for training to improve the detection ability of small targets. Then, Ghost is introduced to build the network, reducing the parameters and calculation amount of the original network, and realizing a lightweight network. Finally, the coordinate attention mechanism is added to the backbone network to enhance the representation and positioning of the attention target and improve detection accuracy. The experimental results show that in comparison with the YOLOv5s, the number of parameters of the YOLOv5s-GC network model is reduced by 12%; the detection speed is increased by 22%; the average accuracy reaches 94.2%. The YOLOv5s-GC model is easy to deploy and can meet the speed and accuracy requirements of traffic sign recognition in actual autonomous driving scenarios.

Abstract:A human-machine collaborative classification model based on the deep forest is proposed to solve the problems encountered in the classification of defects in industrial products, such as sample image shortage, insufficient classification precision, and time-consuming model training. For this purpose, sample images are preliminarily identified with deep forest, and their features are extracted by the multi-granularity scanning module and the cascaded forest module. The initial estimation result is thereby obtained, and sample images difficult to identify are separated out. Then, the human-machine collaboration strategy is employed. Specifically, some of the sample images difficult to identify are randomly labeled manually, and the remaining ones are reclassified with the K-nearest neighbor algorithm. The experimental results on the public dataset and the real data collected from the production line indicate that the improved classification model offers performance superior to that of the baseline algorithm on the dataset of industrial product surface defects.

Abstract:With the improvement of urban residents’ consciousness of green and low-carbon travel, ride-sharing travel of online car-hailing emerged at the historic moment. However, due to the driving route issue involved in the ride-sharing mode, differences between the passengers and between the passengers and the driver are highly likely to occur. Moreover, the costs of this ride-sharing travel mode remain to be clarified. For the above-mentioned and various other reasons, the ride-sharing mode has not been widely promoted and applied. To address the problems with this travel mode, this study constructs a route optimization model for online car-hailing ride-sharing. and the model takes into account the cost of waiting time, that of driving distance, benefit, capacity constraint, time window constraint, etc. According to the characteristics of the ride-sharing model, a solving genetic algorithm satisfying the constraints on the ride-sharing model is designed by drawing on the genetic algorithm. Matlab software is used to run the algorithm program and thereby solve the calculation example, and a maximum profit of 6 906.297 1 CNY and the detailed driving route for the vehicle are obtained after the program is run 44.08 s. The experiment shows that an approximate optimal solution for the ride-sharing route can be obtained by the ride-sharing model of online car-hailing constructed and the genetic algorithm designed, which proves the feasibility and effectiveness of the proposed model and algorithm.

Abstract:In federated learning, due to barriers such as industry competition and privacy protection, users keep data locally and cannot train models in a centralized manner. Users can train models cooperatively through the central server to fully utilize their data and computing power, and they can share the common model obtained by training. However, the common model produces the same output for different users, so it cannot be readily applied to the common situation where users’ data are heterogeneous. To solve this problem, this study proposes a new algorithm based on the meta-learning method Reptile to learn personalized federated learning models for users. Reptile can learn the initial parameters of models efficiently for multi-tasks. When a new task arrives, only a few steps of gradient descent are needed for convergence to satisfactory model parameters. This advantage is leveraged, and Reptile is combined with federated averaging (FedAvg). The user terminal uses Reptile to process multi-tasks and update parameters. After that, the central server performs the averaging aggregation of the parameters the user updates and iteratively learns better initial parameters of the model. Finally, after the proposed algorithm is applied to each user’s data, personalized models can be obtained by only a few steps of gradient descent. In the experiment, this study uses simulated data and real data to set up federated learning scenarios. The experiment shows that the proposed algorithm can converge faster and offer a better personalized learning ability than other algorithms.

Abstract:Docker image is the operating basis of Docker containers. As robust methods of image security detection remain to be developed, containers are subject to various security threats, such as container escape and denial of service attacks, during their operation. To avoid the use of toxic images, this study proposes a detection model for trusted Docker image sources, namely detect trusted Docker image source (DTDIS). In this model, the virtual trusted cryptography module (vTCM) is used to build an image benchmark database and thereby detect whether the local image file has been tampered with. The parent image vulnerability database is utilized to extend the Clair image scanner and thus avoid repeated scanning. File measurement information and vulnerability scanning information are availed to determine whether the Docker image source is credible. Experiments in a cloud environment prove that the proposed model can effectively evaluate the security of Docker images and ensure that users use trusted images.

Abstract:Underwater robots with vision systems cannot operate without the accurate segmentation of underwater objects, but the complex underwater environment and low scene perception and recognition accuracy will seriously affect the performance of object segmentation algorithms. To solve this problem, this study proposes a multi-object segmentation algorithm combining YOLOv5 and FCN-DenseNet, with FCN-DenseNet as the main segmentation framework and YOLOv5 as the object detection framework. In this algorithm, YOLOv5 is employed to detect the locations of objects of each category, and FCN-DenseNet semantic segmentation networks for different categories are input to achieve multi-branch and single-object semantic segmentation. Finally, multi-object semantic segmentation is achieved by the fusion of the segmentation results. In addition, the proposed algorithm is compared with two classical semantic segmentation algorithms, namely, PSPNet and FCN-DenseNet, on the seabed image data set of the Kaggle competition platform. The results demonstrate that compared with PSPNet, the proposed multi-object image semantic segmentation algorithm is improved by 14.9% and 11.6% in MIoU and IoU, respectively. Compared with the results of FCN-DenseNet, MIoU and IoU are improved by 8% and 7.7%, respectively, which means the proposed algorithm is more suitable for underwater image segmentation.

Abstract:Trucks cannot be accurately identified when they do not follow the prescribed time and route on urban roads by avoiding cameras and other means. In view of this, an urban road truck detection method based on improved Faster RCNN is proposed. Features are extracted by performing convolution and pooling operations on the vehicle images passed into the backbone network. The feature pyramid network (FPN) is added to improve the accuracy of multi-scale target detection. At the same time, the K-means clustering algorithm is applied to the dataset to obtain new anchor boxes. Region proposal network (RPN) is utilized to generate proposal boxes and complete-IoU (CIoU) loss function is used for replacing the smoothL1 loss function of the original algorithm to improve the accuracy of vehicle detection. The experimental results show that the improved Faster RCNN increases the average precision (AP) for truck detection by 7.2% and the recall by 6.1%. The improved method reduces the possibility of missed detection and has a good detection effect in different scenarios.

Abstract:An improved SURF-based image matching algorithm is proposed for the traditional SURF image matching algorithm which has the problems of complex computing data, time-consuming, and poor matching accuracy. Firstly, the traditional SURF algorithm is employed to extract the feature points of the image to be matched, and then the 64-dimensional descriptor of SURF is reduced to 20 dimensions by replacing the rectangular area with a circular area. Secondly, the KNN algorithm is utilized to bidirectionally match the feature points of the image to be matched, and the matching pair set of bidirectional initial feature points is obtained. Finally, the mismatching pairs of initial matching points are eliminated bidirectionally by the RANSAC algorithm. The experimental results show that the proposed algorithm reduces the detection time, improves the matching accuracy, and has strong robustness.

Abstract:Given the various problems of the cultural algorithm, such as slow convergence speed, high likeliness to fall into local optimum, and low population diversity, this study optimizes the design of the cultural algorithm and proposes a hybrid optimization algorithm that incorporates a genetic algorithm (GA) with an elite retention strategy and a simulated annealing (SA) algorithm into the framework of the cultural algorithm (CA). In light of the idea of co-evolution, this algorithm is divided into a lower population space and an upper belief space that share the same evolutionary mechanism but use different parameters. On the basis of the CA, a GA with an elite retention strategy is added so that the outstanding individuals in the population can directly enter the next generation to improve the convergence speed. An SA algorithm is added as its mutation characteristics can be leveraged to enable the algorithm to probabilistically jump out of the local optimum and accept inferior solutions and thereby increase population diversity. The function optimization results prove the effectiveness of the proposed algorithm. This algorithm is applied to solve the flow shop scheduling problem of minimizing the maximum completion time. The simulation results show that the proposed algorithm is superior to several other representative algorithms in convergence speed and accuracy.

Abstract:Recycling and remanufacturing industrial products is conducive to reducing production costs and protecting the environment. It is very important to make excellent equipment disassembly sequence planning to improve disassembly efficiency and reduce recovery costs. For the factors of equipment recycling in actual disassembly, a disassembly sequence planning model based on a discrete whale optimization algorithm (DWOA) is proposed in this study. The objective function of the model employs the position change cost as the new evaluation indicator and adopts the stratified combination method to rapidly generate the initial population. DWOA features the precedence preservative crossover mechanism, heuristic mutation, and better global and local search ability. Comparative experiments are conducted with recycled upper rubber plate and standstill seal to test the feasibility of the proposed algorithm. The experimental results show that at the same time, the algorithm stability, optimization ability, and convergence speed of DWOA are better than those of other algorithms.

Abstract:Convolutional neural networks have been widely used in SAR target recognition. However, due to the small number of target samples in SAR images and coherent speckle noise in images, the networks cannot fully learn the deep features of samples, which exerts a certain impact on the recognition performance of the networks. To address the above problems, this study proposes a data fusion-based target recognition method. The algorithm firstly suppresses noise and extracts edge information of the original image and then fuses the processed two types of feature information. It expands the single-channel grey-scale image fusion to a two-channel image as the training sample and constructs a convolutional neural network model with high- and low-layer features fused, which uses the attention mechanism to enhance the learning of useful features. The experimental results reveal that the method demonstrates excellent performance in the recognition of different target models on the MSTAR dataset.

Abstract:With the rapid increase in the number of Android applications, more importance is attached to the quality of Android applications. Testing is an important guarantee for high-quality software, and test case generation technology is the key to automated testing. Data shows that nearly 88% of Android applications in Google Play use reflection. The existing automatic test case generation methods for Android, however, usually do not consider the use of reflection and cannot detect the malicious behavior hidden by reflection. To further improve software quality, this study proposes a new test case generation method for Android, which uses reflection features to construct a multi-grain model of Android applications. Meanwhile, it analyzes reflection relationships to generate call paths that can reach reflection and employs the adaptive genetic algorithm to generate test cases that cover reflection paths to test Android applications with reflection features. For verification, the effectiveness of this method is evaluated in terms of the effectiveness of the multi-grain model of Android applications and the efficiency of the test method. The experimental results reveal that the automatic test case generation method for Android, which is based on reflection features, is more effective and efficient in detecting reflection.

Abstract:The network information-centric system of systems (SoS) is a new generation of command-and-control operational SoS proposed by the PLA, which has the advantage of dynamic response to missions and environmental changes. It optimizes the operational resources of the whole network to maximize operational effectiveness. With the development of artificial intelligence and other technologies, the current optimization method, which mainly depends on the implementation of plans, can neither adapt to the self-evolution of intelligent and unmanned equipment nor cover the battlefield dynamics. Considering the above defects, this study takes the air and missile defense operational SoS as an example to study the solution to the resource integration scheme in the case of physical node damage. The down-selection model is adopted to transform the solution to the resource integration scheme into a combinatorial optimization problem, and the formation mechanism of initial evolutionary strategy is improved by adding disturbance restrictions. Thus, a resource optimization method based on the evolutionary game is proposed. The effectiveness of the method is verified by simulations on the Netlogo platform. Compared with the result of the resource optimization method based on the genetic algorithm, the task completion of the solution by the proposed method is increased by 6.4% on average.

Abstract:Currently, patients are transported mainly by fuel vehicles. In view of this, this study carries out a study to model patient transportation by electric vehicles and analyzes the calculation examples of patient transportation by fuel and electric vehicles through comparison, so as to verify the feasibility and superiority of patient transportation by electric vehicles. Firstly, a mathematical model of patient transportation by fuel vehicles is constructed, which considers constraints such as the longest riding time of each patient, the maximum average speed of vehicles, and the time window for patients, with a goal of minimizing the sum of consumption and refueling costs of fuel vehicles. Secondly, a mathematical model of patient transportation by electric vehicles is constructed, which takes constraints such as the charging time of electric vehicles, the remaining power, the maximum average speed of electric vehicles, the longest riding time of each patient, and the time window for patients into account, with a goal of minimizing the sum of consumption and charging costs of electric vehicles. Finally, an example is selected and solved by LINGO software through programming to verify the feasibility and effectiveness of the mathematical models.

Abstract:In the problem of predicting disease scores amid the protection of multi-source domain data considering user privacy, the decentralized data from different source domains cannot be combined and may follow different distributions. Therefore, traditional machine learning methods cannot be applied directly to utilize the information within source domains. In this study, the federated importance weighting method is proposed combining the idea of federated learning and the sample-based transfer learning approach. By re-weighting the samples from the source domains to the prediction task of the target domain, and without data sharing between multiple source domains, it realizes the use of data with different distributions while protecting the data privacy of the source domains. Moreover, this study constructs a weighted model and provides a concise and general algorithm to solve the prediction model for the target domain. Numerical simulation and empirical results show that, compared with the traditional method without considering distribution shift, the federated importance weighting method can effectively utilize the information of the source domain data. It is superior in prediction accuracy of the target domain and can make an accurate prediction of disease scores in the Parkinson’s disease data.

Abstract:In the past half-century, with the development of computer technology, neural networks have been widely used in many fields such as images, speeches, and decision-making. To improve the accuracy of neural networks, different scholars have designed a large number of network structures, and thus neural networks have become more and more complex and multi-parametric. As a result, the training process of neural networks has strong non-convexity, and different initial parameters of the same network often train different models. To more accurately describe the performance of two networks, predecessors proposed to evaluate the distribution of the performance of different random seeds on different models trained by the same network through the statistical method of stochastic dominance. On this basis, this study believes that the distribution of the performance of different models on different samples in a test set is also worthy of attention, and thus the stochastic dominance method is applied to compare the distribution of the performance of different models on different samples. Through the experiments on the networks applied in image segmentation, this study finds that for the two models trained by different networks, although one of them has certain advantages in the performance score, it may show stronger dispersion on different samples in the test set. The practical application, however, requires the neural network model with a better performance score and dispersion as small as possible. The stochastic dominance method can effectively compare different models for the selection of a more suitable one.

Abstract:The spatial-temporal distribution of crowds as well as evacuation safety and efficiency are impacted by the layout of indoor obstacles. To investigate the effect, a crowd evacuation model for a single room with a single exit and obstacles is proposed in this study. Three different influencing factors (i.e., obstacle length, the distance between an obstacle and the exit, and the distance between the obstacle center and exit center) are used to analyze their impacts on evacuation efficiency and safety. The experimental results reveal that the obstacle length is directly proportional to evacuation efficiency and is inversely proportional to evacuation safety. The evacuation efficiency and safety are directly proportional to the distance between the obstacle and the exit and are inversely proportional to the distance between the obstacle center and exit center. Additionally, a multi-objective evolutionary algorithm is used to optimize the layout of the indoor obstacles, and the obtained results can provide an important reference for decision-makers to balance evacuation efficiency and safety.

Abstract:There are numerous methods of network attacks, such as man-in-the-middle attacks, replay attacks, and DoS attacks, which are ways to gain improper benefits. The authentication and key agreement (AKA) is set up to provide a correct authentication portal for legitimate users and deny illegal access and attacks from attackers. AKA is the first line of security to protect mobile communications for higher quality of service. The AKA for 5G networks still has security problems in the actual environment, and it is still unknown whether the security features of AKA can meet the requirements. Therefore, this study proposes to use the method based on probabilistic model checking to build a discrete-time Markov chain model by modeling each protocol party entity of AKA for 5G networks. In the modeling process, the influence of external attacks is considered, and the attack rate is introduced to describe the degree of external influence. The studies of AKA for 5G networks are quantitatively analyzed through the attack rate, and the probabilistic computation tree logic is employed to describe the codes of the specifications for the a priori attributes. Experiments are conducted by the probabilistic model checking tool PRISM. The experimental results indicate that in the AKA model with the introduction of the attack rate, the attacks on each protocol party entity of AKA for 5G networks have different influences on the performance of the attribute specifications such as delay, validity, and confidentiality of the protocol. Therefore, the study of the impact of external network attacks on the security performance of the protocol has certain implications for strengthening the security performance of the protocol and its improvement, and it is of great significance to enhance the security features of AKA for 5G networks and protect the economic and information security of users.

Abstract:Focusing on multi-attribute group decision-making with interval-valued intuitionistic fuzzy numbers as the attribute values, this study uses the grey correlation coefficient method and the information entropy theory to determine the weights of experts and attributes and builds a comprehensive evaluation cloud model through information aggregation. For this purpose, the influences of fuzziness and randomness on the process and results of group decision-making are taken into account, and the cloud model theory and the characteristics of interval-valued intuitionistic fuzzy numbers are leveraged. Different from the traditional ranking method for interval-valued intuitionistic fuzzy numbers, this study uses the 3En rule of the cloud model to convert interval-valued intuitionistic fuzzy numbers into the cloud and determines the comprehensive evaluation value and hesitation degree of the scheme through cloud similarity. Then, the decision-making schemes are comparatively analyzed. The results show that the proposed method can make decisions scientifically and effectively and thereby provide a scientific basis for decision-makers.

Abstract:Considering that shadows caused by changes in lighting are difficult to identify and segment for intelligent surveillance videos in indoor environments, this study proposes a UNet network combining the transfer learning method and the SENet channel attention mechanism. Specifically, because shadow features are blurry and difficult to extract effectively, the SENet channel attention mechanism is added to the upsampling part of the UNet model to improve the feature weight of the effective area without increasing the network parameters. A pre-trained VGG16 network is then migrated into the UNet model to achieve feature migration and parameter sharing, improve the generalization ability of the model, and reduce training costs. Finally, the segmentation result is obtained by a decoder. The experimental results show that compared with the original UNet algorithm, the improved UNet algorithm offers significantly enhanced performance indicators, with its segmentation accuracy on moving objects and shadows respectively reaching 96.09% and 92.24% and a mean intersection-over-union (MIOU) of 92.58%.