Reviewerly/exHarmony huggingface dataset:
Global Semantic Information Extraction Model for Chinese long text classification based on fine-tune BERT

Since Bidirectional Encoder Representation from Transformers (BERT) was proposed, BERT has obtained new state-of-the-art results in 11 Natural Language Processing (NLP) tasks and is the most advanced embedding model available. However, the pre-trained BERT model can process the maximum text sequence length is 512. Usually, people use text truncation method to make the sequence length match the preset value. But this processing can result in the loss of global information and lead to errors. In order to solve the above problem, we use the Long Short-Term Memory (LSTM) model on top of the BERT model for secondary extraction of features, while using the attention mechanism to optimize global features. This is our proposed BERTLSTMATT model. The experiment results on THUCNews dataset show that our model has better classification performance than other models.
Using Open Source Word Vector Corpus to Obtain Synonyms of Library Information

The synonyms of domain-specific terms are of value to information services in that domain, such as information retrieval, question answering, etc. The method of obtaining synonyms entirely by manual annotation is costly and inefficient. Using the open source word vector corpus, through word similarity calculation, the similar words of the term word are automatically obtained, and then manual screening can be performed to obtain synonyms efficiently and at low cost. Experiments in the field of library and information show that this method can quickly and effectively obtain similar words of a large number of domain-specific terms.
Relation extraction method in military domain based on meta learning

Relation Extraction (RE) aims to extract relation facts from unstructured text is crucial for constructing knowledge graph. Aiming at the sparse and expensive data of relation extraction in military domain, and the gradual drift problem of introducing semi-supervised learning, this paper proposes a method of relation extraction in military domain based on meta learning. The BERT model is used to encode sentences and syntactic dependency is used to enhance sentence-level semantics. A meta learning framework is used to filter false labels to achieve label level noise reduction and make full use of unlabeled data. Experiments were carried out on self-built dataset in the military domain and open-source Chinese-Literature-NER-RE-Dataset data sets. The experimental results show that the method proposed in this paper is superior than the current mainstream method, which proves the leading role and generalization of the model. However, there is room for further improvement on overlapping triplet problem in the military domain.
Hand Pose Estimation from Depth Image Based on CPM

Hand posture estimation using RGB images is often ineffective due to the relative position of hands and the background light. Depth picture contains the distance information of an object from the viewpoint, which can more accurately reflect the relative position of the object in the picture. In this paper, we first locate the hand position in the figure by centroid detection method, and then use Convolutional Pose Machines (CPMs) based on the residual network (ResNet) to estimate the key point position and perform pose mapping to complete the 3D reconstruction of the hand pose. Finally, a low-cost Kinectv2 camera is used for real-time detection, and the results demonstrate that the model has good real-time performance and accuracy for hand pose estimation in depth images. The average endpoint error (EPE) is 7.37mm and 10.3mm for the TCVL and MSRA datasets, respectively. In the 3D PCK test, the probability of correctly estimating the key point can reach 98% and 95% or more when the threshold is around 30mm (different thresholds for different test sets), which is superior to other mainstream algorithms.
Backdoor Attack of DNN Models Based on Structure Modification Using Steganography

In the backdoor attack methods of Deep Neural Network Model based on Structure Modification, malicious samples are constructed using static visible triggers and trojan net embedded in target model is complex, which decreases the stealthiness of these backdoor attacks. In this work, we proposed a backdoor attack of DNN models based on structure modification using steganography. Specifically, inspired by the DNN-based image steganography, we construct hiding net to generate malicious samples with dynamic invisible triggers and construct simple trojan net using stacked autoencoder. The trojan net is connected with target model in serial structure to generate backdoored target model. Extensive experiments on multiple datasets and models verify the effectiveness of increasing the backdoor attack stealthiness.
Improved DeepLabV3+ Network Segmentation Method for Urban Road Scenes

Aiming at the poor accuracy and slow segmentation speed of the current road scene semantic segmentation, an improved DeepLabV3+neural network segmentation algorithm is proposed.Firstly, the encoder network is changed to a more lightweight MobileNetV3 network, which increases the segmentation speed without sacrificing segmentation accuracy.Secondly, the atrous spatial convolutional pyramid pooling (ASPP) is improved in the form of dense connection, which further expands the receptive field while maintaining the size of the feature spatial resolution, and increases the feature reuse efficiency. And the dual attention mechanism is added to the encoder and Dense-ASPP module, which improves the recognition effect of the edge area of the model and improves the model segmentation accuracy.Finally, the multi-scale low-level semantic features extracted at the encoder are fused at the decoder, thereby reducing the loss of spatial information due to subsampling.
Research on Forgery Attack on Authentication Encryption Algorithm ACE

The ACE authentication encryption algorithm is an algorithm designed by Aagaard et al. and submitted to the international lightweight cryptographic algorithm standardization competition. It has the function of authentication encryption and hash algorithm. It has certain reference and research significance to analyze the security according to the uniqueness of its structure. Taking the ACE-AE-k algorithm as an example, it is the first time to conduct a chosen-plaintext forgery attack from both direct and indirect aspects. By reusing the Nonce twice to forge the plaintext, the time complexity and data complexity can be neglected, which successfully proved the vulnerability of the ACE authentication encryption algorithm in the case of Nonce reuse or misuse. Secondly, this paper proposed an indirect forgery attack scheme, and combined with the optimal differential characteristics obtained by SMT/SAT solution tools, the maximum expected differential characteristics probability of ACE-AE-k is set to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-253.78336</sup> , which is higher than the probability of exhaustive attacks. On the premise of being discovered by a third party, the same authentication tag is generated to achieve the effect of monitoring or tampering, and the forgery attack is successfully realized.
Adaptive Local Reinforce Attention Network for Few-shot Learning

In many real-world application scenarios, limited to the economic, time and labor factors, it is not feasible to collect a large-scale labeled dataset. Therefore, researchers proposed few-shot learning methods that do not rely on large-scale data training. However, existing few-shot learning methods only use the global feature map of an image to predict its class, ignoring the role of information of local details with category characteristics in the image for classification. In this paper, we propose an adaptive local reinforce attention network (ALRANet) that can capture and strengthen information of local details. By dividing the learned knowledge, different kinds of learned knowledge are used to guide the capture and enhancement of local details in the new sample, and the content of learned knowledge is dynamically updated according to the learning performance. Extensive experiments are conducted on two datasets, and the proposed ALRA model outperforms those existing model, demonstrating the effectiveness of our model.
Secure Electronic Health Record based on Attribute based Encryption and Blockchain

With the rapid development of information technology, it becomes more and more popular for the use of electronic information systems in medical institutions. To protect the confidentiality of private EHRs, attribute-based encryption (ABE) schemes that can provide one-to-many encryption are often used as a solution. At the same time, blockchain technology makes it possible to build distributed databases without relying on trusted third-party institutions. This paper proposes a secure and efficient attribute-based encryption with outsourced decryption scheme based on blockchain, which can realize flexible and fine-grained access control and further improve the security of blockchain data sharing.
Research on Parallelization of Face Recognition

Face recognition is the hotpot in the fields of biometrics recognition. Until now, the method of face recognition is very accurate and simple. The main way to improve the speed of face recognition is to improve the algorithm. This paper speeds the recognition system through partitioning the system with threads and grouping the data. It implements a face recognition system based on PCA algorithm and BP neural network, which parallelized using multi threads and SSE instructions. Firstly, preprocesses the original face image. Secondly, extracts the feature by PCA algorithm. The system is completed through training and identifying the face feature. Lastly, this paper partitions the system with multi threads, and groups the data to implement the parallelization. To parallelize the BP neural network is difficult, and the speed of convergence is slow. So, this paper gets a multi BP neural network by improving the BP neural network. The recognition rate of face recognition in this paper reaches 93.5%, which increases 5.2% compared to the system using PCA only and 2.1% compared to the system based on PCA and BP network. The speed of parallelized system using multi threads and SSE instructions is 5.984 times faster than serialized system.
Fast and stable tracking algorithm for aerial target aiming point based on feature point matching

In some tracking scenarios especially with a specific purpose, it usually requires for stable tracking of a fixed aiming point besides a bounding box. In this paper, we apply the knowledge of feature points matching to achieve stable tracking of any point in the air target. Our approach consists of four steps: 1) select the FAST operator suitable for this scene to extract feature points, and employ the BRISK descriptor to describe; 2) delicately design dynamic mask area for feature points matching, incorporating FDSST tracking algorithm and digital image processing techniques to tackle this issue; 3) employ RNASAC algorithm to calculate the homography matrix of the feature point mapping, then predict the aiming point; 4) propose an effective template update method. As long as the tracking algorithm successfully tracks the target, the position of the aiming point can be accurately calculated by the algorithm proposed in this paper, and can be executed on the personal computer at an average rate of 411fps.
Design of Intelligent Medical Service Robot based on Raspberry Pi and STM32

With the development of medical technology, hospitalization has become a common treatment. Aiming at the complicated and cumbersome phenomenon of medical work, an intelligent medical service robot is designed and manufactured in this article. The intelligent service robot adopts STM32 and raspberry pi as the core controller and establishes the path planning under SLAM algorithm based on ROS system. After putting the items to be transported into the storage box, the user can control them by directly issuing voice commands or using mobile APP. The driving part of the robot adopts a two-wheel differential motion chassis to realize the autonomous path planning and steering obstacle avoidance of the robot. This intelligent service robot has the advantages of small size, low cost and strong scalability. It can well reduce the labor intensity of medical staff and has a good development prospect.
Research on Teaching Quality Evaluation System Based on Data Mining

Starting from the problems existing in the current teaching quality evaluation process in colleges and universities, this paper discusses a teaching quality evaluation system based on data mining. The paper puts forward the design scheme of the teaching quality evaluation system in colleges and universities. Furthermore, by using data mining technology, it discusses the data mining implementation method, system architecture, and system evaluation process in the teaching quality and evaluation system in colleges and universities. Implementing the system can provide a reference for the teaching quality evaluation work in colleges and universities, which has significant application value.
Real-Time 3D Gesture Interaction from Single RGB Images

This paper presents a 3D gesture interaction method based on a single RGB image. In this paper, a real-time image processing and estimation network is used to evaluate two-dimensional and three-dimensional hand joint information from a single RGB image. Subsequently, this paper utilizes a gesture classifier to classify the semantics of gestures and complete the recognition of gestures to complete the interaction. Firstly, the RGB image is positioned, and then the key nodes of the two-dimensional and three-dimensional hand are predicted and esti-mated. The classifier utilizes voting classifier to merge the three classifiers, and achieves considerable classification results. The interaction in this paper contains three-dimensional information, so it can have more flexible and more real interaction.
Research on Parallel Algorithms of Uncertain Combinational Coding

In this era of rapid development of information science and technology, data coding technology has been widely used in multimedia, computer, communication and other fields. Non-deterministic combined coding is further explored in deterministic combined coding. It is a kind of lossless coding. In combinatorial mathematics, the operation of the combination number calculates the ordinal number of the sequence to be coded. On the basis of the deterministic combination coding, the adjacent identical elements are eliminated and processed separately, which reduces the length of the ordinal number. At the same time, the reference sequence is encoded to further save space. However, the ordinal calculation of the non-deterministic combination coding is still a complicated process, and the coding speed is still slow. This article uses openMP technology to parallelize the ordinal calculation part of the non-deterministic combination coding. The running time of coded ordinal on the eight-core CPU is 23.38% of the original, which is 4.28 times optimized, and the overall coding time efficiency of the 17 segmented file instances has been improved about 2.94 times. It indicates the effectiveness of the parallel strategy in this article. Finally, this article analyzes the experimental results.
Cloud Migration Consistency Model Based on Data Characteristics

When users use existing search queries, they often have poor retrieval results because they cannot construct clear and accurate query words. Traditional query recommendation methods do not fully consider the relevance of user behavior, resulting in inaccurate query recommendation results. This paper A new query recommendation model is proposed, which is a query recommendation model based on click model and network embedding. The model first embeds the user's historical viewing behavior and click behavior through the click chain model, and measures the query and returned documents through the attention mechanism. Then use the attribute heterogeneous network to obtain the latent semantic information in the complex heterogeneous network structure; finally capture the complex information in multiple spaces through multi-head attention, and use multi-task learning to make score prediction. It outperforms the baseline model in both discriminative and generative tasks of query suggestion.
Design and Implementation of Smart Campus in Open University Based on the Integration of “Three Forms of Education”

With the maturity and application of Internet of Things and artificial intelligence technologies, the construction of smart campus has become an important direction of educational informatization. Taking Shanghai Songjiang Open University as an example, this paper studies the design and implementation of smart campus with the concept of “open sharing, Internet of everything, convenience and practicality, and energy level promotion” under the background of meeting the needs of degree education, community education and non-academic training. On the basis of intelligent teaching and intelligent management, it realizes the sharing of resources, the standardization of business processes and the improvement of management efficiency, and then promotes the development and improvement of lifelong education system.
Research on Acoustic Feature Extractor for Automatic Speaker Verification Systerm

In this paper, four frontends which are acoustic feature extractors are designed based on PNCCs extractor by removing its medium-time processing, power-law nonlinearity and DCT and by introducing per-channel energy normalization (PCEN), enhancing the robustness of ASV system. Of the four frontends, two frontends have learnable PCEN whose parameters are optimized automatically. These four acoustic feature extractors and MFCCs extractor are used as the frontends of ECAP A-TDNN respectively, resulting in five DNN-based speaker embedding extractors. Based on five speaker embedding extractors, five ASV systems are constructed respectively. All four ASV systems with designed frontends outperform the ASV system with MFCCs extractor in both intra-domain and cross-domain scenarios. On VoxCeleb-O evaluation dataset, the ASV system with learnable PCEN and mean power normalization, which is named as MLPs-ASV in the paper, achieves an improvement of 10.38% in EER, and an improvement of 11.86% in minDCF, respectively, compared to the ASV system with MFCCs extractor. On VoxMovies evaluation dataset, MLPs-ASV achieves 12.96% improvement in EER than the ASV system with MFCCs extractor.
Research and Application of Grid-based Integrated Evaluation and Management of Distribution Grid

With the gradual development of distribution net-work planning theory, the evaluation of the grid is becoming more and more demanding. The evaluation of traditional distri-bution network planning schemes often relies on the subjective experience judgment of experts, which lacks scientificity and rationality. Current distribution network planning schemes has established hierarchical index systems from different perspectives, used various evaluation methods to assign weights to the indicators, and established evaluation models, but different evaluation models have their shortcomings in the process of practical application. In this paper, the corresponding evaluation index system is established on the basis of distribution grid planning, the weights of each index of the planning effectiveness evaluation index system are solved by using hierarchical analysis, the affiliation function model of each index is selected according to the characteristics of the index, and the parameters of the affiliation function are given according to the relevant standards of the power grid and the actual operation of the distribution grid, the affiliation degree of each evaluation index is calculated by using fuzzy synthesis method, and the evaluation results are finally obtained so as to optimize the work of grid-based planning for distribution networks.
The Detection and Defense Mechanism for SQL Injection Attack Based on Web Application

In view of the risk of SQL injection attack faced by the Web system, this paper proposes a SQL injection attack detection mechanism based on triangle module operator. The method uses the analysis results of web logs and user input as fusion operators to judge whether an attack occurs. At the same time, for the defense against SQL injection attack, this paper believes that the source code vulnerability testing, penetration testing and security configuration verification should be conducted before the Web system goes online, therefore it can improve the security of the information system.
Object following car based on template matching

Object tracking technology has always been one of the hot topics in the field of computer vision, its research and development can better improve people's life, and object tracking based on the car platform is an important embodiment of this technology. This paper describes the object tracking car which is composed of OpenCV video image processing module and STM32F407 car platform, using the template matching algorithm of OpenCV function library, and through image preprocessing and frame difference method with template update to match, to achieve the car following the object under a certain video frame rate. It involves mask processing, binary processing, normalized processing and expansion corrosion processing in image processing. After integrated processing by Powersensor, serial port data is sent to cooperate with STM32 microcontroller to control the car movement, so as to complete the locking of objects and tracking of the car.
Based on DeepLab v3+ model to realize the road scene semantic segmentation

The semantic segmentation of deep learning has a very broad development prospect in the field of computer vision, but many network models with good segmentation effect have problems such as large amount of model calculation and long training time for segmentation in road scenes. In response to these problems, this paper changes the feature extraction network to the lightweight MobileNetV2 network, and changes the ordinary convolution in the atrous spatial pyramid pooling module to the depthwise separable convolution, which can effectively reduce the input parameters of the model and greatly reduce the calculation amount of the model. In order to improve the calculation speed of the model without reducing the detection accuracy as much as possible, an attention mechanism is added to the feature extraction module to further optimize the recognition effect of the model for object edges and improve the segmentation accuracy of the model. The experimental results show that the model can better extract the edge features of objects in road scene images and the segmentation accuracy of the optimized model can reach more than 86%.
Design and Implementation of Reading System Based on Improved Personalized Recommendation Hybrid Algorithm

In the paper, a personalized recommendation precision reading system based on an improved personalized recommendation hybrid algorithm is developed. The server side develops and selects the mature lightweight framework Spring Boot, and the client side develops and uses Html5 and Apache Cordova framework to meet the needs of mobile terminal devices of different operating systems. The results show that the improved algorithm can improve the accuracy and stability of reading recommendation.