Reviewerly/exHarmony huggingface dataset:
Time and Space Aggregation Recommendation Model Based on Synthetic Negative Samples

Currently, the recommendation model of the neural network is usually based on space aggregation neighborhood embedded, focusing on gathering information from the perspective of spatial structure information to learn the characteristics of user projects. The negative sampling method is difficult to balance positive and negative samples. In response to the above issues, this article proposes a time-space aggregation recommendation model based on synthetic negative samples. The model uses the multi-header attention mechanism to capture the chronological order of the neighborhood through the multi-header attention mechanism through interactive sequence diagram and sample mixed negative sampling strategies. A mixed sampling of different layers of pooling, thus synthesizing high-quality negative samples so that the model can better learn the boundary between positive and negative instances. Experiments show that the model fully captures users' dynamic interests, enhances the extraction effect of timing characteristics, and alleviates the problem of imbalance of positive and negative samples. And this model can be naturally inserted into the recommendation model of the neural network, which is universal.
Research on Reinforcement Learning algorithms in Computer Vision

As artificial intelligence technology continues to develop, reinforcement learning (RL) is evolving as a potent form of artificial intelligence. Reinforcement learning, as a subfield of machine learning, focuses on how to behave in a given situation in order to maximize the expected rewards. Due to the excellent perceptual and decision-making capabilities of RL algorithms, reinforcement learning has been widely used in various fields including medicine, finance, robotics, video games, and computer vision (CV). Among them, computer vision is a challenging and significant research subject in both engineering and science fields. Because diversity and imperfections are prominent features of the CV domain, there are numerous ways to utilize reinforcement learning to enhance CV tasks. This paper aims to introduce the fundamental concepts and methodology of reinforcement learning. Moreover, this paper details the recent applications of reinforcement learning in different branches of the CV field, and makes a comparison of the performance of the different algorithms involved.
Data Augmentation for Implicit Discourse Relation Recognition

Fine-tuning pre-trained models on downstream tasks tends to have decent performance. Although the pre-training model has learned a lot of knowledge with strong generalization ability in the pre-training stage, the benefits of fine-tuning are not high on the IDRR, a challenging task of NLU. On the one hand, the low amount of data makes the model lose its original generalization ability, and on the other hand, it is difficult for the pre-trained model to obtain enough discourse-level information from the limited data. Therefore, we propose an approach based on data augmentation. The model trains task-related soft prompt on a pre-trained model with frozen parameters. The data is then generated while maintaining the generalization performance of the generative model as much as possible. Furthermore, we use a consistency filtering algorithm to remove the generated noise samples. Experiments on the PDTB benchmark show that proposed method improves the performance of the baseline model.
Design and Implementation of Student Hierarchical Management Evaluation System Based on BP Neural Network

With the rapid development of smart campus construction in my country, the deep integration of information technology and education and teaching has become an inevitable trend. The phenomenon of skipping classes and failing courses has begun to appear while colleges and universities have expanded their enrollment and the number of college students has surged. Therefore, it becomes more and more important to carry out effective hierarchical management of students. Based on the relevant theoretical research of Design and Implementation of Student Hierarchical Management Evaluation System Based on BP neural network(BPNN), this paper analyzes the application of the student hierarchical management evaluation system, analyzes its mechanism, and uses the student hierarchical management evaluation system to help ensure the quality of school teaching and urge students to learn. Among them, BPNN has become one of the research hotspots in many scientific fields because of its simple structure, few training parameters and strong adaptability. This paper studies the target detection algorithm based on BPNN, and applies it to the design and implementation of the student hierarchical management evaluation system, which has important research significance and application value. The number of college participants was 74, 85, 63, 96 and 52 respectively. The corresponding recognition degrees of the hierarchical management evaluation system for students are 91.2 %, 93.8%, 90.4%, 89.5% and 92.7%, respectively. Through the data comparison, it can be seen that the students generally recognize the student hierarchical management evaluation system based on the BPNN.
Teaching Quality Evaluation and Software Implementation Based on ID3 Decision Tree Algorithm

As a classical decision tree algorithm, ID3 selects the best test attribute based on information entropy, uses information gain as the attribute division basis, and selects the attribute with the largest information gain as the split node to generate a decision tree. ID3 algorithm is simple, clear and easy to understand, and has very high classification efficiency. This paper mainly includes three aspects. Firstly, study ID3 decision tree algorithm, including basic algorithm and algorithm improvement. Secondly, it constructs the evaluation index system of teaching quality. Thirdly, the realization of teaching quality evaluation software based on ID3 decision tree algorithm is studied, which consists of data acquisition, data preprocessing, selection of optimal features, generation of decision tree model, evaluation of decision tree model, teaching quality evaluation and visualization of evaluation results.
A Novel Recommendation Model Based on Interactive Nearest Neighbor Sessions

For some session recommendation algorithms, only the information in the target session is modeled, ignoring the auxiliary role of the interactive nearest neighbor sessions to the target session, resulting in the potential collaborative information is not fully utilized. Therefore,we propose a novel recommendation model based on interactive nearest neighbor sessions(ARMBINNS). Firstly,a directed current session graph (DCSG) is constructed, which focuses on the conversion transitions between frequent items in the target session. The directed current session graph is modeled by graph neural network and soft attention mechanism to generate the session representation of user preference items. Then we search the interactive nearest neighbor sessions of the target session, select items from interactive nearest neighbor sessions and construct undirected interactive nearest neighbor graph (UING) with the target session. Similarly, the undirected interactive nearest neighbor graph is modeled by graph neural network and soft attention mechanism to generate a session representation with nearest neighbor information. Finally, rich session embedding is generated by combining the two types of session representation information through the fusion gating mechanism. Through experiments, it is verified that the proposed model has better recommendation performance compared with 9 advanced recommendation methods.
Online Music Performance Evaluation System Based on Apriori Algorithm

Nowadays, with the rise of artificial intelligence, online music performance is improving rapidly. Music has two important aspects: composition and performance. Different genres of music convey different styles, and performers inject different rhythms and dynamics into them, resulting in rich expressiveness. The purpose is an online music performance evaluation system. In order to realize the online music performance evaluation system based on the Apriori algorithm, the function of the online music performance evaluation system is designed in the experiment, and the Apriori based on association rules is used algorithm. The experimental results show that in general, which also shows that the online music performance evaluation system based on the Apriori algorithm is feasible.
Paired and unpaired: unsupervised cross-modal hash retrieval based on collaborative semantic distribution

Existing unsupervised cross-modal hashing retrieval methods generally are restricted by two limitations: 1. Most existing cross-modal hashing methods explore modal semantic information based on paired data, while the semantic information available in unpaired single-modal data is discarded; unsuper-vised methods that rely only on constructing semantic matrix for retrieval have considerable limitations, and the similarity information between heterogeneous data requires deep mining. In this paper, we propose an unsupervised hash retrieval based on colla-borative semantic distribution (UPJS). First, the method constr-ucts a collaborative semantic similarity matrix between modali-ties, which fuses the original semantic information of heterogen-eous modalities and preserves the semantic connections between different modal instances. Secondly, this paper employs feature fusion to transform unpaired information into paired information, and then achieves semantic similarity by considering both paired and unpaired information. Furthermore, the method constructs inter-modal triplet constraints between paired and unpa-ired data, and utilizes a collaborative matrix to jointly train se-mantic binary codes. Extensive experiments on two benchmark datasets, MIRFlickr and WikiData, show that this method has a favorable performance in both paired and unpaired data.
Design and implementation of a web application vulnerability detection system

With the rapid development of the internet in China, we are dealing with web sites all the time, but with this comes the increasing vulnerability of various web applications. The vulnerability of web applications can be used to steal information, account theft and fraud, threatening the security of web applications. Therefore, the security detection of web applications is particularly important. This paper introduces the background of today's web application scanning technology, analyses the importance of securing web sites, and focuses on the history and future development trends of web application vulnerability scanning at home and abroad. The system is based on the OWASP Top 10 vulnerabilities of SQL injection and XSS, which have a large impact. By analysing the risks and principles of these two vulnerabilities, a scanning system is designed to run on the Windows platform.
Joint Chinese entity relationship extraction based on the improved attention mechanism

Entity relation extraction is one of the core sub-tasks of information extraction, and also the focus of natural language processing research.First, according to the problems of pipelined entity relation extraction, a joint method based on sequence annotation is adopted for entity relation extraction. Secondly, the knowledge-enhanced ERNIE pretraining model is used for text semantic representation. In the feature extraction module, a general attention mechanism is not effective in the small-scale data set. An improved attention mechanism and BiLSTM are proposed. Finally, a variant-loss function of circle loss is adopted for the slow model convergence problem caused by the data label imbalance problem. After experiment, it is shown that the proposed fusion model outperforms the other models, while using the variant loss function of circle loss makes the model converge faster.
A Traffic Light Recognition Method Based on Image Super Resolution in Intelligent Blind Glasses System

One kind of traffic light recognition method based on super resolution technology applied in intelligent eyeglasses system is proposed in this paper. Take advantage of the high recognition accuracy of the latest hot target detectors and the ability of super-resolution technology which can increase image resolution dramatically, so as to achieve the accurate identification of pedestrian traffic lights. The lightweight adjustment operation can ensure the real-time performance of the model on the mobile terminal and correctly assist the safe passage of visually impaired people at traffic intersections.
Multi-view 3D reconstruction based on SFM and improved deep network

In recent years, 3D reconstruction technology has been increasingly developed in computer vision, Traditional 3D reconstruction methods have high accuracy and low hardware requirements, but they have great limitations in the reconstruction of non-ideal Lambertian scenes. 3D reconstruction methods based on deep learning can make the matching more robust and the reconstruction effect more complete through large amount of data learning, but they are easily limited by memory. Therefore, this method proposes a cross-fusion of traditional methods and deep learning, combining SFM (Structure From Motion) algorithm with PatchMatchNet deep learning network. And put forward important improvement and optimization for PatchMatchNet network. On the premise of ensuring the reconstruction effect and error accuracy, the initial depth value is optimized, the convergence speed is accelerated, the reconstruction efficiency is improved, and the loss function is improved to improve the quality of the depth map.. Finally, the 3D reconstruction task is completed. In this paper, 3D reconstruction experiment were carried out on Tsinghua School dataset. The experimental results show that the proposed method has advantages in model accuracy, model reconstruction speed and model training time.
Behavior recognition research based on reinforcement learning for dynamic key feature selection

In the task of behavior recognition based on time-series sequential data, there are often some features that are interference redundancies after feature extraction of the original data by the depth model, and these redundancies will not be beneficial to recognition but will have interference effects. Therefore, it is important to accurately select the features that are beneficial for recognition in behavior recognition tasks. To address the above issues, We propose a reinforcement learning framework, called Dynamic Key Feature Selection Network(DKFSN), aiming to achieve accuracy improvement by continuously exploring the advantages and disadvantages of distinguishing features, eliminating the redundant features that interfere with recognition, and retaining the features rich in quality information. First, feature extraction of the original data using a baseline network to capture depth features and prediction results. Using the depth features as input to a dynamic feature selection network to predict which features are retained and then making a determination to retain key features. Finally, behavior prediction by retained key features and feedback on the selection behavior using a reward function are used for the training of the DKFSN. We validated the validity of DKFSN on two public benchmark datasets.
Resilience of Stablecoin Reserve for Distributed Energy Trading

For payment settlement, a blockchain-based Peer-to-Peer (P2P) energy trading requires a stable medium of exchange with little price volatility. Stablecoins, the most suitable medium of exchange, gaining concentration even from central banks. A consortium of central banks recommends complying with capital and liquidity standards for the high-quality liquid asset (HQLA) for the solvency of banks or financial institutions. Stablecoin as HQLA requires the adaption of such standards in P2P energy trading for reserve resilience. We propose a mechanism (NF90) that controls the inflow of stablecoins responding to Liquidity Coverage Ratio (LCR) for reserve resilience. Basel III accord recommends 100% of LCR. We measure the efficiency of NF90 concerning LCR as a metric. We simulate the proposed mechanism in Hyperledger Fabric as a permissioned blockchain platform for decentralisation, data storage, and smart contract. Experiments show that NF90 is the most efficient inflow control mechanism compared to other simulated mechanisms.
A smart contract vulnerability detection model based on graph neural networks

In recent years, smart contract vulnerability detection methods mostly view smart contract source code as natural language for processing, which cannot fully capture the semantic and structural features of the source code and has a high rate of false positives and missing positives. To improve the accuracy of vulnerability detection, this paper uses Graph Neural Network to obtain the semantic and structural information of the source code and Convolutional Neural Network to assist learning. We propose a graph neural network-based vulnerability detection model for smart contracts, which transforms smart contracts into control flow graphs, learns graph embedding using graph neural networks, and introduces Convolutional Neural Networks to learn the node order information of control flow graphs, and finally performs vulnerability detection using graph embedding and node order information. Experimenting on real datasets, our accuracy and F1 values are improved and the model can effectively detect smart contract vulnerabilities.
Chinese medical knowledge mining and analysis based on syntactic dependency and named entity recognition

In the hospital, when doctors get the patient's case analysis, they often need to spend a lot of time reading the text of the current case and then analyze it, in order to get the cause that affects the patient's disease, most of which are limited to the cause of the disease. In order to allow doctors to see the cause of the patient's condition more intuitively and clearly, combined with Python's powerful data processing, text training, data mining and other functions, firstly carry out text training on the hospital's case details in previous years, and perform named entity recognition on medical entities to achieve entity classification. Then, based on the Chinese text structure, through dependency syntax analysis, a keyword for a disease analysis is extracted, and finally a triple is formed. The ontology project is constructed through protege, and a knowledge map is formed, which not only serves as an intuitive analysis and display of this disease.
Analysis of Learning Mechanism of Generative Adversarial Network

The generative adversarial network learns different kinds of real images and generates corresponding fake images. The image quality generated based on the generative adversarial network show the strength of the learning ability of the model for different kinds of images. Based on the apparent differences in features of different types of images, this paper proposes to judge the strength of features of different kinds of images in the generative adversarial network learning process based on generative adversarial networks and convolutional neural networks. The experiment uses three different kinds of data sets, including cartoon, face and food, and carries out three groups of experiments. The experimental results show that the simpler the image is, the stronger the learning ability is.
An Energy-Constrained Optimization-Based Structured Pruning Method for Deep Neural Network Compression

Deep neural networks are widely used in modern intelligent applications due to their superior ability to express reality, and these intelligent applications run on highly energy-constrained edge devices. Neural network structured pruning is an efficient method to reduce the energy consumption of neural networks. This paper proposes an energy-constrained optimization-based structured pruning method to compress deep neural network. This method uses the energy budget provided by edge devices as a constraint for neural network pruning. The key idea is to formulate deep neural network structured pruning as an optimization problem, in which the energy estimate of each layer is used as the Frobenius norm optimization constraint for the convolution kernel weights. Then the importance of all convolution kernels in the network can be evaluated, thereby the neural network structure can be pruned according to the importance to reduce the energy consumption and memory size of the neural network. Compared with traditional heuristic structured pruning methods, our proposal enables neural networks to achieve higher accuracy with the same or lower energy budget.
Design and Implementation of Sports Meeting Intelligent Information Management System Platform Based on Web

With more and more information technology in management and extensive application, management information system has gradually matured. Management information platform is an evolving new discipline. Based on the sports organization, management of the full investigation, research and Analysis on the whole sport meeting's organization and management, this paper develops “sports meet management system” which designs on the principle of Web and combined with the actual operation. The system includes athletes' registration, numbering, packet scheduling, schedule, preliminary and final summary results and record for the overall management of the games, business process to provide comprehensive, consistent and rapid processing.
Implementation of Bidirectional LSTM Accelerator Based on FPGA

Sentiment analysis has become a more active area of research in the past few years due to the popularity of social networks and the e-commerce economy. Bi-directional Long Short-Term Memory (Bi-LSTM) has excellent performance in sentiment analysis. In this work, we design a Bi-LSTM accelerator which is implemented on FPGA. The accelerator completes most calculations in processing engine (PE) array, and multiple PEs are arranged to calculate in parallel to accelerate the calculation. For the element-wise module in each PE, we propose a time-division multiplexing strategy, using only two multipliers and one adder to significantly reduce resource utilization. Finally, we train a small-scale Bi-LSTM model by using the amazon customer reviews dataset. The Bi-LSTM accelerator implemented on FPGA reaches 13.4× and 8.4× speedup compared to CPU and GPU, respectively. And the power consumption is only 6.3% and 7.3% of CPU and GPU. The accelerator throughput reaches 91.47 GOP/s, and the energy efficiency reaches 30.73 GOPS /w.
A Joint Resource Allocation and Efficient Communication Scheme Based on FL for the Internet of Vehicles

Motivated by the rapid development of the Internet of Vehicles (IoV) and fifth-generation mobile communication technology, vehicle clients have put forward higher requirements for reducing communication overhead and enhancing data privacy. The recently emerging distributed learning method, federated learning (FL), can efficiently process data parameters while protecting clients' privacy. Therefore, we propose an FL-empowered resource allocation and communication optimization framework in the IoV. The purpose of this paper is to minimize the loss function of FL while ensuring efficient communication between vehicle devices and the BS. Based on this purpose, the problem of the uplink wireless resource allocation and parameter quantization in the IoV is formulated. Then, the shortest path optimization algorithm is used to achieve wireless resource allocation recursively. Furthermore, an error-feedback enabled quantized stochastic gradient descent algorithm is adopted, which reduces the size of the parameters transmitted to the BS by quantifying the local parameters of the vehicle devices, and the error-feedback mechanism is used to speed up the convergence of the FL. Finally, simulation results show that the proposed scheme outperforms the comparison schemes in terms of global loss and convergence accuracy.