Reviewerly/exHarmony huggingface dataset:
Parameter Factorial Weighting of Federated Learning: Center-Client Access Strategy and Application Design

Federated learning, using multi-party parameter sharing instead of data centralized training, effectively solves the data privacy and security problems in collaborative training, which has become an important research issue in recent years. In this paper, based on the previously proposed FedBN-PW-CTC model [1], we simulate realistic data and do supplementary experiments to further validate the effectiveness of the model and propose supplementary schemes and application scenarios, with the following main contributions. (1) We compare the training result of FedBN-PW-CTC model on both independently identically distribution (iid) data and non-iid data, and verify the effectiveness of parameter weighting (PW) on non-iid data. (2) We propose an asymmetric center-client access discrimination strategy for federated learning model. (3) A realistic application scenario, an intelligent federated learning-based elderly assistance service system, is proposed for the model, and we design the structure for the system.
Application Research on Blockchain Based Access Control

In the era of big data with large information sharing and fast data flow, traditional access control technology oriented to closed environment cannot be directly applied to the distributed and dynamic environment of big data. With the characteristics of decentralization, blockchain can solve the trust problem in traditional access control technology. From the application scenarios of the Internet of Things and e-Health, this paper analyzes and summarizes the existing research progress from two implementations: transaction-based access control and smart contract-based access control. Facing the current challenges of blockchain-based access control mechanism, the future research direction is proposed afterwards.
A self-augmented radial basis function neural network for sensitive systems

In order to solve the imprecise problem of radial basis function neural network (RBFNN) to the sample output for the sensitive system, a self-augmented RBFNN (SA-RBFNN) is designed to improve the accuracy of the model. Firstly, the network structure is constructed by using the correlation between input, output and hidden layer neurons to increase the sensitivity of the network. Secondly, an accelerated gradient algorithm is used to train SA-RBFNN, which improves the speed and the precision of neural network training. Finally, the proposed SA-RBFNN is evaluated through a benchmark experiment and a practical problem in wastewater treatment process. The results indicate that the proposed SA-RBFNN can quickly converge to the optimal solution and has a good effect on more sensitive systems.
Fusion of Different Features of Different Depth Dual Backbone Networks

Convolutional neural networks have demonstrated a dominating performance in many fields in recent years. A notable trend of CNN is that the architectures are deepening and becoming more complex, which means more parameters, higher memory footprints and longer inference times. These major aspects restrict their use in resource-constrained settings, such as embedded systems and mobile devices. Here, we propose a new architecture with different combinations of the dual backbone with different depths. Each backbone has a maximum of 5 convolutional layers; the number of convolutional layers is between 1 and 5; and the features extracted from the dual backbone are fused via summation before the first fully connected layer. The deeper backbone can increase the receptive fields by stacking more convolutional layers, and the shallower backbone can extract lower-level features, preserve better location information and provide finer details. The well-known FMNIST and CIFAR-10 datasets were selected to evaluate the network's performance. The experimental results show that almost all evaluation indexes of dual backbone networks with different depths are better than those of VGGs, ResNeXt and DenseNet.
Application of Varint_LZW encoding algorithm in image compression

The rapid development of information technology has brought tremendous pressure to data transmission and compression, so data compression has great significance. Data compression before transmission can reduce transmission bandwidth, and data compression before storage can reduce data storage. The Lempel-Ziv-Welch (LZW) coding process has a very high requirement on the bit width of the data cached in the dictionary in RAM for such images with the same color appearing continuously. LZW is used to encode the same 16-bit pixel data continuously. When the data is encoded to 128 pixels, the 128bit wide RAM will be filled up. If the same data is continued to be encoded, the RAM will overflow the data. In this paper, Varint encoding algorithm is introduced to solve this problem, and Varint encoding is carried out before LZW encoding. Varint has good compression effect on high continuous and repeated data. Therefore, for this type of data, Varint_LZW encoding shows lower utilization rate of RAM bit width than LZW encoding.
Error Correction of Tibetan Verbs Based on Deep Learning

Verbs are the core of the semantic structure represented by Tibetan sentences, and automatic error correction of Tibetan verbs is one of the important research topics in Tibetan language processing. In this paper, we propose a Bi-LSTM neural network model for Tibetan verb error correction by analyzing the usage rules of verbs in Tibetan, summarizing the grammatical, semantic and spelling features of verbs, and proposes a Bi-LSTM neural network model for Tibetan verb error correction based on these features, which not only extracts various features of verbs, but also capture the contextual information of verbs through Bi-LSTM neural network. The method proposed in this work solves the drawbacks of the traditional methods of low generalization and inability to obtain long-distance contextual implicit information. The experimental results show that the accuracy, recall and F1 values of the proposed method on the test set reach 97.3&#x0025;,95.7&#x0025; and 96.9&#x0025;, respectively, indicating the effectiveness of the proposed method on the task of automatic error correction for Tibetan verbs.
DaDDNet: A Dynamic-aware Dual-discriminators Network for Generating Adversarial Patches

Generating adversarial patches aims to deceive the classifier with generated samples in the real world. Although many current methods have made advances, the realistic and attack capability are still challenging issues. We propose a dynamic-aware dual-discriminators Network (DaDDNet) for generating adversarial patches, which is composed of a generator and dual-discriminators <tex>$D_{\alpha}$</tex> and <tex>$D_{\gamma}$</tex>. The dual-discriminators improve the global authenticity and local aggressiveness of the adversarial patches. To alleviate the phenomenon of overfitting, a dynamic-aware strategy is assigned during the training process. Considering the DaDDNet&#x0027;s effectiveness, 10-fold cross-validation experiments are carried out on seven classifiers in four datasets. The results show that our DaDDNet on 23/28 groups of experiments leads to a decreased recall rate, which is better than the conventional GANs. Furthermore, our method accelerates the convergence rate and the training method is more stable by observing the training loss function.
A Robust Human-Machine Hybrid Visual Tracking System

Visual object tracking is a basic task in computer vision, which has been widely used in intelligent transportation, autonomous driving, security systems, military reconnaissance and other fields. Most studies in visual object tracking assume that the target changes smoothly and the target will not disappear for a long time. However, in practical applications, challenges such as complete occlusion, rapid movement and target appearance dramatic change, make it very difficult to track the target consistently for a long time. In this work, we propose a human-machine collaboration method to cope with such challenges. We hope to build a tracking framework that combines the powerful tracking capabilities of human vision with the state-of-the-art tracking methods, so as to get a robust visual tracking system. Humans participation can effectively improve the accuracy and robustness of tracking. In the process of human participation, the tracker can also improve its discrimination ability by recognizing the target of human interest. Compared with the state-of-the-art trackers, our method achieves higher performance on a fairly complex experimental dataset.
Active Contour Driven by Region Fitting and Laplacian for Image Segmentation

The region-based active contour models can easily get stuck in local minimums and the image has intensity inhomogeneity. To solve this problem, we proposed an active contour model driven by region fitting and Laplacian. Firstly, a improved local fitting image is defined by adjusting the local average intensity weight, which can effectively avoid local minimums. Then, a region fitting term is defined by using both global and improved local fitting images. Moreover, local and global signed pressure force functions are introduced in the solution of the energy function to stabilize the gradient descent flow. Finally, a Laplacian energy term is introduced in the energy function, which can smooth the homogeneous regions and enhance edge information. Experimental results with different types of images are used for quantitative and qualitative compared with region-based active contour models show that the proposed model has a higher segmentation accuracy.
Research on Real-time Medical Online Learning Content Recommendation based on Multi-view Data Mining

The purpose of this paper is to solve the problem of intelligent analysis of learners&#x0027; behavior and intelligent recommendation in the domain of medical online education. The teaching behavior has transformed from experience teaching into massive data teaching. Moreover, the learning behavior is also changed from centralized learning to fragmented learning. In this paper, we study the method of personal education recommendation to meet these challenges. In this paper, a novel multi-view extreme learning machine model is proposed. We can get the optimized classification results. Based on these results, we proposed a collaborative filtering based personal recommendation method and applied via Spark framework. The experimental results show that, based on the effective analysis of learning behavior, the proposed method can be used to recommend the medical online learning content for the learners in practical teaching. In this paper, data mining and recommendation methods are realized in the field of medical online education. The methodological research and case studies can meet the needs of medical online education.
Application of Deep Learning in Intelligentization of Power System Vulnerability Knowledge Graph

With the expansion of power grid, the amount of knowledge in power system is exploding. In order to organize, manage and utilize massive knowledge effectively, knowledge graph technology is introduced into the field of power system. In order to further improve the application of knowledge graph technology in power monitoring system. Firstly, this paper analyzes the power system knowledge graph and its advantages in power system knowledge management. Then, the construction method of power system knowledge graph is designed, focusing on the comprehensive analysis of the relationship between defect level and risk level, and drawing heat map. Combined with the characteristics of power system knowledge graph, the typical application scenarios of knowledge graph technology in the field of power system can be intelligently expanded by applying the mature and stable graph database method in the industry and adding the deep learning Convolutional Neural Network (CNN) method innovatively. Finally, on the basis of analyzing the current research hot-spots, the key problems in the application of knowledge graph in power system and the possible research directions in the future are pointed out.
Adaptively Discriminant Locality Preserving Projection

Dimensionality reduction has been playing a significant role in many fields such as recognition, classification, clustering, high-dimensionality data compression. However, due to the existence of noises in the feature space of the original data, manifold learning methods take risks of finding the k nearest neighbors. LAPP designed a &#x201C;coarse to fine&#x201D; strategy to iteratively obtain the optimal subspace to solve this problem and obtain the optimal subspace. However, Since the discriminant information is also essential for the recognition and classification, ADLPP combined this &#x201C;coarse to fine&#x201D; idea with the idea of Supervised learning, which could not only preserve the local information after projection, solve the problem of noises and obtain the optimal subspaces, but also gain better performance on classification.
Research on the Optimization of the Education and Training Model for International Students Majoring in Information Engineering in Higher Vocational Education

In order to train engineering and application-oriented international students with international professional quality and high skill level and to equip such graduates to have the ability to obtain high-quality employment, this paper shows how Jinan Engineering Polytechnic forms a popularized education and training experience for international students with an individualized education and training program and running the project-based design through the whole practical teaching link by the analysis of information engineering students training problems. Practice has proved that innovation and optimization of education and training mode for highly skilled talents is the key measures to effectively improve the level of international talent training.
Weighted Deformable Convolution Network with IOU-boundary loss for Solid Waste Detection

It is of great significance for the detection of solid waste for environmental protection. In recent years, object detection methods based on deep learning have been widely studied. Different from regular objects such as airplanes or buildings, solid waste commonly has arbitrary shapes and the boundaries are always hard to distinguish. In this paper, a weighted deformable convolution module was proposed in which the offset and weight for each sampling location of the feature map were further considered. In this way, the feature representation of irregular objects can be enhanced and the regions can be extracted effectively. Furthermore, a combined IOU-boundary loss, including the Intersection-over-Union (IOU) and the aspect ratio (AR) losses, was designed to regress these irregular solid waste. Finally, a solid waste dataset was constructed manually to evaluate the proposed method, and it shows that the proposed method outperforms other traditional object detection methods, such as FPN, YOLO and CenterNet.
Distributed State Estimation for Large-Scale Systems in the Presence of Data Packet Drops

This article focuses on the distributed state estimation problem for large-scale systems in the presence of data packet drops. The large-scale system is structured in several correlated subsystems in the physical space, and each subsystem only communicates with its neighbors. In particular, the states of different subsystems are measured by different sensors, and the sensor broadcasts measurement information to the subestimator and its neighbors through the lossy communication channel. Thus, subestimators obtain different local information in the presence of data packet drops. In this article, the distributed estimator is designed and the optimal gain is obtained under the minimum mean square error (MMSE) estimation criterion by using local information set. Finally, the effectiveness of the distributed estimator is illustrated by a simulation experiment.
Chinese Named Entity Recognition via Multi-Channel Attention of Lexicon

Lexicon, as a kind of external knowledge, has been widely used by existing studies to assist the model for identifying entity boundaries effectively. However, most existing approaches only pay attention to the words related to a certain entity and do not consider the impact of words of different lengths on the recognized entity. In this paper, we propose a neural model for named entity recognition tasks enhanced by integrating multi-channel attention. In the multi-channel attention module, we assign words to different channels according to their length and measure the degree of attention the words have to the entity. Experiments results on three widely used Chinese benchmark datasets for NER demonstrate the effectiveness of our method.
Application of a Government Data Center (GDC) Reference Model for Security Management Analysis

The Government Data Center (GDC) differs from other data centers in functionality, service offer and the variety of solutions that are available. In order to facilitate the knowledge acquisition that is required to enable better management of GDCs, the development of an enterprise architecture reference model of these data centers is advisable. The proposed model would assist in data center management; improve communication related to the solutions used in the center; and would apply quality attributes to facilitate center management. The GDC reference model presented in this article consists of several layers and several dozen views. The model was built in ArchiMate, which is a language used mainly for enterprise architecture description. It was decided to adapt and extend this model to incorporate GDC security issues. Due to the variety of GDC software available, security solutions should be tailored to each heterogeneous situation. One of the basic demands of data centers is to meet recognized security standards. In particular, ISO / IEC 27000, a family of standards used to manage security for all types of organizational assets, was considered; the GDC reference model is a direct collection of such assets. A risk view meta model was used, which presents the basic dependencies between threats, risk and security, as related to a company's assets. The focus was on ISO 27001 control domains. Each one was assigned a practice (security implementation) that was used in the GDC. The next step was mapping control domains to GDC layers. This enabled conclusions to be drawn relating to coverage of security standards as well as supplementing the reference model with the missing security aspects.
A Case for Noisy Shallow Gate-based Circuits in Quantum Machine Learning

There is increasing interest in the development of gate- based quantum circuits for the training of machine learning models. Yet, little is understood concerning the parameters of circuit design, and the effects of noise and other measurement errors on the performance of quantum machine learning models. In this paper, we explore the practical implications of key circuit design parameters (number of qubits, depth etc.) using several standard machine learning datasets and IBM's Qiskit simulator. In total we evaluate over 6500 unique circuits with n ≈ 120700 individual runs. We find that in general shallow (low depth) wide (more qubits) circuit topologies tend to outperform deeper ones in settings without noise. We also explore the implications and effects of different notions of noise and discuss circuit topologies that are more / less robust to noise for classification machine learning tasks. Based on the findings we define guidelines for circuit topologies that show near-term promise for the realisation of quantum machine learning algorithms using gate-based NISQ quantum computer.
Discriminative correlation filter tracking algorithm with Transformer based on a multi-frame Cross-Attention mechanism

Currently, tracking methods based on discriminative correlation filter and Siamese network are one of the hot research topics in visual object tracking tasks. Among them, how to make full use of the rich spatio-temporal information of the target between frames in a video sequence is one of the core problems in studying this topic. To address this problem, the information related to the target in the first frame, the history frame, and the current frame is transformed throughout the tracking process with the Cross-Attention mechanism as the core mechanism, and the Siamese-like architecture is used to achieve a more complete characterization of the tracking target features. We propose a discriminative correlation filter tracking algorithm with Transformer based on a multi-frame Cross-attention mechanism to improve tracking accuracy while maintaining the tracking speed essentially constant. We tested our proposed model on GOT-10k, TrackingNet and OTB2015 datasets, and the test results demonstrate the effectiveness of our proposed model, improving tracking accuracy while running at real-time speed.
Analysis and Design of College Student Award Management System

University award management plays an important role in cultivating students&#x0027; good quality. However, at the present stage, most colleges and universities have scattered award management structures and generally use traditional manual means of information office. The shortcomings of low system efficiency, high error rate and complex repetition have made students and faculty complain. In this paper, the current situation of award management system for college students is studied. According to the phenomenon, the causes of the problems are analyzed in depth. Combined with the practical experience of the management of colleges and universities, the significance of the construction of the system is analyzed, and the multi-parameter award management system is constructed, which is realized by using the management information system.
Analysis of Library loan records based on hybrid Apriori- Genetic algorithm

The library database system accumulates lots of information resources in the long-term service process. Apriori algorithm mining association rules has low time and space efficiency, resulting in a large number of association rules. Using hybrid Apriori-Genetic algorithm to analyze records can be effective. Firstly, the records are cleaned and preprocessed, and the algorithm is used to analyze records, so as to find the association rules between different types of books borrowed, and then the two algorithms are compared in the the number of association rules and Algorithm time complexity.
Autonomous navigation for indoor mobile robots based on reinforcement learning

The algorithm of building a map based on SLAM (simultaneous localization and mapping) in indoor environment and using the map for autonomous localization and navigation is mature and has more successful cases applied in practical scenarios, but the cost of building a map is still very expensive, we try to use mapless method to achieve autonomous navigation of robots, according to the human in unknown environment can clearly reach the target location because human in We try to analogize human decision making thinking from the direction of intelligent decision making to guide mobile robots to achieve autonomous navigation, and reinforcement learning is an important algorithm to achieve intelligent decision making. In this paper, We use reinforcement learning methods from the original images acquired by the vision sensors for the robot to learn the optimal decision navigation method from the initial position to the target position. The experiment showed good results