Reviewerly/exHarmony huggingface dataset:
Proxy Aggregation Signature Scheme with Forward Security

With the continuous development of economy and society, data security has been the biggest issue that restricts social development. Based on the elliptic curve discrete logarithm problem and the strong RSA hypothesis, we proposed a forward-aggregation proxy aggregation signature scheme. The scheme has the following characteristics: Firstly, it has non-repudiation and realizes two-way verification of signed users and trusted centers. Secondly, the scheme has forward security and ensures the security of signature information. Finally, under the random oracle model It proves that the scheme is anti-existence forgery.
Sentences Similarity Model Based on Fusion of Semantic, Syntactic and Word Order Multi-Features

In sentence similarity research methods, sentence similarity is often calculated from semantic aspects, however, the influence of other features is ignored. For example, the influence of sentence syntactic structure and word order on sentence similarity is not considered. Therefore, This paper proposes a method to measure the degree of similarity between sentences based on a multi-feature fusion of semantics, syntactic similarity and word order similarity, respectively. For the aspect of semantic similarity, in this paper, we first preprocess the sentence and obtain the word vectors, and then obtains sentence vector features through neural networks, so as to calculate the sentence semantic similarity; Finally, for the aspect of word order similarity, word order is considered as the auxiliary feature information to calculate sentence similarity since word order has a smaller effect on sentence similarity compared to the above information. The experimental results show that the proposed model has an accuracy of 77.4 % and an F1 value of 84.3% on the MRPC dataset.
Flink Task Scheduling Based on LBGA

In order to solve the problem of uneven task load of different nodes affecting task execution time and throughput caused by the Flink's default scheduling strategy, a task scheduling strategy based on load balancing genetic algorithm (LBGA) in the Flink streaming computing environment is proposed. The strategy starts by calculating resource node performance metrics based on the resource data monitored by the Ganglia module. Considering the matching relationship between task resource requirements and performance metrics, this paper proposes an idea of applying the genetic algorithm (GA) to a streaming computing platform for optimisation. In addition, the LBGA scheduling is proposed on this basis and then experiments are conducted to verify the effectiveness of the algorithm. The experimental results show that compared to the default scheduling algorithm of Flink, the proposed algorithm reduces the execution time of WordCount benchmark tasks by 7.6% and 7.7% for parallelism degrees of 8 and 16 respectively. Not only is the performance of resource node load balancing optimised, but throughput is also significantly improved.
Software Usage Prediction Based on Hybrid LSTM-ARIMA Algorithm

The number of users of software is an important indicator for engineers when developing, modifying, and maintaining software. However, the number of software users is often influenced by numerous factors, such as software features, trends, and so on. This leads to the fact that the number of users contains both linear and non-linear trends in the process of change. This leads to the original single algorithm not being able to predict the number of users of the software better. For this reason, a hybrid LSTM-ARIMA algorithm is designed in this paper to predict this problem. In this paper, a Bayesian combination model is used to calculate the weights of two single algorithms respectively, so as to realize the combination of two algorithms. Experiments show that the designed algorithm can better fit the trend of the change in the number of people and has a low SMAPE, which indicates that the prediction performance of the algorithm is good.
Audio-Face Generating Using Squeeze-and-Excitation Enhanced Generative Adversarial Network

Generative adversarial network (GAN) can generate similar results to real data by learning the probability distribution of real data through generators and discriminators playing against each other, and they are widely used in the field of image generation. The traditional convolution generation adversarial network combines the convolution neural network (CNN) and GAN to solve the problem of unstable GAN training. However, the existing convolution GAN faces two main challenges: (1) Due to the limitations of convolution operators, the traditional convolution GAN model is difficult to establish dependencies between different image regions. (2) There is a great heterogeneity between visual and auditory modes, so it is extremely difficult to realize the task of cross-modal face generation according to audio by establishing the relationship between speaker's audio and face. In response to the above challenges, we aim to explore the cross-modal relationship between human auditory and visual perceptions, and propose a generative adversarial network approach based on the Squeeze-and-Excitation attention mechanism (SE), and apply the approach to the cross-modal face generation task. First of all, we combine the SE attention module with the generative adversarial network, and propose a new SE-GAN (Squeeze-and-Excitation Enhanced Generative Adversarial Network) method to enhance the division of different features between the generator and discriminator during the training phase. Second, we apply SE-GAN to the audio-face cross-modal generation task to establish the relationship between audio and face. Finally, we do experiments on the Voxceleb1 datasets, and the experimental results show that the face image is basically generated through the audio of the speaker, which is consistent with the identity of the speaker.
Silhouette: Federated Privacy-Preserving Data Mining Method for Classification and Clustering

Since wireless devices permeating our modern life, the concern of personal privacy leaking lingers in industry, public, and policy areas. However, obstacles, like the high-dimension of real-world datasets, resource limitation in the network, restrain the implementation of many privacy-preserving data mining methods in more complex scenarios. Combined with Federated Learning, we propose the Silhouette algorithm, a federated privacy-preserving data mining method for the high-dimensional dataset on classification and clustering tasks. The algorithm irreversibly reduces the dimension of a dataset, keeping the original distribution with the differential-privacy guarantee. Compared with other methods, Silhouette achieved higher accuracies on both clustering and classification and dropped more than 90% communication in the federated setting.
Research on Domain Adaptive Methods Based on Self-Supervised Learning

In order to solve the problem of training data without labels in a single domain, self-supervised learning tries to train the model by setting auxiliary tasks and providing false labels to the samples. Finally, fine tuning the trained model on the downstream tasks can achieve better results. In this paper, the problem of unlabeled target domain samples in unsupervised domain adaptive is regarded as a self-supervised learning problem, and rotation prediction is proposed as an auxiliary task in the target domain and rotation Angle label is used to provide pseudo-supervised signals for the target domain samples. At the same time, this paper fuses the batch kernel norm maximization algorithm with the target domain sample prediction consistency loss, and proposes the consistency batch kernel norm maximization algorithm. In this paper, visual converter is selected as the basic architecture of the model, and experiments are carried out on three data sets of Office-31, Office-Home and VisDA-2017, and compared with the latest and best algorithms, all of which achieve the best results.
Research on Standardized Information Model of Distributed Energy Grid-Connected Communication Considering Social Factors

The large-scale grid connection of distributed energy resources is an effective way to solve the problems of power supply shortage and environmental pollution, and there is no unified standard for the communication protocol when the existing distributed energy resources are connected to the grid. These standards focus on the electrical characteristics of the transformer itself, ignoring social factors. Firstly, this paper analyzes the business data requirements of distributed energy grid-connected communication. Then, the “phenomenon/data/data-provider” pattern is extracted, and a standardized information model of distributed energy grid-connected communication is constructed. Finally, the summary and application prospect are carried out.
Tram Energy Consumption Prediction Based on Improved Gaussian Process Regression Model

To overcome the problem of low energy consumption prediction accuracy caused by the diversity of influencing factors for tram energy consumption, a tram energy consumption prediction theory based on improved Gaussian process regression model come up, in combination with gray theory. Due to the fact that the standard Gaussian process regression model directly selects the covariance function, which can easily lead to a problem that the fitting degree with the physical process of the research object is not high enough, a multi kernel covariance function is proposed, in order to improve the traditional Gaussian process regression algorithm. The calculation results of an actual example indicate that the accuracy of this prediction method is improved compared with conventional means.
Bank Credit Score Card Selection and Threshold Determination Based on Quantum Annealing Algorithm and Genetic Algorithm

With the progress of the times, influenced by the rapid development of Internet finance and financial technology, the demand for lending is gradually increasing, and the lending services provided by banks are also gradually increasing, and the calculation of bank loan income is more cumbersome. Therefore, in practice, banks tend to assess the credit of their customers through credit scorecards. The assessment is divided into two types: single credit card assessment and combined credit card assessment. Today's society is a highly centralized information society, quantum computers reflect great potential in computing, storage space quantum bits is 2n times than classical bits, and quantum computing can simultaneously calculate multiple numbers at the same time, in various scenarios graph coloring, traveler problem, vehicle path optimization problem, all reflect the great advantages of quantum computing. To this end, this paper takes the selection and threshold determination of bank scorecards as the research object, establishes the QUBO model for scorecard selection and threshold determination considering single scorecard and combined score cards respectively, and solves the model based on quantum annealing algorithm and genetic algorithm respectively. The case validation shows that the model developed and the applied solution method can effectively find the optimal single and combination scorecards.
Graph Convolution Recommendation Algorithm Combined with Self-Supervised Learning

Aiming at the problem of sparse interactive data of user-item in the current recommendation model, a graph convolutional recommendation model combined with self-supervised learning is proposed, which expands the supervision task through an auxiliary self-supervision task and uses self-discrimination to strengthen the representation learning of nodes. First, a graph convolution model that introduces attention mechanism is constructed as a supervision task to obtain the user and item node representation; Then, adaptive data augmentation is designed for graph contrastive learning as an auxiliary task to fully alleviate the problem of data sparseness; At last, the model is trained by the joint supervised learning and self-supervised learning tasks to obtain the final embedding representation of the nodes. According to the experiment results on two datasets, Yelp2018 and Amazon-Book, compared with the best comparison model, the proposed model improved by 5.52% and 5.62% on Recall and NDCG on Yelp2018, and by 14.02% and 15.43%, respectively, on Amazon-Book.
Multi-Scale and Multi-Path Networks for Simultaneous Enhancement and Super-Resolution of Underwater Images

Underwater images usually struggle with blur and color distortion because of the light absorption and scattering. Moreover, the harsh imaging conditions lead to small target scales and vague details, which pose a great challenge to underwater image recognition and detection. To solve these problems, we propose a unified method for simultaneous enhancement and super-resolution of underwater images. Specifically, our multi-scale network and Multi-path Residual Block (MPRB) can use different receptive fields to fully extract feature information. The Feature interaction attention block (FIAB) captures the long-range relationships between pixels by focusing on the correlation of global regions (such as very distant pixels). Thus, FIABs are embedded at the end of each frequency branch, avoiding the redundancy introduced by additional branches. In addition, the local and global residual learning alleviates the degradation caused by excessive network depth, makes training easier and improves the network performance. Qualitative and quantitative evaluations demonstrate that our method holds the superior performance and achieves the delightful visual effect.
Extensions of Local-Mean K-Nearest Neighbor Classifier with Various Distance Metrics

K-Nearest Neighbor (KNN) classification algorithm is one of the simplest methods in data mining classification technology. The LMKNN algorithm based on this method uses the local average vector of each category to classify the data. However, because distance measurement only uses Euclidean distance algorithm, it is not suitable for some cases. Therefore, this paper proposes a local mean k-nearest neighbor classification algorithm based on different distance algorithms. And compare the classification capabilities of the LMKNN model based on four distance measurement algorithms through experiments. The results show that the LMKNN algorithm based on Minkowski and City block distance metric schemes has more advantages than the LMKNN algorithm based on Euclidean distance metric schemes.
Enhancing Customer Service Recommendation: A Hybrid Model Incorporating Factorization Machine with Temporal &amp; Cross Networks and Behavior Sequence Analysis

This paper presents a novel approach to address the challenges of personalization and efficiency in customer service within the telecommunication industry. The proposed model utilizes behavioral sequences and employs deep learning techniques to develop a customer service recommendation system. By leveraging the temporal dependencies in these sequences, the model accurately predicts the likelihood of customer clicks on various recommended items, thereby enabling the delivery of personalized customer service solutions. To enhance recommendation accuracy and effectiveness, the model integrates several key technologies, including input-aware factor machines, inflated causal networks, and feature crossover networks. These technologies effectively capture associations and higher-order interactions among features, resulting in improved recommendation outcomes. Experimental validation of the model demonstrates significant performance enhancements in customer service recommendation tasks. Overall, the proposed model provides a promising solution for enhancing personalization and efficiency in customer service within the telecommunication industry. Telecom customer service recommendation success rate prediction and click-through rate prediction both belong to binary classification tasks. Experimental results have demonstrated that the model performs excellently in both customer service recommendation and click-through rate prediction tasks.
NBCGP: A Global Pointer based Network with BiLSTM and Chinese Pretrained Model for Named Entity Recognition

The named entity recognition (NER) task aims to identify entities belonging to predefined semantic types (such as persons, locations, organizations, etc.) from a piece of text. Currently, both deep learning-based methods and pretrained model-based methods have achieved good results. However, for Chinese text, existing models have not shown ideal accuracy. This article proposes a novel NER model, based on the Chinese pretrained model NEZHA, bidirectional long short-term memory network (BiLSTM), and Global Pointer technology to build a neural network framework(NBCGP). The model uses NEZHA to capture contextual information for word embedding, further feature extraction is done by BiLSTM, then the outputs of NEZHA and BiLSTM are concatenated to obtain richer semantic information. The dimensionality reduction is performed through fully connected layers, and finally, labeling classification is done using Global Pointer. We tested this model on three common datasets and compared it with other classical models. Experimental results show that this model outperforms existing methods in terms of F1 score and speed, especially on datasets with nested entities. This demonstrates the effectiveness and superiority of this model.
A Method for Improving Urinary Sediment Object Detection of YOLOv5

In order to accurately and quickly detect urinary sediments under diverse background, in this paper, we propose a model for improving urinary sediment detection of YOLOv5 detector and name it ATT_YOLOv5, which mainly combines with CBAM attention in backbone, and it thus could solve the problem of losing semantic and global features caused by convolutional option. Meanwhile, inspired by the way about attention's weights extraction and the structure of adaptively spatial feature fusion, we propose a new detection head module about adaptively all feature share, which names AAFS and it efficiently restrains background information. In order to solve the problem of the lack of medical urine sediments dataset, we construct a standard dataset of urinary sediment, which includes 12 categories and more than 30000 images. We get the best detection result 78.2 % at AP@.5 on the dataset. It is almost 5% higher than YOLOv5 detectors, and the detection performance on objects in almost scale is improved.
Protection of Express Information Using Parallel Computing

In daily life, our express information items are stored in the server of express company. To avoid the leakage of the users' private information, this paper proposes a fast encryption algorithm to protect them. The proposed method first creates multiple threads. Then the information items are grouped into multiple groups with the same number of threads. Next, each thread employs a chaos-based encryption algorithm to encrypt the express information. Finally, the proposed method output the ciphered express information. Security analysis and efficiency test are carried out, and the results show that the proposed method can resist brute-force attack, statistical analysis, and have high efficiency compared with serial encryption mode.
Multi-Person Pose Estimation Based on MobileNet Neural Network

Human pose estimation has always been a hot issue in the field of computer vision. Its main content is to let the computer locate the key points of the character (also called joint points, such as elbows, wrists, etc.) from images or videos. Inferring the poses of multi-person in an image is a unique set of challenges. Human pose estimation is mainly used in fields such as motion recognition, human-computer interaction, intelligent security, and augmented reality. The main problems of human pose estimation include: (1) The image may contain an unknown number of people, who can appear in any position or on different scale; (2) Due to contact or body weight, the interaction between people will cause complex spatial interference, making the association between components difficult; (3) At runtime, complexity tends to increase as the number of people in the picture increases. This problem makes real-time performance a challenge. The main function of this paper is as follows: aiming at the shortcomings of the previous human pose estimation algorithms in model size and computing speed, this paper proposes a new network structure and a bottom-up method, using MobileNet portable network for image feature extraction, using joint points confidence field and part affinity field are used to predict the position of the joint points of the human body and connect the joint points, and use the sparse matrix storage method to filter and store the vectors in the calculation process to complete a base model for bottom-up human pose estimation with better computing speed, referred to as MBHPE.
Research on Mask Wearing Detection Algorithm in Complex Scenes

In recent years, various viral outbreaks have had a huge impact on the world, and wearing masks has become an important public health measure. Incorrect wearing of masks can increase the risk of virus transmission, so how to detect and monitor the correctness of mask wearing has become an important research direction. However, complex factors such as occlusion, dense crowds, and small-scale targets often exist in natural scenes, which have an impact on the detection performance of wearing masks. To address this issue, a mask wearing detection algorithm incorporating attention mechanism is proposed based on the RetinaNet algorithm. Specifically, it is to establish a branch in the residual learning unit of the original target detection network RetinaNet, and incorporate an attention mechanism with feature recalibration characteristics into this branch. The idea of this attention mechanism originates from the human visual system. In deep learning, it can be understood as obtaining the importance of each feature map channel through feature learning, and then assigning weights to each channel based on this importance, thereby achieving the effect of enhancing useful information and suppressing useless information. Due to severe facial occlusion in densely populated scenes, incorporating attention mechanisms into the object detection network can better learn local features of the target by enhancing important information weights, thereby achieving better detection performance. The experimental results show that compared with YOLOv3 algorithm, the detection accuracy of this algorithm on facial targets and facial mask wearing targets is improved by 0.11% and 3.16%, with an average detection speed improvement of 2 FPS.
Branch Cable Path Planning Method Based on “Genetic-A*” Fusion Algorithm

In view of the low efficiency of the traditional branch cable laying method for complex electromechanical products, we propose a method that combines genetic algorithm and A* algorithm for branch point optimization and path planning, using genetic algorithm to optimize the branch point coordinates, calling A* algorithm to plan the cable laying path in the process of branch point coordinate optimization, and keeping the spatial form of the path close to the wall, and then using B spline curve to smooth the path to ensure the authenticity of the cable path. Then the B-sample curve is used to smooth the path to ensure the realism of the cable path. Finally, the feasibility and rationality of the proposed method are analyzed and verified in the simulation experiment.