Reviewerly/exHarmony huggingface dataset:
Real-time Local Feature with Global Visual Information Enhancement

Local feature provides compact and invariant image representation for various visual tasks. Current deep learning-based local feature algorithms always utilize convolution neural network (CNN) architecture with limited receptive field. Besides, even with high-performance GPU devices, the computational efficiency of local features cannot be satisfactory. In this paper, we tackle such problems by proposing a CNN-based local feature algorithm. The proposed method introduces a global enhancement module to fuse global visual clues in a light-weight network, and then optimizes the network by novel deep reinforcement learning scheme from the perspective of local feature matching task. Experiments on the public benchmarks demonstrate that the proposal can achieve considerable robustness against visual interference and meanwhile run in real time.
SFME: Score Fusion from Multiple Experts for Long-tailed Recognition

In real-world scenarios, datasets often perform a long-tailed distribution, making it difficult to train neural net-work models that achieve high accuracy across all classes. In this paper, we explore self-supervised learning for the purpose of learning generalized features and propose a score fusion module to integrate outputs from multiple expert models to obtain a unified prediction. Specifically, we take inspiration from the observation that networks trained on a less unbalanced subset of the distribution tend to produce better performance than networks trained on the entire dataset. However, subsets from tail classes are not adequately represented due to the limitation of data size, which means that their performance is actually unsatisfactory. Therefore, we employ self-supervised learning (SSL) on the whole dataset to obtain a more generalized and transferable feature representation, resulting in a sufficient improvement in subset performance. Unlike previous work that used knowledge distillation models to distill the models trained on a subset to get a unified student model, we propose a score fusion module that directly exploits and integrates the predictions of the subset models. We do extensive experiments on several long-tailed recognition benchmarks to demonstrate the effectiveness of our pronosed model.
Markov Decision Process for imbalanced classification

In imbalanced classification problems, the samples of different classes are so imbalanced that the model cannot effectively identify the minority class samples. To solve this problem, this article proposes a new algorithm which is named TargetValue algorithm. It constructs a Markov Decision Process according to the imbalanced data set. And the reward function is carefully designed. Since the constructed Markov Decision Process has simple dynamics, the action value function can be directly calculated by derivation and handed over to the neural network for fitting. The neural network classifies unknown samples by comparing the values of different action. This article analyzes the reasons for the effectiveness of the algorithm from two perspectives: the reward function influence both the target value and the gradient of the long-term expected return. And binary classification and multi-classification experiments on multiple imbalanced data sets are conducted to verify the effectiveness of the algorithm.
Research on Multi-target Tracking Algorithm Based on Classified Data Association

Data association, aiming at matching multiple targets between frames, is a key step in multi-target tracking. In this paper, we propose a multi-target tracking algorithm based on classified data association. We first use the RetinaNet detection algorithm to quickly obtain highly accurate detection results to provide reliable data for subsequent data association. Then, we build a model for the motion of the target and obtain the candidate association values of the target in the current frame according to the established model, thus eliminating the influence of interference detection values and reducing the possibility of false matches in data association. Finally, a classified data association method is proposed. The association scenarios are classified according to the association strength and number of target candidate associations, and specific data association methods are proposed for different association scenarios. The effectiveness of the proposed algorithm is confirmed by extensive experiments, and the experimental results show that the algorithm has high tracking accuracy and precision.
Channel Estimation of 5G New Radio using Convolutional Neural Network

Due to high throughput, low latency, reliability, and massive communication, 5G and beyond networks have become popular in the era of high-speed wireless communication. This high-frequency multi-path transmission suffers from attenuation, delay, and distortion which affects the signal transmission. In this practical scenario, it is necessary to know or estimate the channel to reconstruct the original signal. The effective channel estimate is a crucial component of the receiver. In this study, deep learning aids in channel estimation for a 5G-and-beyond network under the level of mobility given by the Doppler impacts. Utilizing the deep learning feature to estimate the channel, lowers complexity and improves the accuracy of wireless systems. The mean square error (MSE) results of channel estimation show that the convolutional neural network (CNN) outperforms the linear interpolation for all the tapped delay line (TDL) modes. Similarly, CNN MSE results for TDL-A are 72%, TDL-B 7%, TDL-C 10%, TDL-D and TDL-E 44% less than the practical estimator for 10 dB SNR.
Self-attention based GRU neural network for deep knowledge tracing

How to provide students with personalized guidance and personalized teaching is a key concern in the field of education. Knowledge tracing can analyze students' exercise records, trace students' learning situations in real-time, obtain students' knowledge mastery, predict students' future performance, and provide a new way to teach students in accordance with their aptitude. Aiming at the problem that the traditional model loses information under long sequence data, this paper uses GRU neural network combined with attention mechanism to realize deep knowledge tracing model, which improves the sensitivity of the model to long sequence data, strengthens the influence of historical knowledge state on future answer performance, and improves the prediction accuracy of knowledge mastery. The experiment has achieved good results in public datasets.
Macro modeling of liquid crystal cell using machine learning method: reservoir computing approach

Abstract A macro model of liquid crystal cells including electrical and optical behaviors has been developed using a machine learning framework called reservoir computing and implemented into a circuit simulator. Assuming the arbitrary time steps given from the circuit simulator, we confirmed that our model in which the time-continuous reservoir update equation is discretized by a fourth-order Runge–Kutta method shows high prediction accuracy even at the different time steps from that in the training phase. The director distribution of liquid crystals, which is the microscopic state that realizes the specific macroscopic characteristic, capacitance, and transmittance, is not uniquely determined. Therefore, it is essential to utilize the reservoir’s ability to memorize history to improve prediction accuracy. We found it effective to adjust the parameters that control memory length and update speed according to the response time of each capacitance and transmittance.
A Federated Domain Adaptation Algorithm Based on Knowledge Distillation and Contrastive Learning

Smart manufacturing suffers from the heterogeneity of local data distribution across parties, mutual information silos and lack of privacy protection in the process of industry chain collaboration. To address these problems, we propose a federated domain adaptation algorithm based on knowledge distillation and contrastive learning. Knowledge distillation is used to extract transferable integration knowledge from the different source domains and the quality of the extracted integration knowledge is used to assign reasonable weights to each source domain. A more rational weighted average aggregation is used in the aggregation phase of the center server to optimize the global model, while the local model of the source domain is trained with the help of contrastive learning to constrain the local model optimum towards the global model optimum, mitigating the inherent heterogeneity between local data. Our experiments are conducted on the largest domain adaptation dataset, and the results show that compared with other traditional federated domain adaptation algorithms, the algorithm we proposed trains a more accurate model, requires fewer communication rounds, makes more effective use of imbalanced data in the industrial area, and protects data privacy.
Multi-Functional Resource-Constrained Elliptic Curve Cryptographic Processor

With the rising data evolution, the demand for secured communications over networks is rising immensely. Elliptic Curve Cryptography (ECC) provides an attractive solution to fulfill the requirements of modern network applications. Many proposals published over the year over different variants of ECC satisfied some of the issues. Nevertheless, modern network applications such as Internet-of-Thing (IoT) and Software-Defined Networking (SDN) put the requirements on various aspects and can only be solved by different ECC algorithms. Looking at this point of view, an efficient architecture that could combine multiple ECC algorithms becomes an urgent request. In addition, even though many investigations of ECC on Field-Programmable Gate Arrays (FPGA), an efficient architecture that could be well-deployed on Application-Specific Integrated Circuit (ASIC) needs to get more study. Therefore, this paper proposes an area-efficient ECC hardware design that could integrate multiple ECC algorithms. The proposed design is deployed on both ASIC and FPGA platforms. Four well-known ECC-based Digital Signature Algorithms (DSAs), which are the Edwards-curve Digital Signature Algorithm (EdDSA) with Curve25519, Elliptic Curve Digital Signature Algorithm (ECDSA) with National Institute of Standards and Technology (NIST) Curve P-256, P-384, and P-521, are implemented. Furthermore, the design supports all DSA schemes: public-key generation, signature generation, and verification. We also provide optimized calculation flows for modular multiplication, modular inversion, point addition, point doubling, and Elliptic Curve Point Multiplication (ECPM) for two different elliptic curves: the NIST curve and Edward curve, on a unique architecture. The calculation processes are designed in projective coordinates and optimized in time and space to achieve a high level of parallelism. The proposed ECC processor could run up to 102-MHz on ASIC 180-nm and 109.7-MHz on Xilinx Virtex-7. In terms of area, The processor occupies 377,471 gates with 4.87- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$mm^{2}$ </tex-math></inline-formula> on the ASIC platform and 11,401 slices on the FPGA platform. The experimental results show that our combinational design achieves area-efficient even when compared with other single-functional architecture on both ASIC and FPGA.
Development of Google Slide-based learning media on the topic of social arithmetic

Teaching and learning activities using technology are the biggest challenges in education today. One of the uses of technology is the application of Google Slides in learning mathematics. This study has a goal to produce a Google slide-based mathematics learning media on the topic of social arithmetic. This development research uses the Plomp development model. The research subjects were 56 students of class VIIA and VIIB of SMPN 1 Tajinan. In this study, the instruments used were validation sheets, student response questionnaires, and student learning outcomes tests. The validity of the google slides-based mathematics learning media was tested by 3 validators. The results of the validation of the three validators obtained a score of 3.39 which means that it is in the valid category. The results of the student response questionnaire obtained a score of 3.5, which means that the criteria are very practical. For the effectiveness of the Google Slide-based mathematics learning media, it can be seen from the completeness of classical learning outcomes which reached 78.57% which were in good criteria.
Medical Data Clustering Based on Multi-objective Clustering Algorithm

With the development of massive medical data, clustering algorithm becomes an effective way for medical data processing and data mining. On the one hand, it helps medical learners find effective information patterns from massive data; on the other hand, it promotes the development of medical technology and increase productivity. For traditional clustering algorithm, a single clustering index is difficult to meet people's needs of diversity and comprehensiveness. In contrast, multi-objective clustering (MOC) considers multiple objectives at the same time, and comprehensively deals with various clustering problems and standards, such as compactness, diversity of feature selection and high data dimension. Artificial bee colony algorithm (ABC) has a faster speed and embodies the idea of swarm intelligence. It imitates the optimization process of bees, and finally obtains the global optimal value. On this basis, this paper proposed a multi-objective artificial bee colony clustering algorithm (MOC-NABC) that is combined with current better-performed clustering algorithm. It takes normalized mutual information (NMI), Calinski-Harabasz (CH), Fowlkes-Mallows index (FMI) and silhouette coefficient (SC) of clustering as the final evaluation indexes. The experiment on UCI mouse protein gene dataset shows that the overall performance effect is greatly improved, e.g. compact clustering and the effective utilization of data features.
Image Encryption Algorithm Based on a New Five-Dimensional Lorenz System

For the question of key space of image encryption algorithm based on the low dimension chaotic system is small and the safety is not high. On the basis of four-dimensional hyperchaotic Lorenz system, by adding the dimension of differential equations and adding a nonlinear term, this paper establishes a new five-dimensional hyperchaotic Lorenz system. And a digital image encryption algorithm based on this hyperchaotic system is proposed. First, we prove that the system is a hyperchaotic system, through the Lyapunov exponents calculation and simulation result of the trajectory and Chaotic attractor phase diagram. Then, a digital image encryption algorithm based on this hyperchaotic system is implemented. By analyzing the security performance of the algorithm, we found the algorithm is extremely sensitive to key, with very good diffusivity, and it can resist all kinds of attacks effectively such as brute force attack, chosen-plaintext attack and known-plaintext attack.
Construction of Color Network Model of Folk Painting Based on Machine Learning

In order to more accurately explore and reflect the color design thinking of folk paintings, and provide more reference and inspiration for the current design with Chinese characteristics and ethnic characteristics, a research on the construction of color network model of folk paintings based on machine learning was proposed. By calculating the folk painting color feature point set, the concentric circles coordinate system, constructing the corresponding feature points according to the folk painting color main curvature, the tectonic pattern of folk painting color description factor, combined with folk drawing design of color invariants, matching the folk painting color, the introduction of image histogram constraint, nonlinear histogram transformation model is set up, According to the contrast distortion adjustment algorithm, the model is solved, and the folk painting color network model is constructed. The case shows that the color network model can accurately reflect the color characteristics and color matching design logic of Ansai folk paintings.
A Method for Residual Network Image Classification with Multi-scale Feature Fusion

The traditional convolutional neural network (CNN) only focuses on the features of the last layer of the network, ignoring those of other layers. The detailed information of the shallow network can improve the accuracy of classification to some extent. With the deepening of the network, the problems such as gradient disappearance and explosion are obvious. In order to alleviate this problem, a method for residual network image classification with multi-scale feature fusion is proposed in the paper. First of all, the Random Image Cropping and Patching (RICAP), a data augmentation method, is adopted to cut and splice the new training samples from the training set, based on which feature maps of different sizes are obtained by residual modules. Secondly, after reducing the dimension of high-level feature maps, all the feature maps with the same dimension are processed by the multi-scale fusion. Finally, these feature vectors are input into the Softmax classifier for training and classification, and the method of learning rate decay is used in the training process. The experiment results indicate that this method has a good performance on image classification based on the public datasets of MNIST and CIFAR-10.
Optimal Revenue Analysis of the Stubborn Mining Based on Markov Decision Process

As one of the most popular cryptocurrencies, Bitcoin is essentially a decentralized ledger. Each node maintains the security of the blockchain through the workload proof mechanism, and the block that obtains the accounting right will receive a block reward in the form of Bitcoin. Because the Bitcoin system follows the “longest legal chain” principle, when a fork occurs, orphan blocks will inevitably be generated, and some miners’ computing power will be a waste. In recent years, researchers have discovered that miners can obtain profits disproportionate to their own computing power by deviating from Bitcoin’s honest mining. Selfish Mining (SM1) is a case in dishonest mining strategy, and dishonest miners (attackers) can obtain higher returns by retaining the blocks they create and selectively delaying their release. The stubborn mining strategy is a generalized form of selfish mining. It increases the revenue of the stubborn miner by adopting a wider range of parameters. Its three mining strategies are: Lead-Stubborn, Equal Fork stubborn and Trail stubborn. The mining problem can be formulated as a Markov Decision Process (MDP), which can be resolved to give the optimal mining strategy. This work describes the three mining strategies of stubborn mining as a Markov decision process, solves it and gives the lower bound of the highest return under the optimal stubborn mining strategy. Our experimental results demonstrate that the revenue of the optimal stubborn mining strategy is higher than SM1 under certain circumstances, and this strategy allows dishonest miners (stubborn miners) to obtain revenue that does not match the actual computing power paid.
Semi-supervised Learning with Nearest-Neighbor Label and Consistency Regularization

Semi-supervised learning, a system dedicated to making networks less dependent on labeled data, has become a popular paradigm due to its strong performance. A common approach is to use pseudo-labels with unlabeled data for training, however, pseudo-labels cannot correct their own errors. In this paper, we propose a semi-supervised method that uses nearest neighbor samples to obtain pseudo-labels and combines consistency regularization for image classification. Our method obtains pseudo-labels by computing the similarity of the data distribution between the weakly-augmented version of the unlabeled data and the labeled data stored in the support set and combines the consistency of the strongly-augmented version and the weakly-augmented version of the unlabeled data. We compared with several standard semi-supervised learning benchmarks and achieved a competitive performance. For example, we achieved an accuracy of $$94.02\%$$ on CIFAR-10 with 250 labels and $$97.50\%$$ on SVNH with 250 labels. It even achieved $$91.59\%$$ accuracy with only 40 labels data in the CIFAR-10.
Performance Improvement of Classification Model Based on Adversarial Sample Generation

The bad information in the network is often filtered by the neural network model, which is easy to be attacked by various adversarial samples. In order to improve the text filtering ability of the neural network model, it is necessary to make the filtering model learn more bad text feature information, especially the feature information that is not recognized by the filtering model at present. Therefore, having more abundant and diverse high-quality data set is one of the ideal methods to improve the accuracy of neural network filtering model. First of all, aiming at the generation of Chinese adversarial samples, we propose a method to generate semantically similar adversarial samples based on GPT2 model. At the same time, we put forward the mutation strategy by using three kinds of mutation methods (homophonic substitution, visual replacement and letters replaced), in order to extend the data set. So that we can improve the performance of classification models. After retraining the classifier with the expanded data set, the accuracy of the LSTM model of the classifier is improved from 82% to 93%.
Distributed Power Load Missing Value Forecasting with Privacy Protection

In the era of the Internet of Things (IoT) supporting 5 G technology, the Smart Grid (SG) is an important part of Smart City. Specifically, load forecasting is a key ingredient of the sustainable development of the SG. Similarly, the data collected from the IoT devices (i.g., smart appliances, smart meters) is one of the key factors to improve the accuracy of the prediction results. However, two challenges are present in the process of data collection, where we use multi-source data for load forecasting. First, in the process of collecting data from different platforms, we should protect the users’ privacy information. Second, data loss is caused by some reasons (i.g., equipment power failure and communication failure), which affects the accuracy of load forecasting. Considering the above challenges, we propose a new distributed Locality-Sensitive Hashing (LSH) method for load forecasting, named $$LF_{dLSH}$$ . At last, a case study is put forward to illustrate the feasibility of our approach.
Federated Community Detection in Social Networks

Community detection is an effective approach to unveiling relationships among individuals in social networks. Detecting communities without privacy leakage remains an area of ongoing and indispensable focus. Therefore, anonymization and differential privacy based community detection methods are proposed to protect the privacy of social network information. However, the above methods cause inevitable accuracy loss in some way, resulting in the low utility in the final community division. In this paper, we propose a secure and efficient interaction protocol based on homomorphic encryption to find the index of the maximum value of encrypted floating-point numbers. Besides, we design a novel federated community detection framework, using user-server interactions to adjust and construct global optimal community division results, which could not only get an effective community division model but also guarantee strong privacy preservation. Through theoretical analysis and empirical experiments, the time cost of our proposed secure protocol is $$4\times $$ faster than previous works. Meanwhile, our framework ensures modularity error in the range of 0.03 comparing with the plaintext framework, and modularity improves at least 0.3 with 3 other state-of-the-art privacy-preserving community detection schemes.