Reviewerly/exHarmony huggingface dataset:
Fingerprint Identification Technology of Power IOT Terminal based on Network Traffic Feature

Due to the characteristics of heterogeneous hardware and complex access methods in power Internet of Things (IOT) terminal, it is difficult to achieve fingerprint identification through unified deployment of security probe agents. A fingerprint identification technology for power IOT terminal was proposed based on network traffic feature in order to carry out identity authentication, access control and other security protection of IOT terminal based on traffic fingerprint. According to the characteristics of the hierarchical model of power IOT, the technology extracts a traffic feature matrix by deploying traffic capture, analysis, and fingerprint identification model at the edge of the IOT agents, and then classifies and identifies the extracted traffic fingerprint data based on Multi-class support vector machine (MCSVM) to achieve fingerprint identification for accessing the power IOT terminal. The simulation results show that the technology can complete the classification of common power IOT terminal and identify their device types, and has better fingerprint identification accuracy compared to some machine learning algorithms.
Research and Development of Recycling Boats for Floating Garbage

In recent years, the problems of water garbage pollution and water bloom pollution have been widely concerned and discussed. On the one hand, due to the development of industrial activities in recent years, the discharge of garbage in the water body is increasing, which has seriously affected the water ecology. On the other hand, the pollution of water bloom in China is serious, which affects the maintenance of ecological environment and the development of freshwater aquaculture. At present, the equipment and technology for water garbage cleaning in China are still incomplete. In view of the critical situation of environmental pollution and the defects in the existing technology, the team designed and made a new type of water robot, which provides a new type of water surface garbage cleaning and water bloom management robot, which integrates identification, collection and transportation of garbage, and has many functions such as water bloom management and water quality detection. It provides an effective solution to the problem of water quality resources.
Local Matching Relation Network for Few-shot Affordance Classification

Affordance refers to the potential ”operational possibilities” that a target object exhibits in an unknown external environment. Affordance classification is to classify objects with the same function into a class and predict affordance labels, which is a very important skill in the field of intelligent service robots serving human users. Classification models based on deep convolutional neural networks rely on huge amounts of training data, and there is a lack of datasets with a large number of annotations available for affordance classification, which can easily lead to overfitting. Therefore, it is of practical interest for us to train a model that can be quickly adapted to new affordance classification tasks by a small number of samples. Based on this, this paper applies the few-shot learning technique and proposes a local matching relation network. The model is based on relation network, and we propose a multi-scale fusion feature extractor as an embedding module to extract more effective image feature vectors. A local matching module is designed to improve the problem of poor classification due to the large intra-class variation of affordance images. Meanwhile, we build a dataset for affordance classification and conduct some affordance experiments. The experimental results verify the effectiveness of the method in this paper.
A Probability Matrix Factorization for User Behavior Perception Recommendation Model

Accurate representation of user behavior characteristics is one of the key factors to improve the accuracy of collaborative filtering recommendation. However, only using sparse rating data to represent user behavior greatly deteriorates the performance of the recommender system. Extracting more effective features of auxiliary textual information, such as reviews or abstracts, not only can effectively alleviate data sparsity, but also can better improve prediction accuracy. This paper proposes a novel model, namely TCLPMF, which integrates textual Convolutional Neural Network and Latent Dirichlet Allocation to capture milti-level features into a probabilistic matrix factorization model. Extensive experiments show that compared with the traditional recommendation model, such as PMF and ConMF, the mean absolute error of the proposed model are reduced on Movielens dataset.
Mobile Robot Path Planning Algorithm Based on Improved RRT*FN

For the problem that the rapidly-exploring random trees star fixed nodes(RRT*FN) algorithms, with a fixed number of nodes have large randomness and low search efficiency. An improved RRT*FN path planning algorithm for mobile robots is proposed. First, the improved algorithm inherits the advantages of RRT*FN in optimizing memory and introduces a heuristic sampling strategy to add high-performance nodes to replace inefficient ones when the total number of nodes in the tree reaches a preset value. Secondly, an adaptive extension strategy is used to automatically adjust the weights of tree growth toward random and target points by collision detection methods. Finally, it is demonstrated through simulation experiments that the improved algorithm takes into account the exploration of obstacle-dense and narrow regions while fixing the number of nodes and optimizing the memory. The improved algorithm is more efficient in the path planning process and has stronger environmental adaptability.
Research on Closed loop Control of Steering Angle for Intelligent Patrol Wheeled Robot

This paper uses the optimized PID control algorithm to realize the angle control of the intelligent patrol wheeled robot. Because the servo motor control mode used is the absolute value type, each control only needs to give the absolute speed value required by the motor servo control, not the speed increment value, so the optimized position type PID control algorithm is used as the calculation method of output control. This method designs the lateral distance deviation control, steering closed-loop control, acceleration and deceleration control through the navigation control of the intelligent patrol wheeled robot, and realizes the lateral deviation control in the navigation process of the intelligent patrol wheeled robot and the dynamic acceleration and deceleration control when the speed changes. In order to test the navigation accuracy of the navigation system, this paper also conducts navigation experiments under the simulation environment of the intelligent patrol wheeled robot, which verifies the effectiveness of the navigation algorithm.
Prediction Of Aluminum Electrolytic Cell Condition based on WOA-TCN Network

The cell condition plays a very important role in the stability and efficiency of production in the aluminum electrolysis production process. In order to enhance the stability of aluminum electrolytic cell condition, a prediction model of aluminum electrolytic cell condition based on whale optimization algorithm and temporal convolutional network is proposed. Firstly, the time series features are input into the TCN prediction model for pre-training, and then the trained TCN model is adjusted using the whale optimization algorithm to adjust the hyperparameters, and the WOA-TCN combination model is constructed to predict the condition of the aluminum electrolytic cell. The results show that using WOA-TCN combination model can predict the cell conditions accurately, thus providing effective practical guidance for the actual electrolytic aluminum production.
A Fractional-order Neural Network for Solving Nonsmooth Programming<sup>*</sup>

In this paper, a fractional-order neural network is designed to solve non-smooth programming with linear equality constraints. First, we transform the feasible domain of the problem by extreme value mapping. We design a fractional-order neural network model by using the differential inclusion principle. The existence criterion is established by using the KKT condition for the existence of the optimal solution to the original problem. We select the appropriate Lyapunov function to prove the global stability of the equilibrium point of the fractional-order neural network. Finally, a numerical example is given to verify the obtained results in this paper.
Improved Sparrow Search Algorithm Based On Adaptive Hybrid Strategy

Sparrow Search Algorithm (SSA) is a novel swarm intelligence optimization algorithm that has been shown to have good optimization performance. However, SSA has some drawbacks, such as being prone to getting stuck in local optima during the iterative process, low solution accuracy, and slow convergence speed. To address these issues, a Sparrow Search Algorithm based on an adaptive hybrid strategy is proposed. Bernoulli mapping is used to initialize sparrow individual positions, which increases population diversity. By combining the flight mode of the Levy Flight Optimization algorithm, the position update strategy of the followers is improved, enhancing the algorithm’s global search ability. An adaptive weight factor is introduced to strengthen the search ability and convergence speed. The position update method for explorers and followers is adjusted to some extent to prevent the algorithm from getting stuck in local optima. In simulation experiments, ISSA is compared with SSA, DE, PSO, SCA, CSA, and 5 basic algorithms based on 11 benchmark test functions. The results show that ISSA performs better in terms of optimization accuracy, convergence speed, and has better stability, enhancing its ability to avoid getting stuck in local optima.
Research on Optimization of Traffic Flow Prediction Algorithm

To improve the accuracy of traffic flow prediction based on wavelet neural networks, an optimization algorithm for traffic flow prediction was proposed. The initial parameters of the wavelet neural network are optimized using the global optimization, fast convergence speed, and strong adaptive random search capabilities of the beetle antennae search algorithm, thereby overcoming the shortcomings of the wavelet neural network that is prone to fall into local optimization and cannot obtain optimal parameters. The BAS-WNN optimization algorithm is proposed and the traffic flow prediction model based on BAS-WNN is established. The experimental results show that the RMSE of the BAS-WNN traffic flow prediction algorithm is 8.0289, and the MAE is 5.3416. Compared with the WNN prediction algorithm, the prediction accuracy is significantly improved, with better prediction performance.
Design of coal mine safety risk index analysis system based on big data

In order to meet the requirements of mine intelligent construction which requiring the reduction of person, the increasment of safety and the improvement of efficiency, a coal mine safety situation awareness analysis platform based on multi-source data fusion is designed and implemented by using the mode of “platform + end” and micro service. Through multi protocol adaptation, coal mine safety risk data integration is realized, data credibility measurement and analysis indicators are established, and safety risk data quality judgment is realized. Based on the neutral reference object, we have established the risk evaluation index system and builded the two-level risk evaluation model applied to the company and the mine, which realizing the risk quantitative and hierarchical evaluation. The automatic identification of mine typical anomalies is realized by establishing the characteristic map of monitoring data. The visual analysis of safety situation is realized by using GIS technology and spatial interpolation technology. The field application reflects that the platform improves the ability of mine safety risk identification and risk exception handling, and provides guarantee for coal mine safety production.
MSFNet: A Multi-Level Semantic Feature Fusion Network for Instance Segmentation of X-ray Security Images

In order to eliminate security risks and reduce the probability of terrorist incidents, X-ray security scanners have been installed in places such as airports, customs and railway stations to check whether luggage packages contain prohibited items. At present, the security inspection is manually checked by professional security personnel. In the face of a large number of objects to be detected, the efficiency of manual detection is low, and the detection results are susceptible to human experience and psychological interference. The design of automatic identification and detection of prohibited items in X-ray security inspection scenes can effectively improve the efficiency of security inspection and reduce the difficulty of potential risks and human operation. With the development of deep learning in recent years, computer vision technology can realize fast and reliable automatic detection of prohibited items. However, due to the penetrating imaging nature of X-ray, the X-ray security images contain a lot of occlusion and overlap, which increases the difficulty of identifying prohibited items. Based on the SCNet <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> algorithm, this paper proposes an instance segmentation algorithm multi-level semantic feature fusion network(MSFNet). MSFNet enhances the upsampling ability of the model to recover detailed information, and effectively fuses low-order shallow detail information and high-order abstract semantic information. Firstly, a dynamic upsampling feature fusion module(DUFM) was proposed to capture a longer range of context information and reduce the interference of occlusion noise. Secondly, a multi-stage global semantic extraction module(MGSEM) is proposed to extract multi-scale global semantic information from semantic features of different granularities. Experiments on the Pidray dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</sup> show that MSFNet has further improvement in accuracy compared with common instance segmentation algorithms, and the accuracy index in detection and segmentation tasks reaches 72.3%and 62.4% mAP respectively.
An NTRU-based Homomorphic Encrypted Data Analysis System

In the current data processing systems, there exist widespread threats to the privacy of data providers. To solve the problems in privacy protection in data processing, this paper proposes a homomorphic encryption data analysis system-MeSec, which is based on NTRU cryptosystem. Firstly, MeSec constructs a new type of password-based authorized key exchange protocol (PAKE). Secondly, MeSec adopts a more efficient NTRU-based homomorphic encryption system. And respectively designs a federated learning scheme for encrypted distributed training models and a privacy machine learning prediction scheme whit the encrypted private data as inputs; finally, an effective multiuser agent NTRU-based re-encryption module is designed to realize safe and convenient data intercommunication and sharing between users, which completely breaks the barriers of data islands. This system can be widely used in various fields for data sharing, analysis, and processing.
Research on the influencing factors of LLVM IR optimization effect

The intermediate representation has a natural advantage in solving the problem of cross-architecture binary code similarity detection, which can greatly reduce the amount of data required for machine learning model training and improve the versatility and scalability of the model. optimizing the intermediate representation can solve the problem of binary differences caused by different compilation architectures, compilers, optimization options, and obfuscation strategies, which is conducive to improving the accuracy of binary code similarity detection. We explored the effects of compilation architectures, compilers, optimization options and obfuscation strategies on the optimization of intermediate representations through four experiments. The experimental results show that the compilers GCC and Clang at the time of binary file compilation have a significant improvement on the optimization effect of intermediate representation. In addition, the optimization effect using opt-Ol and opt-O2 is the best, and the similarity can be improved by up to 21.7%. The compiler architectures XS6_32, XS6_64, ARM32, ARM64 and optimization options −OO, −Ol, −O2, −O3 have little effect on the optimization effect of the intermediate representation. The compiler architecture MIP32 and obfuscation strategies F1a, Sub, Bcf, All have a negative effect on the optimization effect of intermediate representation.
Real -time face expression recognition network based on attention mechanism

Convolutional neural networks are widely used in the field of face expression recognition. In order to meet the problem of high recognition rate and lightweight model of facial expression recognition algorithm based on convolutional neural network, this paper proposes a real-time facial expression recognition network based on attention mechanism. First of all, this paper uses the Mini-Xception convolutional neural network, which removes the fully connected layer of the traditional model and uses the depth-wise separable convolution instead to reduce the model parameters. At the same time, the convolutional block attention model CBAM attention mechanism is added to the network architecture to improve the detection effect of the attention target, so as to improve the network performance without increasing the network parameters. Finally, the elu activation function was introduced to enhance the robustness of the model. The experimental results show that the recognition rate of the proposed method on the public facial expression dataset FER-2013 reached 72.9%, which has a higher accuracy than the current advanced expression recognition network. At the same time, the expression recognition system interface is designed to test different expressions, and the time per frame is about 0.3s while achieving fast and accurate testing. Therefore, the improved network in this paper can realize fast and accurate facial expression recognition.
A Chinese short-text oriented intent classification network based on BERT and Bi-GRU

With the development of computer technology, Internet applications have penetrated into all aspects of people’s lives, and now people are more inclined to get answers from these applications when they encounter problems. To accurately answer users’ questions, the prerequisite is to be able to accurately identify the intended category of users’ questions. In this regard, this paper proposes a short textual intent classification network BERT-DBGRU based on BERT and Bi-GRU, which uses BERT as the word embedding layer and a two-layer Bi-GRU to complete the contextual feature extraction of the input sequence. Also, in order to utilize the features of the word vectors obtained from BERT as much as possible, the model also uses residual connectivity to fuse the word vector features with the output of the GRU network, after which the classification task is completed using the maximum pooling layer and the fully connected layer. After experimental verification, the accuracy of the method in this paper reaches 83.44% on the text classification task, and the effectiveness of this method on the text classification task is proved by comparing with classical networks such as TextCNN, TextRCNN.
FAS-reid: Fair Architecture Search for Person Re-IDentification

The development of deep learning has driven the development of ReID, and more and more excellent methods have been proposed, but most of these are artificially designed network backbones. Automation is a trend in the development of deep learning. This paper propose FAS-ReID(Fair Architecture Search for Person Re-IDentification), which can automatically design and generate a neural network backbone for ReID. This paper compare operation selection to competition, and inject noise to make the competition fairer, while making the architecture of automated search More adaptable to ReID We use TriHard loss to improve the feature extraction ability. Our experiments show that the backbone searched by FAS-ReID is better and reduces the search time. Meanwhile, as for the problem of performance collapse caused by skip-connection enrichment in the search process, FAS-Reid does not use the early stop strategy to avoid performance loss like other solutions, which is also the reason why our method is more reliable and robust.
A Manual Annotation for the Task of Pronoun Resolution for Events

In this paper, we add an annotation for the task of Chinese pronoun resolution for events to the existing dataset. We first segment the article by sentence and parse the sentence into consistency parsing. Then we collect the pronouns from the consistency parsing. After that, we annotate them manually. Finally, we employ an existing model which contains BERT, LSTM, and MLP modules to test the performance of the annotated data. We also use two strategies to balance the number of positive and negative samples, making the F1 metric reach 0.537.
Preserving edge with Transformer for deep image inpainting

Most inpainting methods adopts convolutional neural networks (CNNs) due to its remarkable modeling capabilities. However, CNNs have certain limitations in understanding the global edge structure due to their inherent properties, such as local inductive priors. This has led to poor performance in reconstructing reasonable edge structures. Recently, deep generative methods have made considerable progress in image restoration by introducing edge priors. In contrast to CNNs, Transformers have demonstrated powerful capabilities in edge reconstruction. To this end, we propose a two-stage model PE-Net that combines a Transformer edge generator and a CNN image generator network. The edge generator is responsible for reconstructing the edges of the missing regions of the image, while the image generator uses the edges from the previous stage as auxiliary information to fill the missing regions. We conduct qualitative and quantitative experiments on the publicly available datasets CelebA and Places2, and the results demonstrate the superiority of the proposed method.
Research on Intelligent SEI Method for Navigation Radar Based on Metric

Specific emitter identification (SEI) of radars is an important function of electronic reconnaissance. Aiming at the individual identification of navigation radars, this paper provides a method combining metric learning and deep learning. That method extracts the front & back edge and instantaneous frequency of pulse envelop based on original pulse waveform data as initial input features, then combines the central loss function and cross entropy loss function to training the deep neural network so as to extract the individual features, at last makes identification to radar individuals through the optimized fully connected classification network. The test results show that the proposed method can effectively improve the performance of critical parameters including cluster effect of radar individual features and correct individual identification rate etc.
A RGB-UV Facial Skin Image Translation Method

In dermatology and cosmetology, ultraviolet (UV) images can reflect deep facial problems better than RGB images, but its reliance on UV imaging devices limits its clinical application and widespread popularity. This paper proposes a UV facial image generation method without using any UV imaging UV equipment. Specifically, many RGB-UV facial skin image pairs are collected and a dataset is then built. The dataset includes high-resolution RGB-UV image pairs of different positions on the face of people in different age groups. By using the dataset, a generative adversarial network-based method for facial skin image translation is proposed, which achieves better results in RGB-UV image conversion than some compared image-to-image translation methods.