Reviewerly/exHarmony huggingface dataset:
Self-supervised Recommendation Model Combined with Constrastive Learning Function

Aiming at the problem of data deficency and cold start in recommendation algorithm, this paper propose a self-supervised recommendation model combined with constrastive learning function(CLSRec). The model completes self-supervised learning through two positive-and-negative case-based constrastive learning tasks clotfilling prediction and subsequence prediction. Firstly, the algorithm masks a individual item and subsequence of the user interaction respectively according to the corresponding task requirements, and then predicts the missing items through the encoding layer and the self-attention layer. During the process, the model automatically constructs the positive and negative examples of the prediction results, and completes the training according to the constrastive learning loss function corresponding to the task, so as to learn the correct semantic feature representation and space vector coding. Secondly, the model utilizes the trained upstream item coding for the downstream prediction. Finally, the model can obtain user-satisfactory recommended results. By comparison of experimental results on Beauty, Toys and LastFM, the CLSRec works better on multiple metrics.
CNN-Zero: A Zero-Shot Learning Framework for Jamming Identification

Anti-jamming is a critical issue of wireless communication security, where jamming identification is an important pre-stage of anti-jamming. However, it is challenging to perform a jamming identification task in the absence of some jamming classes. To overcome this obstacle, we propose a zero-shot learning framework CNN-Zero, which aims to identify the known and unknown jamming signals. Specifically, we employ CNN to learn the potential representation of the semantic feature space of jamming signals. Then, we build a hybrid loss function consisting of attribute distance loss, cross entropy loss and reconstruction loss to ensure the semantic features have greater minimum inter-class distance than maximum intra-class distance. Finally, we build an appropriate distance measurement matrix to identify known and unknown jamming signals. Experimental results prove that compared with the supervised method using neural networks, CNN-Zero achieves a better average accuracy between eight jamming signals even in the absence of training samples.
Life Detection Based on UAVs - Thermal Images in Search and Rescue Operation

Disasters arise frequently on a global scale and not only seize human lives but also create difficulties to rescue workers, some factors that affect a victim's chances for rescue is the speed in which they are located and dug out. The rescue team work hard to save people by using methods such as trained dogs and robotics, but the resources and time required to deploy a rescue team remain a major issue that reduces people's chances of survival. In recent years, some systems have been developed to speed up the detection of people in disaster scenarios. Today, UAVs are widely deployed in search operations, as they can seize and control a huge region in a period of time. Moreover, with a base in the sky, UAVs can quickly detect disaster-stricken areas to rescue people. Thus, the accurate detection of individuals in images or videos obtained by drones is a well-suited tool to aid in search and rescue (SAR) missions. Therefore, this research explores the reliability of the yolov4 detection model with the collection of thermal images dataset to address the challenge of bad weather conditions, given that thermal images are not impacted by light or bad weather.
Pre-established Entanglement Distribution with Time-Guaranteed Memory Cells Selection in Quantum Networks

Quantum entanglement enables numerous quantum applications such as secure communication. Based on entanglement-based quantum networks, long-distance entanglement establishment between two distant end nodes is vital to realize practical quantum networks. However, due to the decoherence and imperfect quantum operations such as the non-determinism of entanglement swapping, the latency of remote entanglement distribution will be increased, simultaneously, ongoing quantum operations will be cut off caused by the short coherence time of quantum memory. In this article, we propose a pre-established entanglement distribution algorithm which contains a time-guaranteed memory cells selection algorithm, aiming to choose the maximum time-matching entangled particle in each node to establish end-to-end entanglement. Simulation results show that for the different scales of grid network topologies, all success probability values are increasing with increase the size of the memory cells and the size of the time slots. Considering constructing the large scale of quantum network, it's necessary to consider employing high multi-mode capability and long lifetime quantum memory.
Telecom Fraud Detection via Imbalanced Graph Learning

In recent years, an increasing number of telecom frauds have caused huge losses to people around the world. Graph neural network(GNN) brings new possibilities for telecom fraud detection. However, due to the existence of the imbalance problem in the graph, it is difficult for general GNN models to detect a small number of positive samples. To address this problem, we design a new GNN-based fraud detector. First, we transform the node features with a multilayer perceptron. Subsequently, a reinforcement learning-based neighbor sampling strategy is designed to balance different classes of node neighbors. Then the node features are aggregated using GNN, while the focal loss function is used to measure the model error. We conducted experiments on two real-world telecom fraud datasets, and the results show that the proposed method does have competitive performance, especially for the graph imbalance problem.
Quantum Cryptography Technology and Application in Smart Grid

Recently, more and more governments and institutions are paying attention to quantum security communication because of its strict security. In China, some significant progresses have been made in practice when the research goes deeper. This paper investigates some key technologies of quantum cryptography in smart grid of China. First, the applications and challenges of quantum communication in smart grid are depicted, then the key technologies utilized in smart grid based on quantum key distribution (QKD) are explored in tail. At last quantum secure communication, including fault locating, long distance transmission and WDM technology are discussed. Our researches on the application of quantum cryptography in smart grid are very effective and hopeful to be extended to more application fields.
Facial Expression Recognition Based on Semi-supervised Vision Transformer

The purpose of Facial expression recognition is to classify human expression using the designed algorithm. FER2013 database which contains 30,000 facial expressions in 7 groups is an emerging human emotion database used in training models. Furthermore, semi-supervised learning is a data processing technique, which is gradually gaining importance in natural language processing with the support of computer technology. Semi-supervised combines supervised learning and unsupervised learning. It learns from a small number of labeled examples to predict a large amount of unlabeled data, moreover, the model must learn and predict new examples. The basic process of semi-supervised learning involves using the labeled data to mark the unlabeled data, to significantly help to increase the training data. This paper aims to design a Vision transformer (ViT) semi-supervised learning model to classify facial expressions based on FER2013 and achieved an 89.43% high accuracy.
Deep Learning-Based Joint Detector for Cooperative Communication

In this paper, we propose a deep learning-based joint detector (DL-JD) for cooperative communication. This detector takes advantage of the nonlinear processing ability of deep neural networks. In the detector, the combination of the two branch signals from the source node and the relay node and the detection of each branch are jointly implemented based on the proposed deep learning algorithm. The simulation results demonstrate that the DL-JD can achieve superior performance than the existing approaches.
Attention-CNN-Aided Specific Emitter Identification Method with Limited Radio Frequency Dataset

Specific emitter identification (SEI) plays a more and more important role to recognize unknown radio frequency (RF) devices in the field of physical layer security. Existing deep learning (DL)-based SEI methods have been proposed for solving problems that enough RF dataset can be obtained at the model training stage. In many real application scenarios, however, only the limited dataset can be collected which causes the conventional DL methods unstable. To solve this problem, in this paper, we propose a robust SEI method using a self-attention mechanism and convolutional neural network (attention-CNN). To be specific, we collect a real RF dataset from the automatic dependent surveillance-broadcast (ADS-B) signal. Here, the ADS-B dataset is limited in some special cases and data augmentation is used to improve the features of the dataset. Simulation results show that the proposed method achieves better identification performance than the benchmark.
The Development of VR Technology and Computer Technology Integration Application

With the progress and development of science and technology, virtual reality(VR) technology has been widely used in military, medical, aerospace, architectural design, entertainment and other fields. VR technology is a comprehensive computer graphics technology, which is composed of multimedia technology, human It is a technology developed by a variety of science and technology such as computer interaction technology, network technology, stereoscopic display technology and simulation technology. This paper starts with the product characteristics of VR, studies and summarizes the characteristics that should be possessed at all levels in the integration of VR technology and computer, including composition characteristics, experience characteristics, technical characteristics and application characteristics, etc. The technical characteristics of VR products, and the research on VR interactive input methods. This paper systematically summarizes the interactive input characteristics of VR technology and computer technology, and then combines the traditional user interface design theories and compares the differences and commonalities between VR interfaces and traditional interfaces, and proposes information display strategies with VR characteristics. Interface presentation strategy and visual design strategy. The final results of the study show that, comparing the scores of the experimental group and the control group, the scores of the experimental group are higher than those of the control group. Therefore, VR technology can help to improve teaching results in classroom teaching.
Multi-scale Generative Adversarial Network for Speech Bandwidth Extension

The purpose of speech bandwidth extension is to recover the high frequency information missing in narrowband speech signal. In recent years, with the introduction of neural networks, the research of speech bandwidth extension has been greatly developed. In this paper, we propose a new speech bandwidth extension method, which increases the sampling rate of speech signal from 8kHz to 16kHz and 48kHz respectively. The model architecture is based on a multi-scale generative adversarial network (MSGan) with multiple discriminators in both time and frequency domains, and is trained using time domain loss, frequency domain loss, adversarial loss and feature matching loss of the discriminators. The multi-scale waveform discriminators are able to improve the naturalness and clarity of speech. In addition, the combination of time domain and frequency domain can stabilize the training and balance the weights of different perceptual quality factors, thereby improving the perceptual quality of wideband speech. We compare our method with several baseline methods. The results show that our method outperforms other baseline methods on Log-Spectral Distance (LSD) and High-Frequency Log-Spectral Distance (H-FLSD), achieving a good balance between time-domain metrics and frequency-domain metrics.
Deciding Complete Integer Lattices via Localization

Transition to quantum-safe cryptogrpahy is being carried on to resist menace of the developing powerful quantum computers, and lattice-based cryptosystems are among the most primising ciphers in the quantum era to defend confidential communication and also to assure information security. Complete integer lattices are the set of all points with integer coefficients in a linear subspace, and have played a significant rôle in cryptanal-ysis of lattice-based ciphers. This correspondence concentrates on deciding completeness of integer lattices via the local-global principle. Given an integer matrix A consisting of basis vectors of an integer lattice L, two equivalent characterizations of completeness are given, one using ranks of A over residue class fields and the other using decomposition of A over the p-adic ring of integers. From the new characterizations we obtain a new constructive proof of the fact that a complete integer lattice has the same determinant as its orthogonal lattice.
HKSR: A Research of Code-Switching Hong Kong Cantonese Speech Recognition Based on Multi-task Rescoring Strategy

Hong Kong Cantonese blends Cantonese and English due to its specific historical background and regional circumstance. This paper studies code-switching in Hong Kong-style Cantonese and proposes a Hong Kong Cantonese Speech Recognition (HKSR) method based on a multi-task rescoring strategy. Firstly, this work innovatively develops the Cantonese-English difference modelling unit to narrow modeling discrepancies between Cantonese and English, and simultaneously alleviate insufficient vocabulary issue caused by the lack of English in the data. Secondly, to better distinguish Cantonese from English, we construct a language identification(LID) subtask. Finally, to jointly train the LID and Automatic Speech Recognition(ASR), this paper develops a multi-task bilingual rescoring module based on U2 end-to-end model. We also investigate the impact of five different rescoring strategies, including multi-task bilingual rescoring, on Hong Kong Cantonese speech recognition. The experimental results demonstrate that HKSR combined with the multi-task bilingual rescoring strategy improves accuracy by <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10%-49%</inf>
Research on End-to-End Wireless Communication System Based on Two Dimensional Convolution

Aiming at the problem that the existing end-to-end automatic codec wireless communication systems do not perform well in multipath channel, this article proposed an improved auto-codec end-to-end wireless communication system. In this article, by changing the size of the convolution kernel, two dimensional(2-D) convolution is used to combat the frequency selective fading caused by the multipath effect, and the traditional OFDM module is combined with the auto-codec, so as to improve the ability of the system to cope with multipath. Simulation results show that the performance of the proposed 2-D convolution based self-codec end-to-end wireless communication system is improved by 17% and 60%, respectively, compared with the one dimensional(1-D) convolution self-codec wireless communication system and the traditional OFDM communication system in 5-path Rayleigh channel and 64QAM modulation.
Spectrum Analysis for Modulation Classification in Aeronautical Wireless Communication Systems

The degeneration of signal time-frequency characteristics has impeded the performance of correlation algorithms. To address this issue, a generative adversarial network (GAN)-based recognition framework is proposed for aeronautical wireless communication systems. It consists of GAN and different dimensional of recognition networks. Firstly, we transform the sampling signals into time-frequency (TF) maps using a short-time Fourier transform (STFT), which shows an apparent signal frequency variation with time. And then, we design an improved GAN, transferring the TF maps affected by the multipath effect into pure maps, to weaken the interference of channels. Next, we put forward the two-dimensional (2D) recognition networks to extract signal time-frequency characteristics, and a deep long short-term memory (LSTM) network was introduced to obtain the time correlation from the TF maps. The experimental results show that the performance of the proposed GAN-based recognition framework is superior to that of conventional algorithms, especially performing in aeronautical multipath wireless channels. When the channel parameters change rapidly, the recognition rate of the proposed algorithm is more than 95.0%.
3D Human Movement Pose Generation Based on Pre-trained AI Model with Skeleton Constraint Analysis and Intelligent Prediction

This paper research will focus on the application and implementation of coordinate data transformation from 2D to 3D, in terms of the human skeleton recognition area. Then the processed 3D skeleton nodes data will be applied to control the 3D human object with auto-modeling and auto-generation, with the programming of Python. The research of this paper uses the pre-trained model of Pytorch to gain the basic 2D detection points data of the human skeleton nodes, to transform the key node 2D skeleton data to 3D skeleton data by the structural analysis of human skeleton nodes. Subsequently, processing the 3D data with the optimization algorithms by the specific skeleton-moving range constraint analysis and prediction. The research provides an available method that generates the 3D skeleton data from the 2D data with an intelligent and efficient method. The generated skeleton 3D data can be used in various 3D modeling applications, especially in the area of the human objects movements and actions design in 3D animation. The experiment result analysis and conclusion will be presented.
Traffic Sign Recognition Based on Multi-branch Residual Mixture Model

In order to improve the accuracy of traffic sign recognition in real environment, a multi-branch residual mixture model based on LeNet-5 is proposed. Firstly, erosion and dilation in morphology are used to preprocess images. Secondly, two relatively independent convolutional branches are constructed to extract the multi-scale feature information of the images. Based on the idea of residual connection, bottleneck blocks are constructed to increase the depth of the network to improve the fitting ability of the model. In addition, Squeeze-and-Excitation block is added to the improved algorithm to reinforce the useful features, and Dropout is added to alleviate the overfitting. Finally, the hyperparameters are selected by using the five-fold cross-validation, and the exponential decay learning rate is used instead of the original fixed learning rate, so that the model can converge better. The accuracy of the improved algorithm on GTSRB and BelgiumTSC is better than that of the classical LeNet-5 model.
Power Domain Named Entity Recognition Based on Rules and Dictionaries

There are massive electricity data in the daily management, normalization operation, troubleshooting and other aspects of the power domain, but these professional and accurate data have not been fully mined and used. Constructing a power domain knowledge map can not only help power grid companies tap the value of these massive data and realize the integration of power knowledge, but also greatly facilitate the staff's query and acquisition of power information, and improve the work efficiency of the power industry. NER (name entity recognition) is the basis for constructing knowledge graph. This paper studies name entity recognition based on dictionaries and rules. It can standardize and accurately extract electricity from unstructured text through three methods: power entity dictionary, feature character rule matching, and part-of-speech combination rule matching. Related entities provide high-quality and high-precision entities for the construction of power domain knowledge graph.
Vehicle tracking from Bird-Eye view

Perception algorithms in 3D space are key research contents in the fields of autonomous vehicles and robots. 3D perception in a bird's-eye view (BEV) has attracted a lot of attention in recent years, because the BEV's representation of traffic scenes contains abundant target depth information and dimensional information, and the detection results can be directly applied to downstream tasks such as trajectory tracking, path planning, etc. Motivated by this observation, We propose a tracking module based on bird's-eye view and a target correlation method optimized for tracking tasks. The method we propose has advantages over other algorithms in performance on the Nuscenes dataset and the KITTI dataset. We further demonstrate that by projecting 3D objects under the bird's-eye view, we can achieve significant improvements in monocular 3D perception tasks, including detection and tracking.
Accurate and Fast Mask Recognition Based on Multiple Color Areas Detection and Face Landmarks Locating

Using deep learning technologies to assist mask recognition in personal healthy activities detection is recom-mended. However, there are some disadvantages in directly using deep learning to make the mask recognition, such as the low recognition rate in detecting the blurry human faces far away or detecting the masks from the human side faces. For the fast moving human faces, the mask detection also has the challenge. In this paper, a novel idea about using the color analysis from the special area of the human face to detect the mask wearing, which combines the face landmarks detection, location and color detection. Using the special color analysis to detect the mask wearing will save more computed resources and get fast performance. What more, for the human face far away, this method still remains a good performance compared with the pure learning mask detection. A novel multiple areas detection and comparison method that performed well is proposed in this paper. The related code is open source in GitHub.
Multistage 3D Hand Pose Estimation Algorithm Based on Skeleton Points

Multi-stage 3D hand pose estimation based on RGB images is a popular research direction in the field of hand pose estimation due to its high accuracy and wide application scenarios. However, there are certain problems and difficulties in this task. Accordingly, we design a multi-stage 3D hand pose estimation network based on skeleton points. Firstly, the joint points that cannot be detected due to occlusion in the 2D hand pose estimation stage are corrected, and a time-series dilated convolutional network is constructed to return the 2D hand pose coordinates to 3D in order to solve the mapping ambiguity problem. Meanwhile, a new loss function is proposed to constrain the generated 3D hand pose coordinates. We validate the network on public datasets, demonstrating the effectiveness and state-of-the-art of our work.