Reviewerly/exHarmony huggingface dataset:
Chinese overlapping event extraction method based on Bert - Text CNN fusion

Event extraction aims to extract event element information from natural language text, and most of the current research methods do not consider the complex overlapping event extraction problem. Therefore, the research focuses on the problem of Chinese event overlap: first, the same event in a sentence may contain multiple different target types; second, in event element recognition, due to the complexity of the semantic environment, Chinese entity recognition is difficult. In order to solve the above problems, an improved joint learning model is proposed. The text sentence vector is constructed by the Bert pretraining model, and the TextCNN model is used to mine the semantic features of the text. Then, the two models are combined to solve the multi-target type event detection problem. A combination of multi-head self-attention and Bi-directional Long Short-Term Memory is used to solve the problem of event element recognition in complex semantic environments. Experiments are carried out on the Chinese financial dataset FewFC, and the results show that the adopted method achieves good results on the task of overlapping event extraction with complex semantic environments.
Transfer learning-based Traffic Identification for UAV-Assisted IoT

Traffic datasets are costly and difficult to attain in the UAV-assisted IoT environment, and the time-sensitivity of traffic distribution is high, which makes it difficult for traditional machine learning traffic identification methods to be applied in practice. To address this challenge, we propose a transfer learning-based approach for UAV-assisted IoT traffic identification: TLB-CNN (Transfer Learning Based Convolutional Neural Network). Firstly, the initial model of the convolutional neural network is pretrained based on the source domain-complete IoT dataset, and then the retraining of the convolutional neural network is realized through the layer frozen based fine-tuning learning algorithm on the incomplete dataset in the target domain. The experimental results indicate that our method can effectively ensure the accuracy of traffic recognition under the conditions of limited traffic training samples. Compared with existing few-shot learning methods, the classification performance is significantly improved
Deep Learning-based Chinese Speech Recognition in Food Safety Field

In order to solve the problems of poor robustness of speech recognition and inaccurate recognition of proper nouns in the field of food safety, this paper creates a Chinese speech recognition model, constructs an acoustic model using Deep Full Sequence Convolutional Neural Network (DFCNN) and Connectionist Temporal Classification (CTC), constructs an acoustic model of A language model is constructed based on statistics, and a terminology database is constructed based on the collation of scientific knowledge in the field of food safety. The speech recognition method achieved a correct rate of89.8% on the optimal model and was able to effectively recognize proper nouns in the field of food safety.
Traffic signs recognition model over data augmentation based on Yolov5

Due to the long time consuming of data collection, it is difficult to label small targets of data samples, the amount of data is small, and the sample distribution is uneven. At the same time, the proportion of small targets is small, the missed detection rate is high, and the model feature fusion is insufficient. In order to pay more attention to the detection target and improve the feature extraction ability of the algorithm. In this regard, this paper proposes a method to generate a new sample dataset by expanding the existing dataset samples, and integrates the Convolutional Block Attention Module (CBAM) into the backbone feature extraction network. The scale feature fusion module, combined with the yolov5 target detection model, achieves the purpose of improving the detection rate of individual identification and enhancing the generalization ability of the target detection model. This data augmentation method enriches the traffic sign dataset and improves the robustness of the model, making it more suitable for practical scenarios. Taking the TTIOOK dataset as an example, its experimental results demonstrate the effectiveness and superiority of the proposed method compared with the unimproved method.
A Deep Fusion Network for Violence Recognition

With the rise of smart city construction, the importance of violence recognition based on surveillance video is increasingly prominent. For the violence in a surveillance video, it is challenging to define specific violent behavior, and the number of participants engaging in violence is unknown while the involvement of each participant is different. Undoubtedly, these barriers are unfavorable for the consistency of the video frame and video clip labels. In this paper, we propose a new framework of violence recognition: a frame selection strategy based on local differential brightness is designed for the accurate selection of violence frames; meanwhile, a deep fusion network P-VFN is designed, targeting to avoid the mismatch between frames and video labels; finally, various motion image detection algorithms are compared to explore the substitutability of the optical flow method, which aims to better the unsatisfactory real-time performance of current optical flow calculation. Experimental results on three challenging benchmark datasets demonstrate that the proposed approach outperforms many state- of-the-art violence recognition models. Furthermore, to compensate for the lack of the current public dataset in the real surveillance scene, we use real surveillance cameras to capture and produce a largescale real violence dataset, which also attributes to a better performance.
A Mongolian-Chinese neural machine translation model based on Transformer’s two-branch gating structure

To design a new two-branch gating structure for the existence of Transformer structure, by dividing the attention mechanism into two pieces, one part adopts the attention mechanism for global information capture and the other part adopts dynamic convolution for local information capture, and the captured two-branch is fused with features by way of gating mechanism to replace the attention mechanism and feedforward neural network, which makes the model fewer parameters and higher ability of capturing information. The experimental results show that our BLEU4 value is improved by 3. 07 compared to the Transformer structure.
The Polar CenterNet for Radar Signal Identification

The radar signal sorting is important in the reconnaissance phase of electronic battlefield, and its performance affects the subsequent decision for the situational awareness seriously. With the wide adoption of various types of radars, the electromagnetic environment is more complex than ever. In this paper, a dynamic multi-parameter processing technique based on CenterNet is proposed. The proposed method can map the pulse description words (PDW) as dots in PDW polar diagram, then the radar pulse can be sorted by identifying the PDW dots using the improved CenterNet. The detected pulse signals are identified to prove the proposed method, and 97.74% accuracy can be achieved. Moreover, the proposed method provides a PDW visualization approach for the complex application scenarios.
A novel multi-UAV patrol path planning method based on evolutionary multi-objective optimization

This paper investigates collaborate path planning of multiple Unmanned Aerial Vehicles (UAVs) for area patrolling. The problem includes task assignment and path planning for each UAV in the team. In the scenario of urgent observation, two objectives are considered simultaneously, i.e., time cost and energy cost. The problem is formulated as a multiple traveling salesman problem (MTSP) with multiple depots, which is a nondeterministic polynomial-hard problem. A decompositionbased multi-objective algorithm with 2-opt local search strategy is proposed to solve the problem. Through computational experiments, the benefit of the novel algorithm (MOEA/D-2opt) is demonstrated by comparing with other existing algorithms. Experimental results show that proposed algorithm is effective and efficient in solving the multi-UAV patrol path planning problem.
Multi-class Target Detection for Optical Remote Sensing Images Based on Improved RetinaNet

In recent years, multi-class target detection in remote sensing images has been widely studied, which is of great importance in both the military and the civil fields. The phenomenon of small targets densely parked (STDP) often exists in such images, people often use oriented bounding box (OBB) method to detect such targets. But the regression of the OBB is difficult, resulting in a decrease in network performance. Therefore, to solve this problem, a cascaded regression module (CRM) is proposed to increase the precision of OBB regression. This paper conducts experiments on DOTA remote sensing data set. Experimental results indicate that the proposed structure can effectively improve the accuracy of multi-class target detection in remote sensing images.
Fraud Web URL Detection Based on Bi-LSTM

Aiming at the problem of accurate identification of fraudulent web pages, a Bi-directional Long Short-Term Memory (Bi-LSTM) recognition model based on URL sequence mixed encoding of characters and words is proposed. According to the arrangement rules of URL sequences, special characters are used as segmentation points to be divided into different words, and then the URL sequences are encoded in two ways based on words and characters. The extraction results of the two encoding methods are added together. The extracted features are detected and classified using multiple classifiers such as Convolutional Neural Networks, Recurrent Neural Networks, and Bidirectional Long Short-Term Memory Networks. The experimental results show that the model using Bi-LSTM as the classifier can obtain the long-distance dependent features of URLs, and can achieve better classification results, and its accuracy can reach 99.63%.
Edge Intelligence with Long-distance Micro Objection Detection and Low-light Image Enhancement for Oil Gas Pipeline Monitoring

The smooth operation of the oil and gas pipeline network plays a vital role in ensuring the safety and stability of the national energy supply. The implementation of pipeline protection is a necessary measure to ensure the smooth operation of the pipeline network, and video surveillance is an important technical means of pipeline protection. Existing video surveillance technology has the following problems in longdistance micro-targets, low-light images and intelligent identification in harsh environments. To tackle these problems, in this paper, we propose a set of optimization techniques to improve the situation. First, we propose a new objection detection method that can handle long-distance micro-targets. Here, the object detection refer to the detection of moving targets such as vehicles, people, animals. Second, we propose a transfer learning based photo-enhance technique under low light. We have implemented our techniques on RK3399 platform and extensively verify the performance.
Design and Implementation of a Radio Frequency Fingerprint Identification System

Aiming at the actual application scenario of radio frequency (RF) fingerprint identification, a low-cost, small-volume embedded system for specific identification of RF devices is designed and implemented, and the improved VGG-16 model is used to extract the RF fingerprint features of RF signals. The hardware of the system consists of two parts: the RF Signal Acquisition Module and the RF Fingerprint Identification Module. The RF Signal Acquisition Module uses Zedboard as the main control board, and the AD9361 chip is the RF signal receiving circuit; the RF Fingerprint Identification Module uses FPGA to accelerate the deep learning algorithm, and displays the identification results on the host computer. Using 6 wireless routers of the same model for testing, the system has an identification accuracy rate of 96% for wireless routers in the actual electromagnetic environment, and the real-time identification feedback time is about 6 seconds, which has a certain practical application value.
Study on the named entity recognition method implementation for food safety

In order to improve the relevance and efficiency of food safety supervision, a food safety knowledge database and a named entity recognition (NER) model that can extract food safety entities such as poison, disease, and symptom has been constructed. First, the key content of ‘‘Food Safety Accident Determination and Prevention Control’’ was identified, and 8265 valid statements were obtained. It then used data cleaning and sequence marking to obtain the dataset, dividing the dataset into training, validation and test sets in the ratio of 8:1:1. The Chinese food-safety domain NER model was constructed by introducing a bidirectional encoder representation from transformers (BERT) as a vector embedding model based on a bi-directional long short-term memory (BiLSTM) combined with a conditional random field (CRF). The BERT-BiLSTM-CRF model achieved 96.96% accuracy, 83.87% precision, 87.14% recall and an F1 value of 85.48, with low model error and high accuracy, and successfully extracted eight types of entities: poison, disease, host, category, source, symptom, disinfection method and drug. The NER model for food safety based on the BERT-BiLSTM-CRF has high accuracy and can establish an exclusive knowledge base for food safety, which helps improve food safety supervision efficiency.
An Aggregation Method based on Cosine Distance Filtering

Federated learning provides privacy protection for source data by exchanging model parameters or gradients. However, it still faces the problem of privacy disclosure. For example, membership inference attack aims to identify whether target data sample is used to train machine learning models in federated learning. Active membership inference attack takes advantage of the feature that attackers can participate in model training in federated learning, actively influence the model update to extract more information about the training set, which greatly increases the risk of model privacy disclosure. Aiming at the problem that the existing secure aggregation methods of federated learning cannot resist the active membership inference attack, DeMiaAgg, an aggregation method based on cosine distance filtering, is proposed. The cosine distance is used to quantify the deviation degree between clients’ gradient vector and global model parameter vector, and the malicious gradient vector is excluded from gradients aggregation to defense against the active membership inference attack. Experiments on the Texas 100 and Location30 datasets show that DeMiaAgg method is superior to the current advanced differential privacy and secure aggregation methods, and can reduce the accuracy of active membership inference attack to the level of passive attacks.
Research on the realization of multilingual speech synthesis and cross-lingual sound cloning in Tibetan

Speech synthesis technology has achieved rapid development in recent years, and the speech synthesized has reached a very high level of intelligibility and naturalness. However, once the speech to synthesize is mixed with words from other languages, the quality of the speech will be greatly compromised. Imagine how great it would be if one can hear foreign place names pronounced in the corresponding language very smoothly when navigating. Given that most of us can only speak one or two foreign languages due to time constraints, it would make a big difference to speak a foreign language in your voice. Implementing it using the existing monolingual model has difficulty in collecting sound data from someone who speaks different languages at the same time. Using only monolingual corpora, our model can do a good job of cloning one person’s voice and realizing code-switching. The parameters of the encoder are generated by a separate network based on a specific language vector, the parameter generator module consists of several specific parameter generators, each of which takes a language vector as input to generate the parameters of a layer of an encoder in a given language and to complete the sound cloning, we use an adversarial speaker classifier to eliminate specific speaker information in model training and the information will be going back in the synthesis. Our model performs very well on code-switching task and can synthesize high-quality speech with high accuracy.
Graph Attention Network Fraud Detection Based On Feature Aggregation

With the rapid development of digitalization in the financial industry, fraud detection has become an important task to ensure safe development. In traditional fraud detection tasks, training and prediction are often only based on the dimensional characteristics of a single sample, but with the development of digitization and fraud methods, these methods are often no longer applicable. In addition, there are rich information associations between users themselves, which makes a large social network graph between users. In this regard, based on the Dgraph-Fin dataset, this paper uses the relationship network formed between users to better learn the weights between different edges through feature aggregation and attention mechanism. The experimental results show that compared with the existing baselines Accuracy and validity have been improved.
High-altitude parabolic detection method based on GMM model and SORT algorithm

With the accelerated urbanization process in China, the resident population is flocking to high-rise buildings. The number of high-altitude parabolic has increased significantly, resulting in many casualties and property damage accidents. To this end, a proposed technique for high-altitude parabolic detection based on Gaussian Mixture Model (GMM) and Simple Online and Realtime Tracking (SORT) is proposed. Firstly, the GMM-based background modelling method is used to separate the background and foreground from the image sequence to obtain the motion image of the dynamic target at the current moment. The image is subjected to mathematical morphological denoising to determine whether the dynamic target is a throwing object by SORT. Experimental results show that the proposed method can accurately detect the dynamic target of overhead throwing objects with good stability and can effectively reduce the false detection rate.
Object detection algorithm based on fusion of spatial information

Aiming at the problem that the small target size is small and the feature extraction is difficult, which leads to the poor detection effect of small target, this paper proposes a target detection algorithm based on the fusion of spatial information. Firstly, this paper takes the single-stage target detection algorithm yolov5 as the basic model, and adds a spatial information detection network module to solve the problem that the features of small targets are gradually reduced or completely lost in down-sample, so as to retain more spatial information in the low-level network. Secondly, in the feature fusion part, multiscale feature fusion module is used to fuse high-level semantic information and low-level spatial information, so as to locate small targets more accurately. Finally, the fusion features are used for the detection task to improve the accuracy of small target detection. The experimental results show that the Mean Average Precision (mAP) value detected on the COCO test set reaches 42%, which proves the effectiveness of the algorithm for small target detection.
PAR<sup>2</sup>Net: End-to-end Panoramic Image Reflection Removal

In this article, we investigate the problem of panoramic image reflection removal to relieve the content ambiguity between the reflection layer and the transmission scene. Although a partial view of the reflection scene is attainable in the panoramic image and provides additional information for reflection removal, it is not trivial to directly apply this for getting rid of undesired reflections due to its misalignment with the reflection-contaminated image. We propose an end-to-end framework to tackle this problem. By resolving misalignment issues with adaptive modules, the high-fidelity recovery of reflection layer and transmission scenes is accomplished. We further propose a new data generation approach that considers the physics-based formation model of mixture images and the in-camera dynamic range clipping to diminish the domain gap between synthetic and real data. Experimental results demonstrate the effectiveness of the proposed method and its applicability for mobile devices and industrial applications.
Securing Deep Learning as a Service Against Adaptive High Frequency Attacks With MMCAT

Most cloud providers offer Deep Learning as a Service (DLaaS) for different business, science and engineering domains. However, it is known that deep neural networks (DNNs) are vulnerable to adversarial examples, which can cause well-trained DNN models to misbehave by injecting human-imperceptible perturbations to the query input data. Securing deep learning as a service becomes a critical challenge in mitigating such adversarial input perturbations, and enhancing the robustness of DNNs. In this article, we report two important facts: First, most adversarial perturbations are high frequency signals or are added to high frequency signals. Second, due to Frequency Principle that neural networks overly pay attention to fit the low frequency signals during training, the models could be easily misled by the high frequency signals of adversarial examples. These facts consequently contribute to the vulnerability of DNNs service in the Cloud. We conjecture that the more robust the neural networks are in learning from high frequency signals, the more resilient these neural networks are against adversarial perturbed examples. We propose a novel method for generating high-frequency-enhanced adversarial examples, which is achieved by a high-pass filter in the frequency domain via Fourier Transform. This method enhances the learning ability for high frequency signals and ameliorates to over-fit useless low frequency signals. In order to improve the robustness of DNNs service under such signal frequency attacks, we propose a multi-modal collaborative adversarial training framework, named as MMCAT, which uses the multi-modal information of the input images for cross-modal collaborative training, delivering excellent extension for effectively learning of multi-modal image information. Extensive experiments show that under strong adaptive frequency attacks, the DNNs service trained with the proposed MMCAT method achieve superior performance and robustness over the state-of-the-art adversarial training approaches.
Third-Person View Attention Prediction in Natural Scenarios with Weak Information Dependency and Human-Scene Interaction Mechanism

First-person view attention has been widely studied in computer science domain since 1990s while third-person view attention in natural scenarios begins to gain the intensive interest until 2015. This paper focuses on the problem of third-person view attention prediction in natural scenarios where a human freely performs daily activities without constraints. To handle the two insuffiencies of existing methods: (i) assuming some extra information (except for input images) are given in advance and (ii) ignoring the importance of human-scene interaction, this paper proposes a model with weak information dependency, which helps to alleviate annotation costs. In addition, a transformer-based human-scene interaction mechanism is proposed to explore the global and long-dependency contexts between the human and scene. The pipeline of the proposed model is firstly extracting human and scene features, then inferring human attention probability map by fusing human and scene features via a transformer-based network, and finally predicting human attention object based on human attention probability map and object detection. The experiments on two public datasets validate the effectiveness of our model.