Reviewerly/exHarmony huggingface dataset:
Localized Differential Privacy-based Data Privacy Protection Scheme for Home Smart Meters

The data collected by the smart meter involves the user’s privacy, and there is a significant security risk in sending it to the cloud for statistical analysis. Therefore, this article proposes a localized differential privacy-based privacy protection scheme for home smart meters(LDP-HSM), which firstly discretizes the data by probability, then aggregates and analyzes the data locally after perturbation based on a random response technique and finally transmits the aggregated results homomorphically encrypted to the control center for further. Finally, analyze and process the data after homomorphic encryption on the server. It is proved by experiment that the scheme in this paper performs well in the availability of aggregation results and communication overhead.
A Fast Hash Image Retrieval Method Based on Dual Learning

Aiming at the problems of low efficiency of feature extraction and insufficient use of feature correlation in image retrieval based on deep hashing, an image retrieval method Dual Learning Hashing(DLH) that combines feature layer learning and hash layer learning is proposed. In view of the problem that existing methods usually extract local features with intensive attention mechanism by focusing on dense local regions, these local regions cannot contain different local information, a module is developed to learn differentiated local features by locating the peaks of non-overlapping subdomains in the feature map. In order to make full use of semantic information and generate high quality hash code, quantization function and probabilistic semantic-preserving function are designed in the hash layer. Finally, combined with the two parts of learning, the total loss function is given. A large number of comparative experiments have been carried out on three widely used datasets. The experimental results show that compared with other advanced deep hash methods, DLH has achieved better retrieval performance.
Adaptive Random Forest with Dynamic Detectors for Evolving Data Stream Classification

Evolving data streams containing concept drift breaks the assumption that data are independent and identically distributed (IID) in traditional machine learning models. A series of ensemble-based models have been adapted to data stream learning tasks due to the abilities of dynamic partially update and cooperate with drift detection methods. Adaptive Random Forests (ARF), one of the state-of-the-art in the stream setting, encounters high resource requirements in both efficiency and memory like all ensemble methods. We are committed to reducing running cost without compromising ARF’s performance. In this paper, we propose the Adaptive Random Forest with Dynamic Detectors (ARFDD) which utilizes diversity measurement to combine homogeneous detectors and split heterogeneous ones accordingly, leads to lower computing costs and thus speed up in applications. Experiments on all 8 synthetic and 4 real datasets show a great CPU-time thrift while avoiding performance degradation.
SE-CNN Attention Structure for Quantitative EEG-Based Assessment of VR Motion Sickness

The applications of virtual reality (VR) technology are currently numerous and promising, but motion sickness (MS) problems are affecting the development of the VR market. Questionnaires are commonly used to subjectively assess motion sickness, but they are applied before and after the user experiences VR and cannot assess the user's motion sickness in real time. In this work, this paper proposes a convolutional neural network (CNN) structure incorporating squeeze and stimulus (SE) attention mechanisms, with subjective questionnaire scores used as markers of electroencephalographic signal (EEG) for real-time prediction of VR motion sickness scores. In this thesis, EEG signals were collected from 16 subjects in a virtual reality environment and the experimental data were fed into the network structure to train the model. The experimental results showed that the root mean square error (RMSE) was 25.69, the Pearson linear correlation coefficient (PLCC) was greater than 0.85, and the Spearman rank correlation coefficient (SROCC) was also greater than 0.85, indicating a significant relationship between the prediction and subjective assessment scores (p<0.05), proving proposed method's effectiveness.
ECenterDet: An Efficient Anchor-free Detector

Current networks rely on custom operators to achieve high performance, but these new operators are difficult to obtain hardware acceleration quickly. Anchor-based object detection algorithms rely on a large number of anchors, and the post-processing of these anchors also limits the speed and performance of the detection task. In this work, we propose an efficient deployment-friendly detector, and name it as ECenterDet. Our proposed model uses an anchor-free paradigm and treats the detection problem as a key point estimation problem, the backbone comes equipped with a efficient cross stage partial network(ECSPNet), an adaptive feature pyramid (AFP) with both high speed and accuracy. We try to improve the training efficiency by using all pixel points in the whole Gaussian region as training samples, and further optimize the loss calculation in order to balance the number of objects of different sizes, which can significantly improve the performance of the model without bringing additional computation and parameters. In addition, we develop the s/m/l/x model for different application scenarios. The proposed ECenterDet shows very competitive performance compared with existing methods, and our ECenterDet-x achieves 51.6 mAP on the MS COCO dataset, outperforming YOLOv4-Tiny and YOLOv5s with faster inference speed.
Resolving Gendered Ambiguous Pronouns with Gender-Fair Modeling Based on BERT Word Embeddings

Ambiguous anaphora resolution is a longstanding challenge in natural language understanding. This field possesses tremendous potential to improve the performance of other natural language processing (NLP) tasks like machine translation, sentiment analysis, paraphrase detection, summarization, etc. Previous studies have shown that current neural network based anaphora resolution systems all have the problems of gender bias, as these methods tend to behave better in male pronouns resolution than female. In this paper, we use the pre-trained BERT model combined with the natural language inference (NLI) architecture to transform the anaphora resolution task into a question answer (QA) extraction task, and employ a new data augmentation method to effectively overcome the gender bias problem. Without introducing any external data, we can achieve a single-model score (log loss) 0.20457 on the Gendered Ambiguous Pronouns (GAP) dataset released by the Google AI team through a Kaggle competition.
Graph Attention Based Variational Adversarial Graph Generation Method

With the wide application of graph structure data in various fields of daily life, the research on graph data based on deep learning has also received extensive attention. The adversarial learning method is a common generation method, in which the one-shot generation method mostly uses Generative Adversarial Networks (GANs) as the basis. However, it needs to predefine the maximum number of nodes and the node generation order, which has great limitations. Based on this, this paper proposes an adversarial conditional graph generation model combined with variational autoencoders——Graph-VAEGAN, which can solve the above problems. Additionally, combined with graph attention mechanism, the generated graph can more fully learn the vertex information and apply it to the structure generation. Experiments show that the model can improve the limitations of the graph generation model and have better performance in the graph generation task.
General Elastic Computing Acceleration Method for Semantic Segmentation

In the field of computer vision, deep convolution neural network plays an increasingly important role, and its accuracy in various tasks has gradually exceeded the traditional method. However, real-time is important in autonomous driving and mobile robot scenarios. Most processes need to be run locally and completed in a short time with sufficient precision. Semantic segmentation based on deep convolutional neural networks consumes huge computing resources, and the balance of accuracy and speed has been a difficult problem in industrial deployment. We propose a general elastic computing acceleration method for semantic segmentation, which makes full use of the correlation between the front and back frames of the input video. We use optical flow, fuzzy prediction and the combination of these two methods to accelerate the inference of semantic segmentation. Experiments show that our method greatly saves computing resources and retains high accuracy. On platforms with limited computing resources, it can well balance accuracy and speed.
Sampled Feature Replay and Multilayer Knowledge Distillation for Class-Incremental learning

Deep neural network is subject to catastrophic forgetting when learning new data incrementally. In this work we propose a class incremental learning algorithm based on feature replay and multi-layer spatial distillation to address the problem of data privacy and catastrophic forgetting. Firstly, different from general methods which save and replay images to retain old knowledge, this method saves prototype and generates feature for replay based on a new feature distribution assumption. Secondly, this method makes better use of the middle layer feature maps and performs multilayer spatial distillation, which alleviates catastrophic forgetting and improves the overall effect of class incremental learning. We implement multiple sets of experiments on public datasets and the experimental results show that the algorithm proposed in this paper can effectively improve the effect of class-incremental learning.
Matrix-type Recurrent Neural Network For Fast Solving Complex-variable ℓ1-norm Optimization

This paper presents a matrix-type recurrent neural network (MRNN) for fast solving a class of ℓ1-norm optimization problems with complex variables. Compared with existing matrix-type neural network for real-variable ℓ1-norm optimization, MRNN has the lower computational complexity and can deal with more general cases. Compared with existing vector-type neural network for complex-variable ℓ1-norm optimization, MRNN also possesses the lower complexity and smaller algorithm storage. Theoretically, MRNN is proved to be stable in the sense of Lyapunov and global convergent to the optimal solution with arbitrary initial points. Simulation results confirm the obtained results and the effectiveness of the proposed MRNN.
MGPose: A Graph Data Augmentation Framework for 3D Human Pose Estimation via Masking

Currently, the scarcity of training data is an important factor restricting the performance of 3D human pose estimators. To make better use of existing data, we propose a graph data augmentation framework, MGPose. We regard the existing pose estimator as an autoencoder architecture, masking the human joints randomly in the encoder and reconstructing them in the decoder. Through random masking, our encoder can only get the features of the joints from the visible subset. The visible joints obtained each time are different, which helps to improve the feature extraction ability of the encoder. However, there is an offset between the masked data and the original data after passing through the same encoder. In order to narrow this gap, we reconstruct the high-dimensional features of the joints in the decoder and introduce the idea of equivariance to constrain the generation of the joint data. The parameter scale of MGPose is comparable to the original model, which is very simple and efficient. We conduct experiments on the most popular dataset Human3.6M and achieve state-of-the-art results.
Cross-contrast Fusion and Aggregation Network for Multi-contrast MRI Super-resolution

Magnetic resonance imaging (MRI) super-resolution (SR) can restore high-quality high-resolution (HR) images from low-resolution (LR) ones, which is helpful for clinical diagnosis and treatment. Different from SR restoration using a single-contrast, multi-contrast SR restoration has received increasing attention because different modalities can provide complementary information to improve the SR quality. However, fusing multi-contrast features as well as capturing cross-level correlations among different layers are still challenging. In this paper, we propose a Cross-contrast Fusion and Aggregation Network (CFA-Net) for multi-contrast MRI super-resolution, which can effectively explore complementary information from multi-contrast images to improve target-image SR quality. Specifically, the multi-contrast images are passed through multiple residual Swin Transformer blocks (RSTB) to learn hierarchical feature representations at different layers. Then, we present a Cross-contrast Fusion Module (CFM) to fuse the cross-contrast features in a layer-wise strategy, which can capture the complementary information from multi-contrast MR images. Moreover, a cross-level Feature Aggregation Module (FAM) is proposed to integrate cross-level features from CFMs for exploring the interaction between different layers. Experimental results on three multi-contrast MR datasets demonstrate that our method performs better than other state-of-the-art SR methods.
Video Bitstream Recompression Based on Sparse Representation of DCT Coefficients

Further compression of encoded video bitstream is necessary to save storage space and bandwidth. However, video bitstream is a compressed version of raw data with video encoders according to different standards, and hence the signal redundancy is already reduced compared with original video data. Recompression of video stream requires further exploring the correlation remained. Transform coding as a part of hybrid video coding framework adopted in the latest video coding standards such as discrete cosine transform (DCT) decorrelates predictive residual signal for efficient quantization and entropy coding. Nevertheless, considerable amount of statistical correlation still remains in the transform coefficients that further reducing the redundancy can lead to improved coding efficiency. In this work, we propose a video stream recompression scheme based on further sparse representation of DCT coefficients. Dictionary-based sparse representation method is used after DCT coefficients are obtained as a secondary transform module. Moreover, the proposed scheme leverages the property of DPCM and avoids sending bits of dictionary by forming redundant dictionaries from DCT coefficients of previously decoded frames. Experimental results demonstrate that the proposed recompression framework further reduces the bitrate of original H.264 bitstream by more than while maintains similar subjective quality.
MeDict-GP: An Accurate Entity Recognition Model Combining Medical Domain Knowledge and Globalization Ideas

In order to extract medical terms from the vast amount of medical literature, such as diseases, treatment modalities, and drug names, clinical named entity recognition (CNER), a crucial step and foundation in medical natural language processing, aims to increase the effectiveness and caliber of clinical research. Medical named entity identification has given rise to a number of recognition models, however these algorithms struggle to recognize nested connections in medical texts and multilingual annotations in Mandarin and English. For this reason, we provide the sensible and precise MeDict-GP model, which fuses the lexicon with the globalization framework GlobalPointer to predict the labels and output the prediction sequences. It solely employs the medical domain as input to the lexicon embedding model. We discovered that the MeDict-GP model attained an F1-Score value of 68.171 percent in the assessment tests on the CMeEE medical dataset, an improvement of 1.17 percent compared to the manual annotation findings. We discovered through a series of comparative ablation experiments that adding domain lexicons and adversarial training can boost entity recognition accuracy. Our next step will be to investigate models that are more particular and have better capacity to demarcate entity borders because we also discovered that the models frequently failed to recognize certain medical entities.
Research on Remote Intelligent Monitoring System for Online Examination Based on Gaze Direction Classification

Aiming at the problem that online examination is difficult to monitor, a remote intelligent monitoring system for online examination based on gaze direction classification is proposed. In the experimental environment, based on the remote online examination monitoring situation, the dataset with nine types of gaze directions for multi-classification problems is established, and the image classification model is used for training. The same dataset is used for comparative experiments on the ConvNeXt network and the ResNet network, and finally the ConvNeXt network is selected as the backbone network. The ConvNeXt network model trained based on nine types of gaze directions classification dataset performs well in the weighted accuracy and weighted recall rate on the test set, with the weighted accuracy rate reaching 88.82% and the weighted recall rate reaching 86.96%. The experimental results show that the solution of a remote online examination monitoring system based on the ConvNeXt network is feasible. It can not only improve the remote online examination monitoring mode, but also have a low economic cost, which is of great significance to promote the popularization of remote online examination.
A Survey on Deep Learning for Chinese Medical Named Entity Recognition

At present, how to make full use of medical and health data for exploration and analysis to better support clinical decision-making faces many challenges. This paper aims to summarize and analyze the methods and research status of Chinese medical named entity recognition, and understand the research progress of named entity recognition technology in Chinese electronic medical record text. This paper conducts literature research from multiple perspectives, such as the basic concepts of electronic medical records and named entity recognition, the acquisition of corpus datasets and the named entity recognition algorithm. The research progress of Chinese electronic medical record named entity recognition in recent years is reviewed, and the development trend of electronic medical record named entity recognition in the future Chinese is analyzed.
Software Defect Prediction using Multi-scale Structural Information

In recent years, most researches have used the sequence of nodes in the abstract syntax tree (AST) of code to extract features for software defect prediction (SDP). While the AST is a kind of graph data, it may ignore some part of the structural information to use the original graph data as a sequence for input. Thus, Graph neural network (GNN) has been used to extract structural information in SDP. However, existing researches ignore that GNN learning is inherently local. It is difficult to interact between remote nodes and to capture long-term dependencies in source code. We apply a combination model of GNN Transformer to predict the software defects. Using an AST directly as the input, GNN extracts local features and structural information between the node and its neighbors. We then encode the relative and absolute positions of the nodes in the AST. The position encodings are passed into the Transformer along with the feature information extracted by GNN to extract the global features, which are the long-term dependencies between nodes. Finally, the extracted fused features are used in the SDP. Experiments on the PROMISE dataset have shown that our method achieves higher F-measure and better identification of defective features in source code than the state-of-the-art SDP method.
SynDG: Syntax-aware Dialogue Generation

Dialogue system is designed to converse with humans in a natural way. As an essential part of dialogue system, dialogue generation aims to generate proper response given historical context. Recently, sequence-to-sequence (seq2seq) based models have achieved great success but suffer from ungrammatical problems. In this paper, we propose a Syntax-aware Dialogue Generation (SynDG) model that incorporates syntactic information to generate grammatical responses with an encoder-decoder framework. Specifically, we first construct a syntax-graph with a dependency parser on the dialogue corpus. Then, we employ three graph embedding algorithms to learn syntactic word representations as the input of seq2seq framework. Furthermore, we devise training strategies to predict syntactic structure of the sentence for sufficient syntax understanding. Our empirical study on two multi-turn dialogue datasets demonstrates the effectiveness of SynDG in generating natural and grammatical responses.
A Decoupled Language Model Based on Contrastive Attention Mechanism for Scene Text Recognition

In recent years, approaches for scene text recognition based on the attention mechanism have achieved amazing success. However, the majority of attention mechanism approaches are coupled, and the majority of ways adhere to the concept of locating the most pertinent image regions. In this paper, we present a language model with a contrastive attention mechanism that is detached from the standard encoder-decoder architecture. First, preliminary text recognition results are obtained based on the encoder-decoder framework; second, we perform the two steps of text prediction in the language model and the calculation of the attention weight of the text to the image, and we not only find the most relevant image area, but also look for the least relevant image area; and finally, the loss function is used to make the model pay less attention to irrelevant areas and more attention to relevant areas. On seven datasets, we evaluated the performance of our model and found that it performed exceptionally well, particularly on the IC13, SVT, and SVTP datasets.
A Novel Seven-Class Facial Expression Recognition Method With Face Mask

Since COVID-19, face masks have become essential in people’s lives. When people are wearing masks, it is incredibly difficult for traditional face recognition techniques to identify facial expression. This paper proposes a seven-class facial expression recognition dataset FM-FER2013 (Face-Masked FER2013). The FM-FER2013 dataset is based on the FER2013 dataset. I also proposed a facial expression recognition method with face mask, based on YOLOv5 for face detection and ResNet for facial expression recognition. The accuracy are about 60% on the seven-class FM-FER2013 dataset and 73% on the five-class FM-FER2013 dataset. And then, real-world test is performed, with accuracy around 65%. The proposed method’s effectiveness and robustness demonstrate its high value and provide a hint for real-world application. The code implement is shown in https://github.com/RoyMikeJiang/FM-FER2013.