Reviewerly/exHarmony huggingface dataset:
Effect of Preference for a Lecturer Disguised as an Avatar on Desire to View Lecturer’s Video

It is said to be more difficult to concentrate in online lectures than in face-to-face lectures. Working towards an increase in desire to view lecture video, in this research we changed the appearance of a lecturer using virtual avatars (of which there are four kinds: young man & woman, older man & woman) and investigated whether a lecture participant’s viewing motivation was affected by video image of a lecturer made to look like their preferred avatar. In this experiment, in which 154 participants took part, participants were asked to select lecturers (including avatars) according to the participants’ preferences regarding appearance. Then, participants were divided into a group that watched their favorite avatar and a group that watched their least favorite avatar, before viewing the lecture video. In addition to subjective evaluation indices such as degree of comprehensibility and boredom, viewing desire was also investigated through objective indices, such as the result of a short test and a record of the time at which participants felt they wanted to stop watching the lecture. As a result, in every evaluation index, the participants who viewed an avatar for which they had a high degree of preference obtained a more affirmative result than the participants who viewed an avatar for which they had a low degree of preference, implying that viewing a preferred avatar has an influence on increase in viewing desire.
Offensive text style transfer based on unsupervised learning

As the current mainstream social networking method, online social networking brings convenience to people, but also generates negative problems such as language violence. Therefore, offensive text style transfer research has become an arduous task. Research has shown that the semantic loss is caused by the discreteness and timing of the text data, resulting in a low degree of content retention after style transferring, and the lack of relevant parallel corpus data and specific style tag keywords, therefore, the accuracy of text style transfer is limited. Aiming at the existing problems, this paper proposes an Offensive Text Style Transfer based on the Unsupervised Learning (OTST-UL) model. First, the input text is encoded through the bidirectional encoding attention mechanism to retain the core content of the text. Then the generatordiscriminator is applied for adversarial training, and a reconstruction loss algorithm is constructed to ensure the accuracy of the offensive language style transformation and the integrity of the text content. Experimental results show that the OTST-UL model outperforms existing text style transfer models on offensive language datasets.
Traffic sign detection algorithm based on improved YOLOv5

Aiming at the problems of low detection accuracy and large weight files in the traditional traffic sign recognition algorithm, it is not suitable for practical application. A traffic sign recognition method based on the improved YOLOv5 algorithm is proposed. First, improve the loss function of YOLOv5, use the DIOU loss function to optimize the training model, improve the accuracy of the algorithm, and achieve faster target recognition. Combined with the lightweight convolutional neural network MobileNetv2, the lightweight improvement of the YOLOv5 network is achieved.
DM-CATN: Deep Modular Co-Attention Transformer Networks for image captioning

The image features are directly input into the decoding part of the model, which leads to the insufficient use of feature information and makes it difficult for the model to better express the image information. We introduce a Modular Co- Attention Transformer Layer (M-CATL) to efficiently model high-order intra-feature and inter-feature interactions for single and multiple input features to mine the details of image features. And construct a Deep Modular Co-Attention Transformer Block (DM-CATB) according to M-CATL and integrated into the encoder part of the model. Furthermore, we present a Deep Modular Co-Attention Transformer Network (DM-CATN) to fully model the spatial information and position information of image features and improve the ability of features characterization, in order to provide richer image information for decoding part. Experimental results demonstrate that DM-CATN significantly outperforms the previous state-of-the-art. Our best single model delivers 133.2% in CIDEr.
Animal recognition using Siamese network with two kinds of backbone networks

Wildlife recognition is the task of matching and outputting similar probabilities given two images, with twin networks being a popular framework in the field. In this work, we use the Siamese architecture as the main framework. As for the backbone, we compare two popular backbone networks, including VGGNet and ResNet, respectively. We compare those two networks with respect to the convergence speed, robustness and maximum accuracy to exploit the effectiveness of our method. We test those networks on AFHQ dataset. Our method with ResNet achieves 98.86% accuracy. Our experiments have confirmed that, to a certain extent, the larger the number of layers in the network, the higher the stability and accuracy of model training.
Research on synthesis of designated speaker speech based on StarGAN-VC model

With the rapid development of deep learning, the research focus of speech synthesis has gradually shifted to artificial neural network technology. The speech quality has been greatly improved and has been introduced into many application scenarios. However, the existing synthesis systems need to use rich and high-quality parallel data sets when training models, and the synthesized speech is also weak in personalized performance. This paper describes an improved Mel spectrogram acoustic feature sequence prediction model based on Tacotron2 and a StarGAN-VC model. The model uses the predicted Mel spectrogram as input to generate Mel spectrogram sequence of the specified speaker and synthesize speech. StarGAN-VC model can train the model in non-parallel Mini dataset, generate Mel spectrogram sequence of designated speaker in real time and synthesize speech, which can well solve the problem of lack of non-parallel dataset and enrich the speech content generated by StarGAN -VC model. The experimental results show that StarGAN-VC model can generate relatively smooth Mel spectrogram by using the Mel spectrogram sequence predicted by the improved model, and have stronger expressiveness in dealing with the details of Mel spectrogram, so as to synthesize smooth and high intelligible speech. The model uses the speech data of the designated speaker for about 27 minutes to train the model and synthesize personalized speech, which provides an effective reference for the synthesis of personalized speech.
Unsupervised image style transfer based on U-GAT-IT

Thanks to the rapid development of Generative Adversarial Networks (GAN) in recent years, great progress has been made in image translation using GAN. Image-to-image transformation is one of the important applications in the field of computer vision, and its scope includes image inpainting, image colorization, super-resolution and image style transfer. In recent years, there are many classic research on GAN-based image transformation, such as CycleGAN, UNIT, AGGAN, etc. This paper focuses on researching and testing the U-GAT-IT unsupervised image style transfer method. The authors introduced a new attention module and a new learnable normalization function (AdaLIN), which enables flexible control of the amount of change in shape and texture during image conversion. This paper uses a new dataset for testing and verification, trying to achieve bidirectional conversion between real face photos and sketches, and qualitatively and quantitatively analyzes the method by calculating PSNR and SSIM.
Research on cat and dog recognition based on several CNN models

CNN (convolutional neural network) is a classical research method of Deep Learning. It can obtain the characteristics of a picture through the information transmission between convolution layers and pooling layers, and generate some output (such as image classification) after final processing. Since the end of the 20th century, scholars have proposed various convolutional neural networks, which have their own characteristics. In this paper, we choose LeNet, AlexNet, VGG, ResNet and GoogLeNet to complete the cat and dog recognition task on kaggle, so as to identify and explore the performance of different networks in different situations. The results show that these classical algorithms have generally become more and more advanced over time, but this cognition is not completely correct. For example, when the number of samples is limited, the more advanced ResNet did not perform as well as the relatively primitive VGG network. Such characteristics help us choose the right algorithm in different situations and guide us refine the algorithm in the future.
A super-resolution image reconstruction method based on generative adversarial network

Compared with traditional methods, the quality of reconstructed images has been greatly improved by super-resolution image reconstruction methods based on deep residual networks. However, in order to make full use of shallow image features and solve the problem of instability during network training. This paper proposes a method for image super-resolution reconstruction that can better reconstruct texture details based on generative adversarial network (GAN). Dense blocks containing residual scaling (RRDBs) is used to construct the generative network to extract more image features. WGAN is introduced to construct the discriminative network to solve the problem of unstable training of generative adversarial network. The experimental results show that the proposed model in this paper has better visual effects and improved SSIM and PSNR values compared with other models.
A privacy measurement model for privacy-preserving stream histogram publishing

Privacy protection in shared data stream publishing witness its thriving in recent years. Different privacy preserving methods commonly provide different protection effects. Unified measurement of protection intensity is the basis of privacy evaluation. Existing privacy measurement methods depend too much on background knowledge, and the measurement effect is strongly related to privacy information. They in common fall short in privacy measurement in privacy-preserving stream histogram publishing. Concerning these issues, a Bayesian-theorem based privacy measurement model is proposed for privacy-preserving stream histogram publishing. By analyzing the correlation degree between background knowledge and sliding window, the correlation between background knowledge and publishing results is established. The concept of correlation histogram is introduced to analyze the correlation between histograms containing the same user's state information, and a sliding window-oriented privacy leakage measurement mechanism of correlation histograms is proposed to measure the degree of privacy leakage between correlative histograms. Further, corresponding weights are set for both error probability of attacks and privacy leakage extent, to realize privacy measurement of privacy-preserving histogram publishing algorithm. Theoretical analysis and experimental results show that our solution can effectively measure the protection strength of streaming histogram privacy protection method.
An object classification and detection method with faster R-CNN based on multiscale anchor box and weighted cross entropy classification

To improve the accuracy of object classification and recognition, one of the methods is to add recommendation boxes, recommendation boxes are generated by region proposal algorithms. Because of the inherent ratio, the size of the candidate box generated by Faster RCNN's RPN is usually large, which would easily cause a great number of overflows in sliding search. This is unfriendly for multi-objects detection. To improve the precision of multi-objects detection, we introduce a multiscale proposal box that could predict object bounds and object scores at each position. Then, in order to increase the positive sample range of the foreground, the weighted cross entropy classification function is used for binary classification in the RPN network. In the ROI network, the candidate frame reset algorithm is proposed to realize the position regression of the prediction box and multi-classification of objects, which further improves the accuracy of object detection. The experimental result achieves the mAP of 76.2% on the VOC 07 classic dataset, which is 2.7% higher than the Faster R-CNN. On the VOC 12 test, the mAP of 75.6% is improved by 2.5% compared with the Faster R-CNN.
An automatic composition scoring system for oversea Chinese students

In order to overcome the influence of subjective factors such as rater effect and improve the speed and accuracy of largescale composition scoring, the research on automated essay scoring has increasingly become a hot topic in language assessment. At the same time, the characteristics of oversea Chinese test make it different from <i>Hanyu Shuiping Kaoshi</i> (HSK). With the help of the word list and vocabulary for oversea Chinese, we trained and test four models. By comprehensively evaluating the performance of each grade, we can find that convolutional neural network (CNN) has the best prediction effect on compositions, and can be used as an auxiliary AES to provide reference value for oversea Chinese teaching.
A segment labeling method for Chinese nested named entity recognition

Named entity recognition (NER) is one of the fundamental technologies in natural language processing. NER task extracts the entities such as location, person and organization from unstructured texts, which plays an important role in machine translation and other tasks. However, lacking of effective solutions of nested named entities to fuzzy boundary makes Chinese named entity recognition much harder than other languages. In order to solve the challenges brought by Chinese nested named entities, a segment labeling method for Chinese named nested entity recognition is proposed in this paper. Firstly, every entity is treated as several non-separable units to improve the labeling method. Secondly, each unit will be tagged differently according to its grammar and function in a particular sentence. Finally, every main entity in the recognition sequence is considered as a starting point, and merges each sub-entity into a complete nested entity after a candidate test. To verify the effectiveness of the method, a Chinese military corpus is collected to test the performance of this proposed method. The accuracy value, recall value and F1 value of this method are higher than those of the ordinary methods, which shows that this method can effectively improve the performance of Chinese named entity recognition.
Cat face recognition using Siamese network

As a challenging problem in the field of image analysis and computer vision, face recognition has garnered a lot of attention in recent years due to its many applications across a wide range of areas. In recent years, face recognition technology has been focused on humans, but considering the expansion and improvements of the cat industry, face recognition of cats should also receive some attention. This paper proposes a face recognition system for cats using Siamese and VGG16. We will send two images of cat faces to the Siamese network, which will be converted to a vector by mapping their features to the space and then get their probability of similarity by calculating their losses. Our training model on 13,106 cat images from a dataset of 509 different cats shows that our method can recognize cats’ faces with an accuracy of 72.91% on a test dataset with 1702 image pairs containing 851 pairs labelled true, and 851 pairs labelled false. Experiments have demonstrated that this method is convenient and contactless and has a high recognition rate.
Associated words recognition in Chinese compound sentences based on deep learning

This paper deals with the problem of recognizing associated words in compound sentences. As the conventional methods rely on manually extracted utterance features, in this paper, an associated words recognition method based on neural networks is proposed. The proposed method fuses the features extracted from the compound sentence corpus into word vectors, which are fed into the constructed deep neural network model for training. To analyze the compound sentences in the modern Chinese compound sentence corpus, we extract four common utterance features to establish an utterance feature base. Then, the extract features from the utterance feature base are combined into a new feature, which possesses high discriminative ability. Based on the utterance feature base, a training set and test set is constructed for test the performance of the proposed method. Experimental results show that the proposed method both improves the efficiency of recognition and achieves a high correct rate with respect to the traditional method.
Research on the adaptability of college students’ English learning based on artificial intelligence

The evaluation of teaching effect is composed of many important indicators, and learning adaptability is one of the more important indicators. Effectively analyzing the application of learning can more accurately evaluate the learning effect of students. Therefore, under the background of artificial intelligence, the analysis of college students' English learning adaptability should be strengthened to accurately understand the effect of college students’ English learning and lay a good foundation for optimizing college students’ English teaching activities. Based on this, this paper studies the English learning adaptability of college students based on artificial intelligence through experimental analysis, and provides support for further improving the results of learning adaptability.
Text sentiment classification based on Vit-BiGRU-attention model

Nowadays, more and more users are playing an important role on the Internet. The more comments they post, the more information they contain and the more informative they are. In order to analyze the sentiment orientation of users' comments more accurately, the text Vit-BiGRU-Attention sentiment classification model is based on BiGRU and Attention mechanisms. First, the CBOW model is used to train the word vector. Second, BiGRU is combined with Viterbi algorithm to extract contextual features of the text by combining forward and backward hidden layers. Then, different weights are assigned to words by the attention mechanism to enhance the understanding of emotions and determine the polarity of emotions. Finally, the output is passed through a softmax classifier. The experimental results show that the accuracy of the model has been greatly improved.
Improve the speed of semantic segmentation models from multiple perspectives

The semantic segmentation model needs to output a category for each pixel, which is an intensive prediction task, so it needs more time. Real-world scenarios, such as autopilot, require fast reasoning speeds. Previous work has been to speed up model reasoning in one way or another, so the speed increase is limited. In this paper, we combine multi-level methods to speed up reasoning in a network and obtain remarkable results.
Power user portrait recognition based on feature vector

User portraits were originally used in the field of e-commerce, which refers to the use of big data to analyze the information and behavioral characteristics of existing users. They are to explore user preferences and needs through user information to achieve precise marketing. Power-user portrait is an important measure for a power supply service system to realize "data-driven operation". This paper starts with user characteristics and constructs an improved word vector model for electronic users to describe the behavior characteristics of electronic user groups to bring about correct marketing methods and services. The experimental results show that the improved word vector model can better meet the aggregation needs of data collection such as electricity cost, electricity cost and power loss of users.
Real-time interaction technology of gateway communication information under ubiquitous power Internet of Things access

In the context of the combination of the Internet of Things and the smart grid, how to build a cost-effective ubiquitous Internet of Things needs to solve the shortcomings of cloud computing and big data information interaction in complex network topology, and excessive reliance on manual data annotation. Therefore, it is necessary to incorporate the complex network topology into the ubiquitous Internet of Things construction process. In this paper, through the analysis of the power Internet of Things system structure design, the real-time interaction of data is completed through the access of gateway devices, and a ubiquitous Internet of Things based on Modules/TCP is proposed. The DCS gateway and the security-hardened DSC gateway information transmission mechanism, design a method for real-time interaction between the perception layer and the network layer of a given power network, and provide certain ideas for the construction of the ubiquitous power Internet of Things.
Automatic Thai Text Summarization Using Keyword-Based Abstractive Method

Traditionally, the training phase of abstractive text summarization involves inputting two sets of integer sequences; the first set representing the source text, and the second set representing words existing in the reference summary, into the encoder and decoder parts of the model, respectively. However, by using this method, the model tends to perform poorly if the source text includes words which are irrelevant or insignificant to the key ideas. In order to address this issue, we propose a new keywords-based method for abstractive summarization by combining the information provided by the source text and its keywords to generate summary. We utilize a bi-directional long short-term memory model for keyword labelling, using overlapping words between the source text and the reference summary as ground truth. The results obtained from our experiments on ThaiSum dataset show that our proposed method outperforms the traditional encoder-decoder model by 0.0425 on ROUGE-1 F1, 0.0301 on ROUGE-2 F1 and 0.0140 on BERTScore Fl.
Cross-corpus named entity recognition on Chinese electronic medical records based on label sharing

Named entity recognition (NER) on electronic medical records (EMRs) is a prerequisite task of medical information extraction. However, due to the high data sensitivity and the labeling difficulty of EMRs, there are few available data resources, making it difficult for NER to reach a practical level on EMRs. To alleviate the problem of low-resource, we propose a label sharing-based cross-corpus NER (LSCC-NER) model, which consists of the shared and private encoders divided by the cross-corpus label similarity. We design a category-wise multi-head self-attention unit for each encoder and introduce the entity category prediction task (ECP) to realize the division. Besides, considering the nested entities and data noise in EMRs, we utilize span-based decoding methods and adversarial training to further improve the robustness of our model. The experiments on two evaluation datasets of EMRs show that our proposed LSCC-NER model can achieve higher recognition performance compared with common transfer learning methods.