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A personality-guided affective brain-computer interface for implementation of emotional intelligence in machines

Frontiers of Information Technology & Electronic Engineering 2022年第8期23卷 1158-1173页

作者： Shaojie LI Wei LI Zejian XING Wenjie YUAN Xiangyu WEI Xiaowei ZHANG Bin HU School of Information Science and Engineering Lanzhou UniversityLanzhou 730099China

Affective brain-computer interfaces have become an increasingly important topic to achieve emotional intelligence in human–machine collaboration.However,due to the complexity of electroencephalogram(EEG)signals and the individual differences in emotional response,it is still a great challenge to design a reliable and effective model.Considering the influence of personality traits on emotional response,it would be helpful to integrate personality information and EEG signals for emotion recognition.This study proposes a personalityguided attention neural network that can use personality information to learn effective EEG representations for emotion recognition.Specifically,we first use a convolutional neural network to extract rich temporal and regional representations of EEG signals,and a special convolution kernel is designed to learn inter-and intra-regional correlations simultaneously.Second,inspired by the fact that electrodes within distinct brain scalp regions play different roles in emotion recognition,a personality-guided regional-attention mechanism is proposed to further explore the contributions of electrodes within a region and between regions.Finally,attention-based long short-term memory is designed to explore the temporal dynamics of EEG signals.Experiments on the AMIGOS dataset,which is a dataset for multimodal research for affect,personality traits,and mood on individuals and groups,show that the proposed method can significantly improve the performance of subject-independent emotion recognition and outperform state-of-the-art methods.

关键词： Electroencephalogram(EEG) Emotion recognition attention mechanism Personality traits

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Investigating Periodic Dependencies to Improve Short-Term Load Forecasting

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Energy Engineering 2024年第3期121卷 789-806页

作者： Jialin Yu Xiaodi Zhang Qi Zhong Jian Feng State Grid Zhejiang Jiaxing Electric Power Co. Ltd.Jiaxing314000China State Grid Zhejiang Electric Power Co. Ltd.Hangzhou310000China

With a further increase in energy flexibility for customers,short-term load forecasting is essential to provide benchmarks for economic dispatch and real-time alerts in power grids.The electrical load series exhibit periodic patterns and share high associations with metrological data.However,current studies have merely focused on point-wise models and failed to sufficiently investigate the periodic patterns of load series,which hinders the further improvement of short-term load forecasting accuracy.Therefore,this paper improved Autoformer to extract the periodic patterns of load series and learn a representative feature from deep decomposition and reconstruction.In addition,a novel multi-factor attention mechanism was proposed to handle multi-source metrological and numerical weather prediction data and thus correct the forecasted electrical load.The paper also compared the proposed model with various competitive models.As the experimental results reveal,the proposed model outperforms the benchmark models and maintains stability on various types of load consumers.

关键词： Load forecasting transformer attention mechanism power grid

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Improving Yolo5 for Real-Time Detection of Small Targets in Side Scan Sonar Images

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Journal of Ocean University of China 2023年第6期22卷 1551-1562页

作者： WANG Jianjun WANG Qi GAO Guocheng QIN Ping HE Bo Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Side scan sonar(SSS)is an important means to detect and locate seafloor targets.Autonomous underwater vehicles(AUVs)carrying SSS stay near the seafloor to obtain high-resolution images and provide the outline of the target for observers.The target feature information of an SSS image is similar to the background information,and a small target has less pixel information;therefore,accu-rately identifying and locating small targets in SSS images is challenging.We collect the SSS images of iron metal balls(with a diameter of 1m)and rocks to solve the problem of target misclassification.Thus,the dataset contains two types of targets,namely,‘ball’and‘rock’.With the aim to enable AUVs to accurately and automatically identify small underwater targets in SSS images,this study designs a multisize parallel convolution module embedded in state-of-the-art Yolo5.An attention mechanism transformer and a convolutional block attention module are also introduced to compare their contributions to small target detection accuracy.The performance of the proposed method is further evaluated by taking the lightweight networks Mobilenet3 and Shufflenet2 as the backbone network of Yolo5.This study focuses on the performance of convolutional neural networks for the detection of small targets in SSS images,while another comparison experiment is carried out using traditional HOG+SVM to highlight the neural network’s ability.This study aims to improve the detection accuracy while ensuring the model efficiency to meet the real-time working requirements of AUV target detection.

关键词： side scan sonar images autonomous underwater vehicle multisize parallel convolution module attention mechanism

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Novel multi‐domain attention for abstractive summarisation

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 796-806页

作者： Chunxia Qu Ling Lu Aijuan Wang Wu Yang Yinong Chen College of Computer Science and Engineering Chongqing University of TechnologyChongqingChina School of Computing and Augmented Intelligence Arizona State UniversityTempeArizonaUSA

The existing abstractive text summarisation models only consider the word sequence correlations between the source document and the reference summary,and the summary generated by models lacks the cover of the subject of source document due to models'small perspective.In order to make up these disadvantages,a multi‐domain attention pointer(MDA‐Pointer)abstractive summarisation model is proposed in this work.First,the model uses bidirectional long short‐term memory to encode,respectively,the word and sentence sequence of source document for obtaining the semantic representations at word and sentence level.Furthermore,the multi‐domain attention mechanism between the semantic representations and the summary word is established,and the proposed model can generate summary words under the proposed attention mechanism based on the words and sen-tences.Then,the words are extracted from the vocabulary or the original word sequences through the pointer network to form the summary,and the coverage mechanism is introduced,respectively,into word and sentence level to reduce the redundancy of sum-mary content.Finally,experiment validation is conducted on CNN/Daily Mail dataset.ROUGE evaluation indexes of the model without and with the coverage mechanism are improved respectively,and the results verify the validation of model proposed by this paper.

关键词： abstractive summarisation attention mechanism Bi‐LSTM coverage mechanism pointer network

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Regional Economic Development Trend Prediction Method Based on Digital Twins and Time Series Network

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Computers, Materials & Continua 2023年第8期76卷 1781-1796页

作者： Runguo Xu Xuehan Yu Xiaoxue Zhao School of International Relations Yonsei UniversitySeoul03722Korea

At present,the interpretation of regional economic development(RED)has changed from a simple evaluation of economic growth to a focus on economic growth and the optimization of economic structure,the improvement of economic relations,and the change of institutional innovation.This article uses the RED trend as the research object and constructs the RED index to conduct the theoretical analysis.Then this paper uses the attention mechanism based on digital twins and the time series network model to verify the actual data.Finally,the regional economy is predicted according to the theoretical model.The specific research work mainly includes the following aspects:1)This paper introduced the development status of research on time series networks and economic forecasting at home and abroad.2)This paper introduces the basic principles and structures of long and short-term memory(LSTM)and convolutional neural network(CNN),constructs an improved CNN-LSTM model combined with the attention mechanism,and then constructs a regional economic prediction index system.3)The best parameters of the model are selected through experiments,and the trained model is used for simulation experiment prediction.The results show that the CNN-LSTM model based on the attentionmechanism proposed in this paper has high accuracy in predicting regional economies.

关键词： Regional economic development attention mechanism digital twins time series network

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AnimeNet: A Deep Learning Approach for Detecting Violence and Eroticism in Animated Content

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Computers, Materials & Continua 2023年第10期77卷 867-891页

作者： Yixin Tang Department of Cooperative Course of Performance Film&AnimationSejong UniversitySeoul05006Korea

Cartoons serve as significant sources of entertainment for children and adolescents.However,numerous animated videos contain unsuitable content,such as violence,eroticism,abuse,and vehicular accidents.Current content detection methods rely on manual inspection,which is resource-intensive,time-consuming,and not always reliable.Therefore,more efficient detection methods are necessary to safeguard young viewers.This paper addresses this significant problem by proposing a novel deep learning-based system,AnimeNet,designed to detect varying degrees of violent and erotic content in *** utilizes a novel Convolutional Neural Network(CNN)model to extract image features effectively,classifying violent and erotic scenes in videos and images.The novelty of the work lies in the introduction of a novel channel-spatial attention module,enhancing the feature extraction performance of the CNN model,an advancement over previous efforts in the literature.To validate the approach,I compared AnimeNet with state-of-the-art classification methods,including ResNet,RegNet,ConvNext,ViT,and MobileNet.These were used to identify violent and erotic scenes within specific video frames.The results showed that AnimeNet outperformed these models,proving it to be well-suited for real-time applications in videos or images.This work presents a significant leap forward in automatic content detection in animation,offering a high-accuracy solution that is less resource-intensive and more reliable than current methods.The proposed approach enables it possible to better protect young audiences from exposure to unsuitable content,underlining its importance and potential for broad social impact.

关键词： Computer vision animation deep learning classification attention mechanism

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A Pixel–Channel Hybrid attention Model for Image Processing

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Tsinghua Science and Technology 2022年第5期27卷 804-816页

作者： Qiang Hua Liyou Chen Pan Li Shipeng Zhao Yan Li College of Mathematics and Information Science Hebei UniversityBaoding 071002China Research Center for Applied Mathematics and Interdisciplinary Sciences Beijing Normal UniversityZhuhai 519087China

In the field of image processing,better results can often be achieved through the deepening of neural network layers involving considerably more parameters.In image classification,improving classification accuracy without introducing too many parameters remains a challenge.As for image conversion,the use of the conversion model of the generative adversarial network often produces semantic artifacts,resulting in images with lower quality.Thus,to address the above problems,a new type of attention module is proposed in this paper for the first time.This proposed approach uses the pixel-channel hybrid attention(PCHA) mechanism,which combines the attention information of the pixel and channel domains.The comparative results of using different attention modules on multiple-image data verify the superiority of the PCHA module in performing classification tasks.For image conversion,we propose a skip structure(S-PCHA model) in the up-and down-sampling processes based on the PCHA model.The proposed model can help the algorithm identify the most distinctive semantic object in a given image,as this structure effectively realizes the intercommunication of encoder and decoder information.Furthermore,the results showed that the attention model could establish a more realistic mapping from the source domain to the target domain in the image conversion algorithm,thus improving the quality of the image generated by the conversion model.

关键词： deep learning attention mechanism image classification image processing convolutional neural networks

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Deep Learning-based Wireless Signal Classification in the IoT Environment

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Computers, Materials & Continua 2022年第6期71卷 5717-5732页

作者： Hyeji Roh Sheungmin Oh Hajun Song Jinseo Han Sangsoon Lim Department of Computer Engineering Sungkyul UniversityAnyang430742Korea

With the development of the Internet of Things(IoT),diverse wireless devices are increasing rapidly.Those devices have different wireless interfaces that generate incompatible wireless signals.Each signal has its own physical characteristics with signal modulation and demodulation scheme.When there exist different wireless devices,they can suffer from severe Cross-Technology Interferences(CTI).To reduce the communication overhead due to the CTI in the real IoT environment,a central coordinator can be able to detect and identify wireless signals existing in the same communication areas.This paper investigates how to classify various radio signals using Convolutional Neural Networks(CNN),Long Short-TermMemory(LSTM)and attention mechanism.CNN can reduce the amount of computation by reducing weights by using convolution,and LSTM belonging to RNNmodels can alleviate the long-term dependence problem.Furthermore,attention mechanism can reduce the short-term memory problem of RNNs by reexamining the data output from the decoder and the entire data entered into the encoder at every point in time.To accurately classify radio signals according to their weights,we design a model based on CNN,LSTM,and attention mechanism.As a result,we propose a model CLARINet that can classify original data by minimizing the loss and detects changes in sequences.In a case of the real IoT environment with Wi-Fi,Bluetooth and ZigBee devices,we can normally obtain wireless signals from 10 to 20 dB.The accuracy of CLARINet’s radio signal classification with CNN-LSTM and attention mechanism can be seen that signal-to-noise ratio(SNR)exhibits high accuracy at 16 dB to about 92.03%.

关键词： attention mechanism wireless signal CNN-LSTM classification deep-learning

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Multimodal SocialMedia Fake News Detection Based on Similarity Inference and Adversarial Networks

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Computers, Materials & Continua 2024年第4期79卷 581-605页

作者： Fangfang Shan Huifang Sun Mengyi Wang College of Computer Zhongyuan University of TechnologyZhengzhou450007China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou 450001China

As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocreate a misleading perception among users. While early research primarily focused on text-based features forfake news detection mechanisms, there has been relatively limited exploration of learning shared representationsin multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal modelfor detecting fake news, which relies on similarity reasoning and adversarial networks. The model employsBidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural Network (Text-CNN) for extracting textual features while utilizing the pre-trained Visual Geometry Group 19-layer (VGG-19) toextract visual features. Subsequently, the model establishes similarity representations between the textual featuresextracted by Text-CNN and visual features through similarity learning and reasoning. Finally, these features arefused to enhance the accuracy of fake news detection, and adversarial networks have been employed to investigatethe relationship between fake news and events. This paper validates the proposed model using publicly availablemultimodal datasets from Weibo and Twitter. Experimental results demonstrate that our proposed approachachieves superior performance on Twitter, with an accuracy of 86%, surpassing traditional unimodalmodalmodelsand existing multimodal models. In contrast, the overall better performance of our model on the Weibo datasetsurpasses the benchmark models across multiple metrics. The application of similarity reasoning and adversarialnetworks in multimodal fake news detection significantly enhances detection effectiveness in this paper. However,current research is limited to the fusion of only text and image modalities. Future research directions should aimto further integrate features fromadditionalmodalities to comprehensiv

关键词： Fake news detection attention mechanism image-text similarity multimodal feature fusion

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AOGAN:A generative adversarial network for screen space ambient occlusion

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Computational Visual Media 2022年第3期8卷 483-494页

作者： Lei Ren Ying Song Zhejiang Sci-Tech University Hangzhou 310018China 2011 Collaborative Innovation Center for Garment Personal Customization of Zhejiang Province China Key Lab of Silk and Culture Heritage and Products Design Digital Technology Ministry of Culture and TourismChina

Ambient occlusion(AO)is a widely-used real-time rendering technique which estimates light intensity on visible scene surfaces.Recently,a number of learning-based AO approaches have been proposed,which bring a new angle to solving screen space shading via a unified learning framework with competitive quality and speed.However,most such methods have high error for complex scenes or tend to ignore details.We propose an end-to-end generative adversarial network for the production of realistic AO,and explore the importance of perceptual loss in the generative model to AO accuracy.An attention mechanism is also described to improve the accuracy of details,whose effectiveness is demonstrated on a wide variety of scenes.

关键词： ambient occlusion(AO) attention mechanism generative adversarial network(GAN) perceptual loss

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