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Temporal aware Mixed Attention-based Convolution and Transformer Network for cross-subject EEG emotion recognition

computers in Biology and Medicine 2024年 181卷 108973-108973页

作者： Si, Xiaopeng Huang, Dong Liang, Zhen Sun, Yulin Huang, He Liu, Qile Yang, Zhuobin Ming, Dong Academy of Medical Engineering and Translational Medicine State Key Laboratory of Advanced Medical Materials and Devices Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin Key Laboratory of Brain Science and Neural Engineering Institute of Applied Psychology Tianjin University Tianjin300072 China School of Biomedical Engineering Medical School Shenzhen University Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen518060 China

Emotion recognition is crucial for human–computer interaction, and electroencephalography (EEG) stands out as a valuable tool for capturing and reflecting human emotions. In this study, we propose a hierarchical hybrid model called Mixed Attention-based Convolution and Transformer Network (MACTN). This model is designed to collectively capture both local and global temporal information and is inspired by insights from neuroscientific research on the temporal dynamics of emotions. First, we introduce depth-wise temporal convolution and separable convolution to extract local temporal features. Then, a self-attention-based transformer is used to integrate the sparse global emotional features. Besides, channel attention mechanism is designed to identify the most task-relevant channels, facilitating the capture of relationships between different channels and emotional states. Extensive experiments are conducted on three public datasets under both offline and online evaluation modes. In the multi-class cross-subject online evaluation using the THU-EP dataset, MACTN demonstrates an approximate 8% enhancement in 9-class emotion recognition accuracy in comparison to state-of-the-art methods. In the multi-class cross-subject offline evaluation using the DEAP and SEED datasets, a comparable performance is achieved solely based on the raw EEG signals, without the need for prior knowledge or transfer learning during the feature extraction and learning process. Furthermore, ablation studies have shown that integrating self-attention and channel-attention mechanisms improves classification performance. This method won the Emotional BCI Competition's final championship in the World Robot Contest. The source code is available at https://***/ThreePoundUniverse/MACTN. © 2024 Elsevier Ltd

关键词： Emotion Recognition

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Does crossing the road in a group influence pedestrians’ gaze behavior?

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Proceedings of the human Factors and Ergonomics Society Annual Meeting 2021年第1期64卷 1938 - 1942页

作者： Mirjam Lanzer Martin Baumann Faculty of Engineering Computer Science and Psychology Department of Human Factors Ulm University Ulm Germany

So far, research on pedestrians’ gaze behavior while crossing roads has mainly focused on individual pedestrians rather than groups. However, pedestrians often travel in groups especially in downtown areas. This observational study investigated how group characteristics (group size and movement of the group), situational factors (presence of traffic), and demographic variables (age and gender) influence pedestrians’ gaze behavior towards traffic during road crossing. A total of N = 197 pedestrians were observed of whom n = 24 traveled alone, n = 128 traveled in groups of two or three, and n = 45 traveled in groups of four or more. Results indicated that with increasing group size, the odds to observe traffic decreased. Diffusion of responsibility among group members might explain this effect. Finally, pedestrians’ group characteristics should be considered when developing automated vehicles that interact with vulnerable road users.

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Modelling IoT Application Requirements for Benchmarking IoT Middleware Platforms 2021

Modelling IoT Application Requirements for Benchmarking IoT ...

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23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021

作者： Mondal, Shalmoly Hassani, Alireza Jayaraman, Prem Prakash Haghighi, Pari Delir Georgakopoulos, Dimitrios Faculty of Science Engineering and Technology Swinburne University of Technology Australia School of Info Technology Deakin University Australia Department of Computer Science and Software Engineering Swinburne University of Technology Australia Department of Human Centred Computing Monash University Australia

ISBN: (纸本)9781450395564

The significant advances in the Internet of Things (IoT) have led to IoT applications being widely used in various scenarios ranging from smart city, smart farming, to Industrial IoT (IIoT) solutions. With the explosion of IoT application development, IoT middleware platforms are increasingly being used for hosting such IoT applications. This has given rise to the need for developing benchmarking solutions to analyze and test the performance of different middleware platforms that host these IoT applications. To develop such benchmarks, there are a number of key components that are needed. One of these components is an IoT dataset. To generate such datasets, representing IoT application requirements in a general and formal way is important. In this paper, we propose a framework to model the IoT Applications Requirements and enable Data Generation(ARDG-IoT). The framework supports a formal way to capture IoT application requirements and use these requirements to generate IoT data that can be used to create benchmarks for different IoT middleware platforms. ARDG-IoT consists of our proposed model, IoTSySML, which captures the application requirements, and an IoT data simulator tool, which is used to generate IoT data. We present an evaluation of the framework using a real world Industrial IoT application case study. © 2021 ACM.

关键词： Benchmarking

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Predicting RNA-RBP interactions by Using a Pseudo-Siamese Network

Predicting RNA-RBP Interactions by Using a Pseudo-Siamese Ne...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Wang, Fu Yuan, Liangliang Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9781665401265

Large scale RNA-protein binding data made computational identification of RNA-protein interactions possible, which provides great convenience for revealing the interplay between non-coding RNAs and RNA-binding proteins (RBPs). Various machine learning methods have been applied to the prediction of RNA-protein interactions (RPIs). However, most of the RPI predictors were designed for linear RNAs, while methods for circular RNAs (circRNAs) are few. Besides, most of the predictors use only RNA sequence or only protein sequences as input. Automatic feature learning from RNA and RBP sequences using deep neural networks has great potential to improve RPI predictors. In this paper, we propose a new method, PSi-bind, to predict the binding relationship between RNAs and RBPs, which is featured by a pseudo-Siamese neural network. We feed both RNA and RBP sequence embedding features to the network and perform an end-to-end learning to discover the latent association between RNAs and RBPs. Especially, we train our model on a large-scale circRNA dataset. The experimental results show that the model achieves very high accuracy on independent test set, and outperforms the state-of-the-art RPI predictors by a large margin. © 2021 IEEE.

关键词： RNA

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A Dynamic Online Dashboard for Tracking the Performance of Division 1 Basketball Athletic Performance

A Dynamic Online Dashboard for Tracking the Performance of D...

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Pacific Rim International Symposium on Dependable Computing

作者： Erica Juliano Chelsea Thakkar Christopher Taber Mehul Raval Tolga Kaya Samah Senbel School of Computer Scoence and Engineering Sacred Heart Univeristy CT USA Department of Physical Therapy and Human Movement Science Sacred Heart Univeristy CT USA School of Engineering and Applied Science Ahmedabad University Ahmedabad Gujarat India

Using Data Analytics is a vital part of sport performance enhancement. We collect data from the Division 1 'Women's basketball athletes and coaches at our university, for use in analysis and prediction. Several data sources are used daily and weekly: WHOOP straps, weekly surveys, polar straps, jump analysis, and training session information. In this paper, we present an online dashboard to visually present the data to the athletes and coaches. R shiny was used to develop the platform, with the data stored on the cloud for instant updates of the dashboard as the data becomes available. The performance of athletes can be compared to the group averages, while coaches have access to all athletes and can compare them to each other and the team averages for all parameters. A simple color-coded design was utilized to convey the coaches which of the measured parameters is in an acceptable range and which is deficient. The dashboard was reviewed by the athletes, coaches, and exercise scientists and was useful for their needs.

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Discovering Chunks in Neural Embeddings for Interpretability

arXiv

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arXiv 2025年

作者： Wu, Shuchen Alaniz, Stephan Schulz, Eric Akata, Zeynep Explainable Machine Learning Lab Helmholtz Munich Germany Max Planck Institute for Biological Cybernetics Germany Munich Center for Machine Learning Germany Department of Computer Science Technical University of Munich Germany Institute for Human-Centered AI Helmholtz Munich Germany

Understanding neural networks is challenging due to their high-dimensional, interacting components. Inspired by human cognition, which processes complex sensory data by chunking it into recurring entities, we propose leveraging this principle to interpret artificial neural population activities. Biological and artificial intelligence share the challenge of learning from structured, naturalistic data, and we hypothesize that the cognitive mechanism of chunking can provide insights into artificial systems. We first demonstrate this concept in recurrent neural networks (RNNs) trained on artificial sequences with imposed regularities, observing that their hidden states reflect these patterns, which can be extracted as a dictionary of chunks that influence network responses. Extending this to large language models (LLMs) like LLaMA, we identify similar recurring embedding states corresponding to concepts in the input, with perturbations to these states activating or inhibiting the associated concepts. By exploring methods to extract dictionaries of identifiable chunks across neural embeddings of varying complexity, our findings introduce a new framework for interpreting neural networks, framing their population activity as structured reflections of the data they process. © 2025, CC BY.

关键词： Network embeddings

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A hybrid deep learning architecture using 3D CNNs and GRUs for human action recognition

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International Journal of Engineering, Transactions B: Applications 2020年第5期33卷 959-965页

作者： Savadi Hosseini, M. Ghaderi, F. Human-Computer Interaction lab Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

Video contents have variations in temporal and spatial dimensions, and recognizing human actions requires considering the changes in both directions. To this end, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) and their combinations have been used to tackle the video dynamics. However, a hybrid architecture usually results in a more complex model and hence a greater number of parameters to be optimized. In this study, we propose to use a stack of gated recurrent unit (GRU) layers on top of a two-stream inflated convolutional neural network. Raw frames and optical flow of the video are processed in the first and second streams, respectively. We first segment the video frames in order to be able to track the video contents in more details and by using 3D CNNs extract spatial-temporal features, called local features. We then import the sequence of local features to the GRU network, and use a weighted averaging operator to aggregate the outcome of the two processing flows, called global features. The evaluations confirm acceptable results for the two HMDB51 and UCF101 datasets. The proposed method resulted in a 1.6% improvement in the classification accuracy of the HMDB51 challenging dataset compared to the best reported results. © 2020 Materials and Energy Research Center. All rights reserved.

关键词： Convolutional neural networks

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Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching Between Parts and Words

Parts2Words: Learning Joint Embedding of Point Clouds and Te...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Chuan Tang Xi Yang Bojian Wu Zhizhong Han Yi Chang School of Artificial Intelligence Jilin University China MoE Engineering Research Center of Knowledge-Driven Human-Machine Intelligence China Zhejiang University The Department of Computer Science Wayne State University USA

Shape-Text matching is an important task of high-level shape understanding. Current methods mainly represent a 3D shape as multiple 2D rendered views, which obviously can not be understood well due to the structural ambiguity caused by self-occlusion in the limited number of views. To resolve this issue, we directly represent 3D shapes as point clouds, and propose to learn joint embedding of point clouds and texts by bidirectional matching between parts from shapes and words from texts. Specifically, we first segment the point clouds into parts, and then leverage optimal transport method to match parts and words in an optimized feature space, where each part is represented by aggregating features of all points within it and each word is abstracted by its contextual information. We optimize the feature space in order to enlarge the similarities between the paired training samples, while simultaneously maximizing the margin between the unpaired ones. Experiments demonstrate that our method achieves a significant improvement in accuracy over the SOTAs on multi-modal retrieval tasks under the Text2Shape dataset. Codes are available at here.

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How Deep is Love in LLMs’ Hearts? Exploring Semantic Size in human-like Cognition

arXiv

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arXiv 2025年

作者： Yao, Yao Yang, Yifei Ma, Xinbei Yang, Dongjie Zhang, Zhuosheng Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan University Wuhan430072 China

How human cognitive abilities are formed has long captivated researchers. However, a significant challenge lies in developing meaningful methods to measure these complex processes. With the advent of large language models (LLMs), which now rival human capabilities in various domains, we are presented with a unique testbed to investigate human cognition through a new lens. Among the many facets of cognition, one particularly crucial aspect is the concept of semantic size—the perceived magnitude of both abstract and concrete words or concepts. This study seeks to investigate whether LLMs exhibit similar tendencies in understanding semantic size, thereby providing insights into the underlying mechanisms of human cognition. We begin by exploring metaphorical reasoning, comparing how LLMs and humans associate abstract words with concrete objects of varying sizes. Next, we examine LLMs’ internal representations to evaluate their alignment with human cognitive processes. Our findings reveal that multi-modal training is crucial for LLMs to achieve more human-like understanding, suggesting that real-world, multi-modal experiences are similarly vital for human cognitive development. Lastly, we examine whether LLMs are influenced by attention-grabbing headlines with larger semantic sizes in a real-world web shopping scenario. The results show that multi-modal LLMs are more emotionally engaged in decision-making, but this also introduces potential biases, such as the risk of manipulation through clickbait headlines. Ultimately, this study offers a novel perspective on how LLMs interpret and internalize language, from the smallest concrete objects to the most profound abstract concepts like ‘love’. The insights gained not only improve our understanding of LLMs but also provide new avenues for exploring the cognitive abilities that define human intelligence. Our code and dataset is publicly available at https://***/Zoeyyao27/LLM-Semantic-Size-Understanding. © 2025, CC BY.

关键词： Semantics

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Predicting Price Direction of Bitcoin based on Hybrid Model of LSTM and Dense Neural Network Approach 4

Predicting Price Direction of Bitcoin based on Hybrid Model ...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Karahyla, Jai Kishan Sharma, Neelam Chamoli, Sushant Shirgire, Anil Kant, Ravi Chauhan, Amit Pgcc Hospital Management Professor and Head Respiratory Medicine Mmimsr Mm Deemed University Human Rights HaryanaMullana Ambala India Lovely Professional University Physical Education Punjab Phagwara India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Dr D y Patil Institute of Technology Department of Civil Engineering MaharashtraPimpri Pune India Shoolini University Cse Department Himachal Pradesh Solan India Department of Life Sciences Karnataka Bengaluru India

ISBN: (纸本)9798350300093

Bitcoin is a rapidly growing but extremely risky cryptocurrency. It marks a watershed moment in the history of cash. These days, digital currency is preferred to actual money. Bitcoin has decentralized authority and placed it in the hands of its users. Many people are joining the largest and most well-known Bitcoin mining pools as the risk of working alone is too great. In order to enhance their chances of creating the next block in the Bitcoins blockchain and decrease the mining reward volatility, users can band together to form Bitcoin pools. This tendency toward consolidation may also be seen in the rise of large-scale mining farms equipped with powerful mining resources and speedy processing capability. Because of the risk of a 51% assault, this pattern shows that Bitcoin's pure, decentralized protocol is moving toward greater centralization in its distribution network. Not to be overlooked is the resulting centralization of the bitcoin network as a result of cloud wallets making it simple for new users to join. Because of the easily hackable nature of Bitcoin technologies, this could lead to a wide range of security vulnerabilities. The proposed approach uses normalization and filling missing values in preprocessing, PCA for feature Extraction and finally training the model using LSTM-DNN Models. The proposed approach outperforms other two models such as CNN and DNN. © 2023 IEEE.

关键词： Bitcoin

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