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International IEEE/EMBS Conference on Neural engineering, CNE

作者： Shadi Sartipi Mujdat Cetin Department of Electrical and Computer Engineering University of Rochester Rochester USA Department of Electrical and Computer Engineering Goergen Institute for Data Science University of Rochester Rochester USA

Decoding the human emotional states based on electroencephalography (EEG) in affective brain-computer interfaces (BCI) is a great challenge due to inter-subject variability. Existing methods mostly use large amounts of EEG data of each new subject to calibrate the algorithm, which could be time-consuming and not user-oriented. To address this issue, we propose a combination of using transformers (TF) and adversarial discriminative domain adaptation (ADDA) to perform the emotion recognition task in a cross-subject manner. TF principally relies on the attention mechanism. Our proposed approach performs scaledot product attention on the feature-channel aspect of EEG data to improve the spatial features. Then, the temporal transforming is applied to get the global discriminative representations from the time component. Moreover, ADDA aims to minimize the discrepancy of EEG data from various subjects. We evaluate the proposed ADDA-TF on the publicly available DEAP dataset and demonstrate the improvements it provides on low versus high valence and arousal classification.

关键词： Emotion recognition Neural engineering Transformers Electroencephalography Brain-computer interfaces Decoding Task analysis

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Prompt Learning on Temporal Interaction Graphs

arXiv

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arXiv 2024年

作者： Chen, Xi Zhang, Siwei Xiong, Yun Wu, Xixi Zhang, Jiawei Sun, Xiangguo Zhang, Yao Zhao, Feng Kang, Yulin Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China IFM Lab Department of Computer Science University of California DavisCA United States Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Ant Group Shanghai Hangzhou China

Temporal Interaction Graphs (TIGs) are widely utilized to represent real-world systems, such as e-commerce and social networks. To facilitate representation learning on TIGs, researchers have proposed a series of TIG models. However, these models are still facing two tough gaps between the pre-training and downstream predictions in their "pre-train, predict" training paradigm. First, there is a temporal gap that exhibits limited accommodation ability to their timely predictions. This shortcoming severely undermines their applicability in distant future predictions on the dynamically evolving TIG data. Second, there is a semantic gap due to the lack of versatility in these pre-trained models to effectively cater to diverse downstream tasks. This hinders their practical applications, as they struggle to align with their learning and prediction capabilities across various application scenarios. Recently, the "pre-train, prompt" paradigm has emerged as a lightweight mechanism for model generalization. Therefore, applying this paradigm within TIGs is a potential solution to solve the aforementioned challenges. However, the adaptation of this paradigm to TIGs is not straightforward. The prevalent application of prompting in static graph contexts falls short in temporal settings due to a lack of consideration for time-sensitive dynamics and a deficiency in expressive power. To address this issue, we introduce Temporal Interaction Graph Prompting (TIGPrompt), a versatile framework that seamlessly integrates with existing TIG models, bridging both the temporal and semantic gaps mentioned above. In detail, we propose a temporal prompt generator to offer temporally-aware prompts for different tasks. These prompts stand out for their minimalistic design, relying solely on the fine-tuning of the prompt generator with very little supervision data, which is extremely efficient. To cater to varying computational resource demands, we propose an extended "pre-train, prompt-based fine

关键词： Forecasting

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StAGN: Spatial-Temporal Adaptive Graph Network via Contrastive Learning for Sleep Stage Classification

StAGN: Spatial-Temporal Adaptive Graph Network via Contrasti...

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2023 SIAM International Conference on data Mining, SDM 2023

作者： Chen, Junyang Dai, Yidan Chen, Xianhui Shen, Yingshan Luximon, Yan Wang, Hailiang He, Yuxin Ma, Wenjun Fan, Xiaomao College of Big Data and Internet Shenzhen Technology University Shenzhen China School of Computer Science South China Normal University Guangzhou China Department of Electrical and Computer Engineering New York University New York United States School of Design The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781611977653

Sleep stage classification is a critical concern in sleep quality assessment and disease diagnosis. Graph network based studies for sleep stages classification have achieved promising performance. However, these studies still ignored the importance of learning morphological feature information with the spatial-temporal relationship among multi-modal physiological signals. To address this issue, we propose a Spatial-temporal Adaptive Graph Network named StAGN for sleep stage classification. The main advantage of StAGN is to adaptively learn the time-dependent and channel-wise interdependent waveform morphological features in multimodal physiological signals. Such features will be extracted by a modified 1-dimensional ResNet with a projection shortcut connection and adjusted by a joint spatial-temporal attention, thereby best serving the followed brain topological connection graph network for sleep stage classification. Meanwhile, we leverage the contrastive learning scheme with label information to further improve classification accuracy without changing the signal morphology. Experiment results on two publicly available sleep datasets of ISRUC-S1 and ISRUC-S3 show that the proposed StAGN can achieve a competitive performance for sleep stage classification, which is superior to the state-of-the-art counterparts. Copyright © 2023 by SIAM.

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arXiv

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arXiv 2023年

作者： Rácz, Miklós Z. Sridhar, Anirudh Department of Statistics and Data Science Department of Computer Science Northwestern University EvanstonIL United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ United States

We consider the task of estimating the latent vertex correspondence between two edge-correlated random graphs with generic, inhomogeneous structure. We study the so-called k-core estimator, which outputs a vertex correspondence that induces a large, common subgraph of both graphs which has minimum degree at least k. We derive sufficient conditions under which the k-core estimator exactly or partially recovers the latent vertex correspondence. Finally, we specialize our general framework to derive new results on exact and partial recovery in correlated stochastic block models, correlated Chung-Lu graphs, and correlated random geometric graphs. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphic methods

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An Online Chronic Disease Prediction System Based on Incremental Deep Neural Network

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computers, Materials & Continua 2021年第4期67卷 951-964页

作者： Bin Yang Lingyun Xiang Xianyi Chen Wenjing Jia School of Design Jiangnan UniversityWuxi214122China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and TechnologyNanjing210044China School of Electrical and Data Engineering University of Technology SydneySydney2000Australia

Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural ***,due to the complexity of the human body,there are still many challenges to face in that *** of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients,*** paper presents a novel chronic disease prediction system based on an incremental deep neural *** propensity of users suffering from chronic diseases can continuously be evaluated in an incremental *** time,the system can predict diabetes more and more accurately by processing the feedback *** diabetes prediction studies are based on a common dataset,the Pima Indians diabetes dataset,which has only eight input *** order to determine the correlation between the pathological characteristics of diabetic patients and their daily living resources,we have established an in-depth cooperation with a hospital.A Chinese diabetes dataset with 575 diabetics was ***’data collected by different sensors were used to train the network *** evaluated our system using a real-world diabetes dataset to confirm its *** experimental results show that the proposed system can not only continuously monitor the users,but also give early warning of physiological data that may indicate future diabetic ailments.

关键词： Deep learning incremental learning network architecture design chronic disease prediction

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Malware Identification Method Based on Image Analysis 11

Malware Identification Method Based on Image Analysis

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11th International Conference on Information Technology in Medicine and Education, ITME 2021

作者： Liu, Yanhua Li, Jiaqi Liu, Baoxu Gao, Xiaoling Liu, Ximeng Fuzhou University College of Computer and Data Science Fuzhou China Institute of Information Engineering Beijing China

ISBN: (纸本)9781665406796

In this paper, we propose a malware identification method employed by image analysis and generative adversarial networks, designed to solve the problems of increasingly sophisticated attack forms, insufficient sample data in malware. Specifically, we first generate fixed-size gray images of malware, which neither disassembly nor code execution is required for identification. Moreover, we introduce generative adversarial networks into malware identification for few samples scenarios and malware variants. Through the game training of generator and discriminator, the malware detection model is obtained from the discriminator and the samples are generated by the generator for data augment. Finally, we demonstrate that the proposed method is efficient and feasible using extensive experiments. © 2021 IEEE.

关键词： Generative adversarial networks

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Research on Deep Clustering Based Trajectory Frequent Pattern Mining

Research on Deep Clustering Based Trajectory Frequent Patter...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Bohua Tian Yan Wang College of Computer Science Inner Mongolia University Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The surge in the number of e-bikes also brings certain problems of traffic road order and safety management. Analyzing and mining the travel routes of e-bikes to discover the valuable patterns latent in large-scale trajectories will help the traffic department to manage e-bikes. For this reason, this paper proposes a frequent pattern mining method for trajectories based on deep clustering, which shows the travel patterns of e-bikes through frequent trajectory patterns in order to improve the transportation service capacity. In the feature extraction stage, the method transforms the trajectory point sequence into a raster sequence, and obtains the potential vectors of e-bike trajectories based on the autoencoder; in the clustering stage, it integrates the deep learning model and the improved clustering algorithm by adding the chameleon model to mine the frequent pattern of trajectory vectors, and completes the unsupervised clustering of deep learning by updating the parameters and clustering centers in the feature extraction stage. Finally, the effectiveness and application value of our method are proved by a large number of experiments.

关键词： Deep learning Software algorithms Transportation Safety management Clustering algorithms Transforms Feature extraction

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EGAGN: Generative Adversarial Graph Networks for Ethereum Phishing Scams Detection

EGAGN: Generative Adversarial Graph Networks for Ethereum Ph...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Bo Cui Zhenyu Zhang Wenhan Hou College of Computer Science Inner Mongolia University Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

In recent years, phishing scams have seriously threatened Ethereum's ecological security and caused massive economic losses. Moreover, the significant disparity between the number of normal addresses and phishing addresses on Ethereum poses a challenge for detecting phishing scams. Existing studies primarily employ methods such as oversampling, filtering rules, and traditional machine learning models to resolve the Ethereum data imbalance problem. However, these methods disregard topological structure features of the transaction network and the link relationship between nodes. In this paper, we propose an Ethereum phishing scams detection model based on Generative Adversarial Graph Networks called EGAGN to alleviate imbalanced data, enhance node representation, and then improve detection performance. Specifically, the graph generator and discriminator play with each other to generate synthetic nodes that satisfy the real nodes distribution to balance Ethereum data and extract effective network structural features. We further extract statistical features from the transaction network and aggregate transaction records based on time series to obtain trading features. The complete representation of nodes is composed of the above three types of features to detect phishing nodes. Experimental results on the real-world Ethereum dataset show that EGAGN outperforms existing models and is far ahead in recall, which indicates that our model can effectively detect Ethereum phishing scams.

关键词： Phishing Biological system modeling Computational modeling Smart contracts Time series analysis Feature extraction data models

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TWO-STAGE PREDICT+OPTIMIZE FOR MIXED INTEGER LINEAR PROGRAMS WITH UNKNOWN PARAMETERS IN CONSTRAINTS

arXiv

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arXiv 2023年

作者： Hu, Xinyi Lee, Jasper C.H. Lee, Jimmy H.M. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Sciences Institute for Foundations of Data Science University of Wisconsin–Madison WI United States

Consider the setting of constrained optimization, with some parameters unknown at solving time and requiring prediction from relevant features. Predict+Optimize is a recent framework for end-to-end training supervised learning models for such predictions, incorporating information about the optimization problem in the training process in order to yield better predictions in terms of the quality of the predicted solution under the true parameters. Almost all prior works have focused on the special case where the unknowns appear only in the optimization objective and not the constraints. Hu et al. proposed the first adaptation of Predict+Optimize to handle unknowns appearing in constraints, but the framework has somewhat ad-hoc elements, and they provided a training algorithm only for covering and packing linear programs. In this work, we give a new simpler and more powerful framework called Two-Stage Predict+Optimize, which we believe should be the canonical framework for the Predict+Optimize setting. We also give a training algorithm usable for all mixed integer linear programs, vastly generalizing the applicability of the framework. Experimental results demonstrate the superior prediction performance of our training framework over all classical and state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Constrained optimization

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Sentimental Insight 2.0: Exploring the Frontiers of Opinion Mining with Deep Learning

Sentimental Insight 2.0: Exploring the Frontiers of Opinion ...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： S.Karthick Murugan S. Jayachandran G. Mageshwari J. Latha Naveen G Department of Computer Science and Engineering R. M. D. Engineering College Kavaraipettai Thiruvallur Tamilnadu India Department of Computer Science and Application SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Tiruvallur Tamilnadu India Department of Computer Science and Engineering R. M. K. Engineering College Kavaraipettai Tiruvallur Tamilnadu India Department of Electronics and Communication Engineering Madha Institute of Engineering and Technology Chennai Tamilnadu India

“Sentimental Insight 2.0” emerges as revolutionary research using deep learning techniques in response to the increasing demand for more complex sentiment analysis in the age of information abundance. Traditional sentiment analysis methods sometimes fail to accurately capture the particulars of emotions, sarcasm, and context, demanding a more advanced technique. The existing system's limitations include its inability to detect minor sentiment variations and its vulnerability to noisy and distorted data. The proposed system integrates cutting-edge deep learning architectures such as recurrent neural networks (RNNs), extended short-term memory networks (LSTMs), and transformer models into an extensive structure. The result showcases the advanced sentiment analysis, outperforming existing systems with 94% accuracy, 93% precision, 91% recall, and 93% F1 score, demonstrating superior computational efficiency and emotion classification across diverse sentiments. In the information-centric world, the study's results significantly expand sentiment analysis skills, providing a vital resource for applications in social media analytics, consumer feedback, and market sentiment prediction.

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