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29th IEEE International Conference on Image Processing, ICIP 2022

作者： Xu, Yuxiao Huo, Yongkai Song, Yukai College of Computer Science & Software Engineering Shenzhen University China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Panoramic video is considered to be an attractive video format since it provides the viewers with an immersive experience. However, only the viewers' focused region of a panoramic video, viewport, is shown on the screen. Numerous applications may benefit from the future viewport prediction, such as the adaptive viewport delivery. Two aspects may be considered for predicting the viewports. Firstly, the viewport trajectory within a time interval is highly correlated, hence the historical viewport trajectory should be utilized. Secondly, the panoramic video content within the viewport highly correlates with human visual attention. In this paper, we propose the Emotional Attention map aided panoramic Viewport Estimation (EAVE) model for predicting the future viewport trajectory. In this model, the historical viewport trajectory and the emotional attention map are jointly exploited by a Long Short-Term Memory (LSTM) network. Our experiments show that the proposed method outperforms several baseline methods in terms of prediction accuracy on a panoramic dataset containing viewing trajectory data from 50 viewers for watching 75 panoramic videos. © 2022 IEEE.

关键词： Forecasting

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A Positive Influence Maximization Algorithm in Signed Social Networks

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computers, Materials & Continua 2023年第8期76卷 1977-1994页

作者： Wenlong Zhu Yang Huang Shuangshuang Yang Yu Miao Chongyuan Peng College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar161006China College of Teacher Education Qiqihar UniversityQiqihar161006China

The influence maximization(IM)problem aims to find a set of seed nodes that maximizes the spread of their influence in a social *** positive influence maximization(PIM)problem is an extension of the IM problem,which consider the polar relation of nodes in signed social networks so that the positive influence of seeds can be the most widely *** solve the PIM problem,this paper proposes the polar and decay related independent cascade(IC-PD)model to simulate the influence propagation of nodes and the decay of information during the influence propagation in signed social *** overcome the low efficiency of the greedy based algorithm,this paper defines the polar reverse reachable(PRR)set and devises a signed reverse influence sampling(SRIS)*** algorithm utilizes the ICPD model as well as the PRR set to select *** are two phases in *** is the sampling phase,which utilizes the IC-PD model to generate the PRR set and a binary search algorithm to calculate the number of needed PRR *** other is the node selection phase,which uses a greedy coverage algorithm to select optimal ***,Experiments on three real-world polar social network datasets demonstrate that SRIS outperforms the baseline algorithms in *** on the Slashdot dataset,SRIS achieves 24.7% higher performance than the best-performing compared algorithm under the weighted cascade model when the seed set size is 25.

关键词： Positive influencemaximization polar relation information decay reverse influence sampling

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A Center-Masked Convolutional Transformer for Hyperspectral Image Classification 2

A Center-Masked Convolutional Transformer for Hyperspectral ...

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2nd Workshop on Complex Data Challenges in Earth Observation, CDCEO 2022

作者： Wang, Yifan Jiang, Shuguo Xu, Meng Zhang, Shuyu Jia, Sen College of Computer Science and Software Engineering Shenzhen University China

Hyperspectral images (HSIs) have a wide field of view and rich spectral information, where each pixel represents a small area of the earth's surface. The pixel-level classification task of HSI has become one of the research hotspots in hyperspectral image processing and analysis. More and more deep learning methods have been proposed in recent years, among which convolutional neural network (CNN) is the most influential. However, it is difficult for CNN-based models to obtain the global receptive field in HSI classification task. Besides, most of the self-supervised training methods are based on sample reconstruction, and it is not easy to achieve effective use of unlabeled samples. In this paper, we propose a novel convolutional embedding module, combined with the Transformer blocks, which successfully improves the context-awareness while retaining the local feature extraction capability. Moreover, a new self-supervised task is designed to make more efficient use of unlabeled data. Our proposed pre-training task only masks the central token and reconstructs the central pixel from a learnable vector. It allows the model to capture the patterns between the central object and surrounding objects without labels. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Pixels

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EvoSense: Towards Self-evolving WiFi-based User Gait Recognition 15

EvoSense: Towards Self-evolving WiFi-based User Gait Recogni...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Yao, Yao Luo, Chengwen Feng, Xingyu Huang, Yijing Zhang, Jin Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Motivated by the recent development in WiFi-based gait recognition, in this work we propose a new system named as EvoSense, which can dynamically adapt to new users and update the recognition models to achieve a robust recognition performance even when there are new/unknown users in the system. EvoSense adopts deep metric learning based on approaches to design an efficient feature extraction network, which is able to extract robust features for different users in WiFi gait recognition, and this makes it possible to dynamically cluster and detect new users and to update the recognition network to adapt to these new user automatically. This feature makes it appealing since WiFi gait recognition with new/unknown users is still an important open problem that hinders the wide deployment of such systems currently. We implement and evaluate the system using real-world dataset, and the results show that EvoSense achieves an accuracy of 93.3% on the dataset of 15 users with 5 additional unknown users, which shows the effectiveness of EvoSense in real-world applications. © 2022 IEEE.

关键词： Feature extraction

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HSI-DETR: A DETR-based Transfer Learning from RGB to Hyperspectral Images for Object Detection of Live and Dead Cells: To achieve better results, convert models with the fewest changes from RGB to HSI 11

HSI-DETR: A DETR-based Transfer Learning from RGB to Hypersp...

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11th International Conference on Computing and Pattern Recognition, ICCPR 2022

作者： Ye, Songxin Li, Nanying Xue, Jiaqi Long, Yaqian Jia, Sen College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781450397056

Traditional cell viability judgment methods are invasive and damaging to cells. Moreover, even under a microscope, it is difficult to distinguish live cells from dead cells by the naked eye alone. With the development of optical imaging technology, hyperspectral imaging is more and more widely used in various fields. Hyperspectral imaging is a non-contact optical technique that provides both spectral and spatial information in a single measurement. It becomes a fast, non-invasive option to differentiate between live and dead cells. In recent years, the rapid development of deep learning has provided a better way to distinguish the difference between living and dead cells through a large amount of data. However, it is often necessary to acquire large amounts of labeled data at an expensive cost to train models. This is more difficult to achieve on medical hyperspectral images. Therefore, in this paper, a new model called HSI-DETR is proposed to solve the above problem on the target detection task of live and dead cells, which is based on the detection transformer (DETR) model. The HSI-DETR model suitable for hyperspectral images (HSI) is proposed with minimal modification. Then, some parameters of DETR trained on RGB images are transferred to HSI-DETR trained on hyperspectral images. Compared to the general method, this method can train a better model with a small number of labeled samples. And compared to the DETR-R50, the AP50 of HSI-DETR-R50 has increased by 5.15%. © 2022 ACM.

关键词： Object detection

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CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for Large Language Models

CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for ...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Zhang, Qin Cai, Sihan Zhao, Jiaxu Pechenizkiy, Mykola Fang, Meng College of Computer Science and Software Engineering Shenzhen University China Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9798891761681

Ambiguity is an inherent feature of language, whose management is crucial for effective communication and collaboration. This is particularly true for Chinese, a language with extensive lexical-morphemic ambiguity. Despite the wide use of large language models (LLMs) in numerous domains and their growing proficiency in Chinese, there is a notable lack of datasets to thoroughly evaluate LLMs' ability to handle ambiguity in Chinese. To bridge this gap, we introduce the CHAmbi dataset, a specialized Chinese multi-label disambiguation dataset formatted in Natural Language Inference. It comprises 4,991 pairs of premises and hypotheses, including 824 examples featuring a wide range of ambiguities. In addition to the dataset, we develop a series of tests and conduct an extensive evaluation of pre-trained LLMs' proficiency in identifying and resolving ambiguity in the Chinese language. Our findings reveal that GPT-4 consistently delivers commendable performance across various evaluative measures, albeit with limitations in robustness. The performances of other LLMs, however, demonstrate variability in handling ambiguity-related tasks, underscoring the complexity of such tasks in the context of Chinese. The overall results highlight the challenge of ambiguity handling for current LLMs and underscore the imperative need for further enhancement in LLM capabilities for effective ambiguity resolution in the Chinese language. © 2024 Association for Computational Linguistics.

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Patty: Pattern Series-Based Semantics Analysis for Agnostic Industrial control Protocols

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5478-5491页

作者： Yang, Daoqing Yao, Yu Shan, Yao Yang, Licheng Yang, Wei Liu, Fuyi Wu, Yunfeng Northeastern University School of Computer Science and Engineering Shenyang110169 China Northeastern University College of Software Shenyang110169 China China Electronics Corporation Sixth Research Institute Beijing100080 China

Reverse engineering of agnostic industrial control protocols (ICPs) based on traffic traces is significant for the security analysis of industrial control systems. Field semantics deduction is an essential step in protocol reverse engineering following the discovery of the message field. Most existing methods rely on knowledge-based analysis for specific fields of common protocols, which require too numerous assumptions and lack semantic knowledge about ICPs. In this paper, we propose a new concept, pattern series, and design the first classification framework for inferring the semantic types of unknown ICPs. Specifically, we first present the definition of pattern series and design the field pattern series generation algorithm for building training data, then develop a field semantics classification model to learn and apply semantic features from known protocols to predict semantic types in unknown protocols. Lastly, we implement a probability-maximizing selection algorithm to obtain optimal semantic types. We demonstrate the effectiveness of the proposed method through extensive experiments with five popular ICPs, including their mixed protocols. Evaluations show that our approach significantly outperforms baseline methods in field semantic recognition, achieving ≥90.8% F1-score. © 2005-2012 IEEE.

关键词： Reverse engineering

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Multiscale Spatial-spectral Joint Feature Learning for Multispectral and Hyperspectral Image Fusion 23

Multiscale Spatial-spectral Joint Feature Learning for Multi...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Min, Zhichao Wang, Yifan Jia, Sen College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781665494571

For RGB image super-resolution, usually operates on a single image. However, due to a large number of spectral bands and high dimensionality of the data in hyperspectral images (HSIs), it is difficult for a single image super-resolution to improve spatial resolution without spectral distortion. Therefore, it is necessary to fuse spatial features of multispectral images (MSIs) to improve spatial resolution. In this paper, we take advantage of the end-to-end advantages of deep learning and propose multiscale spatial-spectral joint feature learning (MSSJFL) for multispectral and hyperspectral image fusion. MSSJFL method utilizes different scale feature learning to exact single spatial feature, spectral feature, spatial-spectral joint feature, and then restore spectral information and spatial details via feature restoration module. Experimental results demonstrate that the proposed network architecture achieves better performance compared with state-of-the-art HSI and MSI fusion. The classification accuracy of the fusion result on the real HSI dataset is also closest to the ground truth. The code of MSSJFL is available at https://***/jx-mzc/MSSJFL. © 2021 IEEE.

关键词： Representation learning Deep learning Smart cities Superresolution Network architecture Feature extraction Image restoration

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Hybrid Optimization Algorithm for Handwritten Document Enhancement

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computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang Václav Snášel Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

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Progressive Semantic Enhancement Network for Hyperspectral and LiDAR Classification

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 11327-11339页

作者： Xiyou Fu Xi Zhou Yawen Fu Pan Liu Sen Jia College of Computer Science and Software Engineering Shenzhen University Shenzhen China

The joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data is gaining attention for its improved classification accuracy. However, effectively integrating the rich spectral information of HSI and the elevation features of LiDAR has remained a challenge in multimodal fusion. This article proposes a novel approach called progressive semantic enhancement network (PSENet) for hyperspectral and LiDAR classification based on a progressive joint spatial-spectral attention mechanism. PSENet mainly comprises two modules: the spatial grouping constraint (SAGC) module and the spectral weighting constraint (SEWC) module. The SAGC module extracts multiscale features in the spatial domain, while the SEWC module focuses on enhancing semantic features in spectral dimension. By gradually utilizing spatial and spectral constraint modules to progressively enhance feature extraction, PSENet integrates affluent information for a more refined classification of ground objects. Based on experimental results, it has been demonstrated that PSENet outperforms several most advanced methods on three datasets. The SAGC and SEWC modules proposed in PSENet enable the effective integration of the spatial, spectral, and elevation information from HSI and LiDAR, providing a promising way to perform classification more accurately. The source codes of this work will be publicly available at http://***/ .

关键词： Laser radar Feature extraction Hyperspectral imaging Data mining Semantics Attention mechanisms Accuracy Distance measurement Neural networks Data models

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