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15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2019

作者： Chen, Yanping Ma, xiao xia, Hong Wang, Zhongmin Yv, Zhong School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an710121 China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China

ISBN: (纸本)9783030325909

By using the principle of sensor data consistency and combining the kalman filter algorithm with the bayesian algorithm, the accuracy of the algorithm is significantly improved under the condition of large data volume and complex system. An adaptive multi-sensor data consistency algorithm based on node credibility proposes the internal correlation between the sensor credibility and the data values it provides, and the final data fusion results are obtained by fusion of the optimized data through the sensor credibility. The advantages of this algorithm include: (1) the data is optimized by kalman filter, which reduces the influence of the data provided by the sensor with low reliability on the fusion result;(2) bayesian algorithm is adopted to calculate the correct probability of data values and the reliability of the sensor, which overcomes the inherent uncertainty of the sensor and improves the accuracy of data fusion. The results show that compared with the traditional data consistency algorithm, this algorithm can further improve the accuracy of fusion results and reduce the position error. © 2020, Springer Nature Switzerland AG.

关键词： Sensor nodes

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A spatiotemporal attention-based ResC3D model for large-scale gesture recognition

A spatiotemporal attention-based ResC3D model for large-scal...

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作者： Li, Yunan Miao, Qiguang Qi, xiangda Ma, Zhenxin Ouyang, Wanli School of Computer Science and Technology Xidian University Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Vision Xi’an China School of Electrical and Information Engineering The University of Sydney Sydney Australia

Abnormal gesture recognition has many applications in the fields of visual surveillance, crowd behavior analysis, and sensitive video content detection. However, the recognition of dynamic gestures with large-scale videos remains a challenging task due to the barriers of gesture-irrelevant factors like the variations in illumination, movement path, and background. In this paper, we propose a spatiotemporal attention-based ResC3D model for abnormal gesture recognition with large-scale videos. One key idea is to find a compact and effective representation of the gesture in both spatial and temporal contexts. To eliminate the influence of gesture-irrelevant factors, we first employ the enhancement techniques such as Retinex and hybrid median filer to improve the quality of RGB and depth inputs. Then, we design a spatiotemporal attention scheme to focus on the most valuable cues related to the moving parts for the gesture. Upon these representations, a ResC3D network, which leverages the advantages of both residual network and C3D model, is developed to extract features, together with a canonical correlation analysis-based fusion scheme for blending features from different modalities. The performance of our method is evaluated on the Chalearn IsoGD dataset. Experiments demonstrate the effectiveness of each module of our method and show the ultimate accuracy reaches 68.14%, which outperforms other state-of-the-art methods, including our basic work in 2017 Chalearn Looking at People Workshop of ICCV. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Gesture recognition

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MAVEN-ERE: A Unified Large-scale dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction

MAVEN-ERE: A Unified Large-scale Dataset for Event Coreferen...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Wang, xiaozhi Chen, Yulin Ding, Ning Peng, Hao Wang, Zimu Lin, Yankai Han, Xu Hou, Lei Li, Juanzi Liu, Zhiyuan Li, Peng Zhou, Jie Department of Computer Science and Technology BNRist Tsinghua University Beijing China Shenzhen International Graduate School Tsinghua University Beijing China THU-Siemens Ltd. China Joint Research Center for Industrial Intelligence and IoT Tsinghua University Beijing China Tsinghua University Beijing China Xi'an Jiaotong-Liverpool University Suzhou China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Pattern Recognition Center WeChat AI Tencent Inc China

The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two drawbacks of existing datasets limit event relation extraction (ERE) tasks: (1) Small scale. Due to the annotation complexity, the data scale of existing datasets is limited, which cannot well train and evaluate data-hungry models. (2) Absence of unified annotation. Different types of event relations naturally interact with each other, but existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions. To address these issues, we construct a unified large-scale human-annotated ERE dataset MAVEN-ERE with improved annotation schemes. It contains 103, 193 event coreference chains, 1, 216, 217 temporal relations, 57, 992 causal relations, and 15, 841 subevent relations, which is larger than existing datasets of all the ERE tasks by at least an order of magnitude. Experiments show that ERE on MAVEN-ERE is quite challenging, and considering relation interactions with joint learning can improve performances. The dataset and source codes can be obtained from https://***/THU-KEG/MAVEN-ERE. © 2022 Association for Computational Linguistics.

关键词： Extraction

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Electrocardiogram-to-Pair (E2P): A Secure Group Pairing Protocol for WBAN Devices

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IEEE Internet of Things Journal 2025年

作者： Zhao, Guichuan Tian, Youliang Jiang, Qi Ma, Jianfeng Guizhou University College of Computer Science and Technology Guiyang550000 China Guizhou University State Key Laboratory of Public Big Data College of Big Data and Information Engineering Guiyang550000 China Xidian University School of Cyber Engineering Xi'an710071 China

In a wireless body area network (WBAN), group pairing among multiple wearable devices enables efficient and secure broadcasting group messages. Existing pairing methods that rely on trusted concentrators, active participation of users, or homogeneous environments are vulnerable to single point of failure and have restricted practicality. In this article, we propose Electrocardiogram-to-Pair (E2P), an electrocardiogram (ECG)-based secure group pairing protocol for WBAN devices that allows the establishment of a shared group key among multiple devices without requiring the user involvement or the central device. First, all wearable devices worn by the same user simultaneously collect ECG signals, which are then pre-processed through filtering and alignment to minimize the effect of noise on the pairing. Next, an adaptive quantization method is designed to reliably quantize the pre-processed ECG signals to generate initial keys. This method is ingenious as it relies on feature transformation and dynamic thresholds generation in the quantization process, which effectively ensures the security of biometrics and increases the key generation rate. Then, the group key is established among devices with initial keys through an improved Cascade method. The simulation results demonstrate that E2P achieves an effective balance the reliability and efficiency in group pairing, with outstanding performance in both security and key generation rate. © 2014 IEEE.

关键词： Electrocardiogram group pairing key establishment wireless body area network

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Computationally Efficient Approximations for Matrix-based Rényi's Entropy

arXiv

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arxiv 2021年

作者： Gong, Tieliang Dong, Yuxin Yu, Shujian Dong, Bo The School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an710049 China The Machine Learning Group UiT - The Arctic University of Norway Department of Computer Science Vrije University Amsterdam Amsterdam Netherlands

The recently developed matrix-based Rényi's αorder entropy enables measurement of information in data simply using the eigenspectrum of symmetric positive semi-definite (PSD) matrices in reproducing kernel Hilbert space, without estimation of the underlying data distribution. This intriguing property makes this new information measurement widely adopted in multiple statistical inference and learning tasks. However, the computation of such quantity involves the trace operator on a PSD matrix G to power α (i.e., tr(Gα)), with a normal complexity of nearly O(n3), which severely hampers its practical usage when the number of samples (i.e., n) is large. In this work, we present computationally efficient approximations to this new entropy functional that can reduce its complexity to even significantly less than O(n2). To this end, we leverage the recent progress on Randomized Numerical Linear Algebra, developing Taylor, Chebyshev and Lanczos approximations to tr(Gα) for arbitrary values of α by converting it into a matrix-vector multiplication problem. We also establish the connection between the matrix-based Rényi's entropy and PSD matrix approximation, which enables exploiting both clustering and block low-rank structure of G to further reduce the computational cost. We theoretically provide approximation accuracy guarantees and illustrate the properties for different approximations. Large-scale experimental evaluations on both synthetic and real-world data corroborate our theoretical findings, showing promising speedup with negligible loss in accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Matrix algebra

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An Adaptive Model Update Object Tracking Algorithm based on DenseNet Features

An Adaptive Model Update Object Tracking Algorithm based on ...

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2020 International Conference on Networking and Network Applications, NaNA 2020

作者： Ma, Sugang Zhang, Lei Pu, Lei Yang, xiaobao Hou, Zhiqiang Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an shaanxi710121 China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an shaanxi710121 China School of Information and Navigation Air Force Engineering University Xi'an shaanxi710077 China

ISBN: (纸本)9781728189543

In order to further improve the ability to deal with complex scenes, a visual tracking algorithm based on DenseNet features and model adaptive updating is proposed. Aiming to improve the feature representation ability of correlation filtering algorithm, a more discriminative DenseNet model is used as feature extractor. Meanwhile, in order to improve the long-term tracking performance, an adaptive model update strategy is proposed. The average peak correlation energy (APCE) based on response map is used to judge and evaluate the tracking result and the model is updated only when the confidence is high. The experimental results show that the proposed algorithm achieves higher success rate and accuracy compared to the other five related algorithms, and improves the robustness of object tracking in complex scenes such as occlusion and deformation. © 2020 IEEE.

关键词： Tracking (position)

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Context-Aware Feature Attention Model for Coreference Resolution on Biomedical Texts

Research Square

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Research Square 2021年

作者： Li, Yufei Zhou, xiangyu Ma, Jie Ma, xiaoyong Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Shaanxi Xi’an China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Technology Research and Development Xi’an Jiaotong University Shaanxi Xi’an China

Background: Bio-entity Coreference resolution is an important task to gain a complete understanding of biomedical texts automatically. Previous neural network-based studies on this topic are domain system based methods which rely on some domain-specific information integration. However, for the identical mentions, this may lead to misleading information, as the model tends to get similar or even identical representations, which further leads to wrongful predictions. Results: we propose a new context-aware Feature Attention model to distinguish identical mentions effectively to better resolve coreference. The new model can represent identical mentions based on different contexts by adaptively exploiting features effectively. The proposed model substantially outperforms the state-of-the-art baselines on the BioNLP dataset with a 64.0% F1 score and further demonstrates superior performance on the differential representation and coreferential link of identical mentions. Conclusion: The context-aware Feature Attention model adaptively exploit features and represent identical mentions according to different contexts, which significantly makes the system obtain semantic information effectively and make more accurate predictions. Considering that this approach is still limited when context information is insufficient, we expect to utilize such information more fine-grained with the help of the external knowledge base in coreference resolution. © 2021, CC BY.

关键词： Semantics

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Heterogeneous two-Stream Network with Hierarchical Feature Prefusion for Multispectral Pan-Sharpening

Heterogeneous two-Stream Network with Hierarchical Feature P...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Dong Wang Yunpeng Bai Bendu Bai Chanyue Wu Ying Li National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi'an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781728176055;9781728176062

Multispectral (MS) pan-sharpening aims at producing a high spatial resolution (HR) MS image by fusing a single-band HR panchromatic (PAN) image and a corresponding MS image with low spatial resolution. In this paper, we propose a heterogeneous two-stream network (HTSNet) with hierarchical feature prefusion for MS pan-sharpening. The HTSNet employs a heterogeneous group of spatial and spectral streams for spatial and spectral information extraction, respectively. The spatial stream utilizes a 2D CNN for spatial information extraction from the PAN images, and the spectral stream obtains spectral feature cubes from the MS images by a 3D CNN. At the same time, a prefusion module is introduced to prefuse the spatial details with spectral information and transfer information between different streams, which can enhance later processing. In the experiment, the Gaofen-2 satellite dataset is utilized to compare the proposed method with the state-of-the-art MS pan-sharpening methods. Experimental results demonstrate the superiority of our HTSNet in terms of visual effect and quantitative qualities.

关键词： Satellites Three-dimensional displays Fuses Streaming media Signal processing Information retrieval Visual effects

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Plagiarism Detection of Multi-threaded Programs Using Frequent Behavioral Pattern Mining

Plagiarism Detection of Multi-threaded Programs Using Freque...

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作者： Tian, Zhenzhou Wang, Qing Gao, Cong Chen, Lingwei Wu, DInghao School of Computer Science and Technology Xi'An University of Posts and Telecommunications China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Shaanxi710121 China College of Information Sciences and Technology The Pennsylvania State University University ParkPA16802 United States

Software dynamic birthmark techniques construct birthmarks using the captured execution traces from running the programs, which serve as one of the most promising methods for obfuscation-resilient software plagiarism detection. However, due to the perturbation caused by non-deterministic thread scheduling in multi-threaded programs, such dynamic approaches optimized for sequential programs may suffer from the randomness in multi-threaded program plagiarism detection. In this paper, we propose a new dynamic thread-aware birthmark FPBirth to facilitate multi-threaded program plagiarism detection. We first explore dynamic monitoring to capture multiple execution traces with respect to system calls for each multi-threaded program under a specified input, and then leverage the Apriori algorithm to mine frequent patterns to formulate our dynamic birthmark, which can not only depict the program's behavioral semantics, but also resist the changes and perturbations over execution traces caused by the thread scheduling in multi-threaded programs. Using FPBirth, we design a multi-threaded program plagiarism detection system. The experimental results based on a public software plagiarism sample set demonstrate that the developed system integrating our proposed birthmark FPBirth copes better with multi-threaded plagiarism detection than alternative approaches. Compared against the dynamic birthmark System Call Short Sequence Birthmark (SCSSB), FPBirth achieves 12.4%, 4.1% and 7.9% performance improvements with respect to union of resilience and credibility (URC), F-Measure and matthews correlation coefficient (MCC) metric, respectively. © 2020 World Scientific Publishing Company.

关键词： Semantics

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MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results 17th

MIPI 2022 Challenge on Under-Display Camera Image Restorati...

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17th European Conference on computer vision, ECCV 2022

作者： Feng, Ruicheng Li, Chongyi Zhou, Shangchen Sun, Wenxiu Zhu, Qingpeng Jiang, Jun Yang, Qingyu Loy, Chen Change Gu, Jinwei Zhu, Yurui Wang, xi Fu, Xueyang Hu, xiaowei Hu, Jinfan Liu, xina Chen, xiangyu Dong, Chao Zhang, Dafeng Huang, Feiyu Liu, Shizhuo Wang, xiaobing Jin, Zhezhu Jiang, Xuhao Shao, Guangqi Wang, xiaotao Lei, Lei Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Huang, Tao Fang, Zhenxuan Huang, Mengluan Xu, Junwei Zhang, Yong Yang, Yuechi Shu, Qidi Yang, Zhiwen Li, Shaocong Yao, Mingde Xu, Ruikang Guan, Yuanshen Huang, Jie xiong, Zhiwei Zhu, Hangyan Liu, Ming Liu, Shaohui Zuo, Wangmeng Jia, Zhuang Song, Binbin Song, Ziqi Mao, Guiting Hou, Ben Liu, Zhimou Ke, Yi Ouyang, Dengpei Han, Dekui Zhang, Jinghao Zhu, Qi Zheng, Naishan Zhao, Feng Jin, Wu Conde, Marcos Nathan, Sabari Timofte, Radu Xu, Tianyi Xu, Jun Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Jiang, Biao Ding, Yuhan Li, WanZhang Feng, xiaoyue Chen, Sijing Zhong, Tianheng Lu, Jiyang Chen, Hongming Fan, Zhentao Chen, xiang Nanyang Technological University Singapore Singapore SenseBrain San Jose United States Shanghai AI Laboratory Shanghai China SenseTime Research and Tetras.AI Beijing China University of Science and Technology of China Hefei China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Samsung Research China Beijing China Xiaomi Beijing China Hefei University of Technology Hefei China School of Artificial Intelligence Xidian University Xi’an China School of Remote Sensing and Information Engineering Wuhan University Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Harbin Institute of Technology Harbin China University of Macau China Changsha Research Institute of Mining and Metallurgy Changsha China Tianjin University Tianjin China Computer Vision Lab University of Wurzburg Würzburg Germany Couger Inc. Tokyo Japan School of Statistics and Data Science Nankai University Tianjin China Founding Minds Software Thiruvananthapuram India Department of Electronics and Communication SRM University AP Amaravati India Fudan University Shanghai China Shenyang Aerospace University Shenyang China Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9783031250712

Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022. In total, 167 participants were successfully registered, and 19 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Under-Display Camera Image Restoration. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://***/mipi-challenge/MIPI2022. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image reconstruction

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