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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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A firmware upgrade solution based on hardware unique ID and SM4 cryptographic algorithm

A firmware upgrade solution based on hardware unique ID and ...

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2023 International Conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Gao, Shaofeng Wang, Kun Chen, Xinze Fu, Yong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250000 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

ISBN: (纸本)9781510677685

This article presents and implements a firmware upgrade solution for embedded systems, which aims to address the vulnerability of using the same file that can be easily copied or stolen. When upgrading firmware, a txt file is sent by the upper-level software, which is encrypted using the SM4 algorithm. This encryption is based on a hardware-unique ID and a pre-stored upper-level software-unique ID, which act as encryption keys. During the upgrade, a secure downloader utilizes its hardware-unique ID and a pre-stored upper-level software-unique ID as decryption keys to decrypt the file. If the decryption key does not match the encryption key, the decrypted txt file will not be readable. Once decrypted, the firmware is downloaded to the target device using BSL (Bootstrap Loader) technology. Practical tests have shown that this method can achieve "one file, one key,"effectively preventing unauthorized use of the upgraded firmware. © 2024 SPIE.

关键词： Firmware

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Zero-Csc: Low-Light Image Enhancement with Zero-Reference Color Self-Calibration

SSRN

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SSRN 2024年

作者： Jin, Haiyan Chen, Yujia Zuo, Fengyuan Su, Haonan Zhang, YuanLin Department of Computer Science and Engineering Xi’an University of Technology Xi’an 710048 China Shaanxi Key Laboratory for Network Computing and Security Technology

Zero-reference low-light image enhancement (LLIE) techniques mainly focus on grey-scale inhomogeneities, and few approaches consider how to explicitly recover a given dark scene for achieving color and holistic illuminance enhancement. In this paper, we propose a novel Zero-Reference Color Self-Calibration framework for low-light image enhancement, namely Zero-CSC, that effectively accentuates channel-wise representations that contain fine-grained color information, achieving a natural result in a progressive manner. Furthermore, a novel Light Up (LU) module with large-kernel convolutional blocks is proposed to improve the holistic illuminance, which is implemented with a simple U-Net. The experiments are conducted on representative datasets, showing that our model consistently achieves SOTA performance on all datasets with the same architecture. © 2024, The Authors. All rights reserved.

关键词： Color

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Riemannian Complex Matrix Convolution network for PolSAR Image Classification

arXiv

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arXiv 2023年

作者： Shi, Junfei Wang, Wei Jin, Haiyan Nie, Mengmeng Ji, Shanshan Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China The Department of Computer Science and Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Xi’an China

Recently, deep learning methods have achieved superior performance for Polarimetric Synthetic Aperture Radar(PolSAR) image classification. Existing deep learning methods learn PolSAR data by converting the covariance matrix into a feature vector or complex-valued vector as the input. However, all these methods cannot learn the structure of complex matrix directly and destroy the channel correlation. To learn geometric structure of complex matrix, we propose a Riemannian complex matrix convolution network for PolSAR image classification in Riemannian space for the first time, which directly utilizes the complex matrix as the network input and defines the Riemannian operations to learn complex matrix’s features. The proposed Riemannian complex matrix convolution network considers PolSAR complex matrix endowed in Riemannian manifold, and defines a series of new Riemannian convolution, ReLu and LogEig operations in Riemannian space, which breaks through the Euclidean constraint of conventional networks. Then, a CNN module is appended to enhance contextual Riemannian features. Besides, a fast kernel learning method is developed for the proposed method to learn class-specific features and reduce the computation time effectively. Experiments are conducted on three sets of real PolSAR data with different bands and sensors. Experiments results demonstrates the proposed method can obtain superior performance than the state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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Deep Reinforcement-Learning-Based Adaptive Traffic Signal Control with Real-Time Queue Lengths

Deep Reinforcement-Learning-Based Adaptive Traffic Signal Co...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Sun, Qi-Wei Han, Shi-Yuan Zhou, Jin Chen, Yue-Hui Yao, Kang University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China Shandong Provincial Network Security and Infomationization Technology Center Jinan250002 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

The reinforcement learning (RL) with deep neural network, as a data-driven approach, is promising for adaptive traffic signal control (ATSC) in traffic scenarios. The majority of the existing studies focus on designing efficient agents and policy optimization for ATSC, but neglect to observe more detailed states of the environment. In this paper, an adaptive traffic signal control strategy, named as A2C RTQL, is proposed for scheduling the traffic signal in an intersection, by combining the real-time lane-based queue lengths with deep RL agent. First, the Lighthill-Whitham-Richards (LWR) shockwave theory is employed for obtaining the real-time queue lengths in each lane. After that, by defining the obtained queue lengths as the inputs, A2C RTQL strategy is designed for traffic signal control based on the advanced actor-critic (A2C) agent, where the lanes are divided into multiple parallel environments based on the phases of traffic signal. Simulation results demonstrate the optimality and efficiency of the proposed strategy compared with other methods in SUMO under simulated peak-hour traffic dynamics. © 2022 IEEE.

关键词： Reinforcement learning

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Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information

Wrong-of-Thought: An Integrated Reasoning Framework with Mul...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Yongheng Chen, Qiguang Zhou, Jingxuan Wang, Peng Si, Jiasheng Wang, Jin Lu, Wenpeng Qin, Libo School of Computer Science and Engineering Central South University China Research Center for SCIR Harbin Institute of Technology Harbin China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China Yunnan University Kunming China

ISBN: (纸本)9798891761681

Chain-of-Thought (CoT) has become a vital technique for enhancing the performance of Large Language Models (LLMs), attracting increasing attention from researchers. One stream of approaches focuses on the iterative enhancement of LLMs by continuously verifying and refining their reasoning outputs for desired quality. Despite its impressive results, this paradigm faces two critical issues: (1) Single verification method: The current paradigm relies solely on a single verification method. (2) Wrong Information Ignorance: The traditional paradigm directly ignores wrong information during reasoning and refines the logic paths from scratch each time. To address these challenges, we propose Wrong-of-Thought (WoT), which includes two core modules: (1) Multi-Perspective Verification: A multi-perspective verification method for accurately refining the reasoning process and result, and (2) Wrong Information Utilization: Utilizing wrong information to alert LLMs and reduce the probability of LLMs making same mistakes. Experiments on 8 popular datasets and 5 LLMs demonstrate that WoT surpasses all previous baselines. In addition, WoT exhibits powerful capabilities in difficult computation tasks. © 2024 Association for Computational Linguistics.

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Improved Global and Local Graph Neural network for Session-based Recommendation 25

Improved Global and Local Graph Neural Network for Session-b...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Wang, Yifan Yu, Qing Wang, Zuohua Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin China China Everbright Bank Co. Ltd Beijing China

ISBN: (纸本)9781665405270

The session-based recommendation(SBR) aims to predict user actions based on anonymous sessions. SBR focuses on recent sessions to make more real-time and reliable recommendations and does not need to extract user IDs, thereby alleviating privacy issues. Recent research mainly models user preferences based on the target session while ignoring other sessions, which may contain items transitions related to and unrelated to the target session. This paper proposes a novel method, namely Improved Global and Local Graph Neural network (IGL-GNN), to models item transitions within not only the target session but also the other sessions. Specifically, we learn the session-level item representation from the local graph, learns the global-level item representation from the global graph. We propose a new method to effectively aggregate global and local representations, which not only retains more useful information but also removes useless information. In addition, we innovatively introduced Transformer, incorporated it into the model as a general deformation function, enhanced the ability to obtain complex transformations, and solved the limitations of the model in learning complex representations. We have done a lot of experiments on three benchmark datasets, and the results show that the performance of IGL-GNN is better than the most advanced methods. © 2022 IEEE.

关键词： Graph neural networks

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DCGSD: Low-Light Image Enhancement with Dual-Conditional Guidance Sparse Diffusion Model

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Jin, Haiyan Wang, Jing Zuo, Fengyuan Su, Haonan Xiao, Zhaolin Wang, Bin Zhang, Yuanlin Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Department of Computer Science and Engineering NO. 5 South Jinhua Road Shaanxi Xi'an710048 China

When restoring low-light images, most methods largely overlook the ambiguity due to dark noise and lack discrimination for region and shape representations, resulting in invalid feature enhancement. In this work, we propose a physically explainable and prior guidance model for low-light image enhancement, termed Dual-Conditional Guidance Sparse Diffusion (DCGSD). Specifically, we introduce an elaborately designed Luminance Structure Guidance Head, which can be easily plugged into the existing diffusion model to emphasize the value of the luminance and structural representation. Furthermore, for reliable noise analysis, we provide a novel Sparse Attention Enhancement Module that is adaptively empowered to exploit the most useful region-to-region dependencies. This dynamic selection makes the diffusion process from dense to sparse, thus improving the efficiency of the reasoning noise distributions. To avoid noise amplification, we further present a Skip Calibration Module, which can be used to refine the local neighborhood that contains noisy and structural information. Extensive experiments have been performed to verify the superiority of the proposed method. DCGSD shows that leveraging dual-conditional guidance can support the diffusion model to produce sharper and more realistic results. © 1991-2012 IEEE.

关键词： Feature extraction

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BEW-YOLO: An Improved Method for PCB Defect Detection Based on YOLOv7 29

BEW-YOLO: An Improved Method for PCB Defect Detection Based ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Li, Zhiyao Li, Aimin Zhang, Yuechen Kong, Xiaotong Li, Wenqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350330717

The PCB defect object size is small and the detection process is susceptible to background interference, usually have the problem of missed and false detection. In order to solve the above problems, an improved method based on YOLOv7 is proposed in this paper. Firstly, the bi-level routing attention (BRA) has been added to the header of the original YOLOv7 model to capture global dependencies and ensure the accuracy of small object detection and localization. Secondly, the explicit visual center (EVC) block is introduced before the fusion of mid-level features and high-level features to capture the global remote dependencies of top-level features, extract the features in local corner regions, achieve a comprehensive feature representation. Finally, the loss function is improved by changing the loss function of the original model to Wise-IoU, which allows our model to focus more on ordinary-quality anchor boxes and improve the performance of the detector while decreasing the competitiveness of anchor boxes for high-quality samples and reducing the influence of low-quality samples on the detection results. The experimental results show that the improved model improves 5% in AP and 1.9% in both AR and mAP over the original YOLOv7 model. Meanwhile, comparison experiments are conducted on the data-enhanced PCB dataset, which proves the superiority of our model over other state-of-the-art models. © 2023 IEEE.

关键词： attention mechanism defect detection PCB YOLOv7

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3D visualization and detection of glomeruli in whole mouse kidney 16

3D visualization and detection of glomeruli in whole mouse k...

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16th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2023

作者： Li, Yuxin Cao, Jia Li, Anan Li, Xiangning Feng, Zhao Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi an University of Technology Xi an710048 China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology Wuhan430074 China HUST-Suzhou Institute for Brainsmatics Suzhou215123 China

ISBN: (纸本)9781510667341

The kidney is an important organ in the body to excrete metabolic waste, and the glomerulus is an essential structure for the kidney to play a role in blood filtration. Abnormal glomerular numbers are associated with nephropathy or circulatory disease. With the development of imaging technology, mesoscopic optical imaging can obtain whole kidney images at single cell resolution. The detection of glomeruli from images is very crucial for understanding the renal function and studying nephropathy. Existing detection methods cannot balance both accuracy and efficiency, so we proposed a deep learning-based glomeruli detection method. First, we imaged an entire mouse kidney with High-Definition fluorescent Micro-Optical Sectioning Tomography (HD-fMOST) and obtained a three-dimensional (3D) kidney image at cellular resolution. Then, we designed an end-To-end 3D convolutional neural network based on the morphological features of glomeruli in the kidney image, which can directly read 3D images and predict the precise coordinates of glomeruli. We used the acquired kidney dataset to train the network and validated the effect of glomeruli detection. Finally, we applied our approach to detect glomeruli in large-scale mouse kidney. The results showed that the proposed method reached the state-of-The-Art level, which is more efficient and accurate compared to similar methods. The proposed method will provide a powerful tool for kidney-related research. © 2023 SPIE.

关键词： Convolutional neural networks

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