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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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Real-Time Routing Design for LEO Satellite Networks: An Enhanced Multi-Agent DRL Approach

Real-Time Routing Design for LEO Satellite Networks: An Enha...

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IEEE International Conference on communications in China Workshops (ICCC)

作者： Hailong Liao Xian Zhang Jiaen Zhou Xuehua Li Key Laboratory of Information and Communication Systems Ministry of Information Industry and the Key Laboratory of Modern Measurement & Control Technology School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Low Earth Orbit (LEO) satellite networks have been heralded as a promising solution for provisioning space-based Internet services. However, the inherent dynamic topology, characterized by frequent variations in inter-satellite link states, causes pronounced fluctuations in latency and exacerbated packet loss rates. To address the challenge, in this paper, we formulate a routing design and optimization problem to maximize the average throughput with considering the end-to-end latency constraint and the real-time routing demand in LEO satellite networks. To solve this problem, we design an enhanced Multi-Agent Deep Reinforcement Learning (MADRL) algorithm, which encapsulates the intricate interplay among LEO satellite agents and their environs within a Real-Time Markov Decision Process (RTMDP) framework, and introduces the self-attention mech-anism to endow agents with a better grasp of environmental state dynamics. Simulation results demonstrate that our proposed routing design outperforms the benchmark schemes in terms of both end-to-end latency and network average throughput.

关键词： Satellites Network topology Simulation Heuristic algorithms Low earth orbit satellites Routing Throughput Real-time systems Topology Optimization

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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Incoherent spectral imaging based on Bessel beams 2

Incoherent spectral imaging based on Bessel beams

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2nd Advanced Imaging and information Processing Conference, AIIP 2024

作者： Cheng, Xukun Yu, Xuelian Wang, Zhengxian Yin, Hao Zhao, Lihang Li, Jia Niu, Jia Heilongjiang Provincial Key Laboratory of Quantum Control School of Measurement and Communication Engineering Harbin University of Science and Technology Harbin150080 China Shanxi Key Laboratory of Quantum Information and Quantum Optoelectronic Devices School of Physics Xi’an Jiaotong University Xi’an710049 China Daheng New Epoch Technology Inc. Beijing100085 China

ISBN: (纸本)9781510683068

Spectral resolution is crucial for achieving precise and high-quality imaging and analysis in imaging systems. Coded aperture correlation holography system face challenges in distinguishing objects at different wavelengths due to their limited spectral resolution. This paper presents a method that combines Bessel beams with a lens phase mask to encode an object's spatial and spectral information into a hologram. Reconstruction is achieved by cross-correlating with the point spread hologram for each wavelength, enabling precise imaging under multi-wavelength conditions. Simulation results show that this method only requires recording a single object hologram at the same wavelength, eliminating the need for multiple recordings. It also offers high spectral sensitivity and spectral resolution. This method is expected to further advance the application of spectral imaging technology in fields such as high-resolution imaging and microscopic endoscopy. © 2024 SPIE.

关键词： Holograms

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Bit-Level Composite Signal Design for Simultaneous Ranging and communication

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China communications 2021年第6期18卷 214-227页

作者： Weigang Chen Yalong He Changcai Han Jinsheng Yang Zhan Xu School of Microelectronics Tianjin UniversityTianjin 300072China Key Laboratory of Modern Measurement and Control Technology Beijing 100192China School of Information and Communication Engineering Beijing Information Science and Technology UniversityBeijing 100192China

As the complexity of space exploration missions augments,how to enhance the overall performance of communication,ranging or other functions has become a challengeable *** the integration of communication and ranging,we present a bit-level composite signal for simultaneous ranging and *** this composite method,through a specially designed mapping scheme using low-weight codewords,the information sequence is converted to a sparse sequence which is then superimposed on the ranging *** ranging,the correlation characteristics of the ranging code component can be maintained to calculate the transmitter-receiver *** communications,the sparse sequence can be extracted without interference by eliminating the ranging code *** results show that the proposed composite signal can support communication and ranging simultaneously with limited sacrifice of ranging performance,and the performance loss of ranging can be controlled and minimized by lowering the density of information sequences using different sparsification encoding methods.

关键词： composite signal spectrum sharing lowweight codeword regenerative PN ranging code

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Twitter Bot Detection with Multi-Head Attention and Supervised Contrastive Learning 21

Twitter Bot Detection with Multi-Head Attention and Supervis...

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21st International Computer Conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2024

作者： Qiang, Liu Jiazhong, Lu Yuanyuan, Huang Weisha, Zhang Jun, Lv School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China SUGON Industrial Control and Security Center Chengdu610225 China School of Foreign Languages University of Electronic Science and Technology of China Chengdu611731 China Co. Ltd. China

ISBN: (纸本)9798331519254

The task of detecting bots on Twitter is an indispensable task for combating social network disinformation, curbing rumors, and managing online public opinion. However, today's social bots often disguise themselves by mimicking various aspects of real user characteristics to escape detection, which poses an unprecedented challenge to the existing Twitter bot detection task. To address this, an innovative detection method is presented in this study that improves the accuracy of Twitter bot detection by realizing the effective fusion of multi-dimensional features through the multi-head attention mechanism and applying it to a relational graph convolutional network. It also combines supervised contrastive learning to strengthen the differentiation ability of the high-latitude features. Experiments conducted on the publicly available dataset Twibot-20, compared to the baseline, the proposed method achieves approximately 1% better results in both Accuracy and the Matthews Correlation Coefficient, proving its effectiveness. © 2024 IEEE.

关键词： Contrastive Learning

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Helmet wearing detection based on YOLOv5 3

Helmet wearing detection based on YOLOv5

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Wu, Chao Wang, De-Yong Shi, Wen-Xi Fang, Jian Zhao, Xue-Yi Fu, Yan-Yun School of Information Science and Engineering Xinjiang University Urumqi830000 China XinJiang Lianhai INA-INT Information Technology Co. Ltd. Urumqi830000 China Key Laboratory of Big Data of Xinjiang Social Security Risk Prevention and Control Urumqi830000 China Beijing Academy of Science and Technology Beijing100035 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Complex environments, such as dense personnel and background interference, affect the detection accuracy of whether personnel wear helmets. To solve this problem, a new detection algorithm for helmets based on YOLOv5 is studied in this paper. Firstly, RepVGGblock is used to replace the common convolution in the network to effectively utilize the computing power of GPU. Secondly, the efficient channel attention mechanism is incorporated into the C3 module of the backbone network to enhance the feature identification ability of the backbone network for the hard hat. Then, the boundary box regression function is changed to SIoU to redefine the distance loss and improve the regression accuracy. Finally, the detection performance of multi-scale targets is improved by adding detection layers. The test results on the self-made safety helmet data set show that the average accuracy of the improved YOLOv5 model reaches 94.6%, which is 4.1% higher than that of the original model, and can meet the requirements of target detection. © 2023 SPIE.

关键词： Computing power

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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Cooperative Local Path Planning for Multi-unmanned Vehicles Formation Using the Enhanced Artificial Potential Field 3rd

Cooperative Local Path Planning for Multi-unmanned Vehicles...

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3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Zhang, Xin Duan, Xuting Zhou, Jianshan Zhang, Long Opening Project of Key Laboratory of Operation Safety Technology on Transport Vehicles Ministry of Transport PRC Beijing China School of Transportation Science and Engineering Beihang University Beijing China State Key Lab of Intelligent Transportation System Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing China Zhongguancun Laboratory Beijing China National Key Laboratory of Science and Technology on Information System Security Institute of Systems Engineering Beijing China

ISBN: (纸本)9789819711024

An enhanced approach utilizing APF(artificial potential field) method is introduced in this paper. By adopting this approach, the challenge of local minima that may occur when dealing with local path planning for unmanned vehicles is tackled. When the unmanned vehicle approaches an obstacle, a virtual attractive potential field is implemented, utilizing the deviation in the heading angle of the vehicle to control the steering angle. Consequently, the vehicle can avoid obstacles, resolving the issue of local minima and enabling its application in path planning for multi-unmanned vehicles. The ROS with Gazebo simulation platform is used to verify the enhanced algorithm’s effectiveness in multi-unmanned vehicle path planning in an unknown environment. © Beijing HIWING Scientific and Technological information Institute 2024.

关键词： Motion planning

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Research on NER model for coal mine safety hazards based on BERT-CNN-BiGRUs-CRF 5

Research on NER model for coal mine safety hazards based on ...

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5th International Conference on Artificial Intelligence and Computer science, AICS 2023

作者： Ma, Li Yang, Fan Dai, Xinguan Gao, Hangbiao Song, Shuang College of Communication and Information Engineering Xi’an University of Science and Technology Shaanxi Xi’an710054 China Xi’an Key Laboratory of Heterogeneous Network Convergence Communication Xi’an710054 China Safety Supervision Division of Shaanxi Cuijiagou Energy Co. Ltd. Shaanxi Tongchuan727000 China College of Security Science and Engineering Xi’an University of Science and Technology Shaanxi Xi’an710054 China

ISBN: (纸本)9781510668621

During the coal mine safety production process, a significant amount of text data containing information about coal mine safety hazards, such as working face, hazard location, hazard subject, and hazard problem description, is accumulated. The extraction of named entities from coal mine safety hazard text serves as the foundation for conducting a study on the early detection of coal mine safety hazards. Since the single modeling strategy is being used, the current named entity recognition (NER) model technique has a low recognition precision and ratio. Firstly, a character-level encoding of coal mine safety hazard text is performed by a BERT pre-training language model to generate word vectors based on contextual information, followed by local and global deep feature extraction of coal mine safety hazard word vectors by a convolutional neural network (CNN) with multi-layer bi-directional gated recurrent neural networks (BiGRUs), and finally decoding by conditional random fields (CRF) to generate global optimal label. On tasks of the NER for coal mine safety hazards, by comparing and analyzing with the mainstream deep learning entity recognition models, As shown by the outcomes that the precision of the NER model for coal mine safety hazards proposed in this paper reaches 91.74%, the recall reaches 93.20%, and the F1-measure reaches 92.45%, which shows a better performance. The NER task of precisely obtaining key information such as hazard location and hazard subject from unstructured coal mine safety hazards text data is achieved, which provides important information for hazard investigation and management. © 2023 SPIE.

关键词： Extraction

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