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An Efficient Multilevel Threshold Image Segmentation Approach Based on Rafflesia Optimization Algorithm 1

18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Zhou, Jin-Liang Chu, Shu-Chuan Weng, Shaowei Zheng, Wei-Min College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Science and Engineering Flinders University Adelaide Australia Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China

ISBN: (数字)9789819901050

ISBN: (纸本)9789819901043

Segmentation is an important step from image processing to analysis. The thresholding technique based on entropy has become one of the most commonly used techniques in this field. Among the many thresholding techniques based on entropy, the minimum cross-entropy threshold (MCET) has been dramatically developed. The key to multilevel MCET technology is quickly finding the optimal threshold point. MCET is a relatively time-consuming technique, so we use optimization techniques to search for optimal multilevel thresholds. Rafflesia optimization algorithm (ROA) is an optimization algorithm inspired by rafflesia growth theory to solve real-world multi-parameter optimization problems. In this paper, Rafflesia optimization algorithm, particle swarm optimization, gray wolf optimization, whale optimization algorithm, and multi-version optimizer are used to segment six images and the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM) as evaluation metrics. The experimental results show that the ROA algorithm can achieve higher-quality image segmentation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

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E2E-MFERC:AMulti-Face Expression Recognition Model for Group Emotion Assessment

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Computers, Materials & Continua 2024年第4期79卷 1105-1135页

作者： Lin Wang Juan Zhao Hu Song Xiaolong Xu Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing210042China School of Network Security Jinling Institute of TechnologyNanjing211169China State Grid Jiangsu Electric Power Company Limited Nanjing210000China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210042China

In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assessstudents’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis,thereby continuouslypromotingthe improvementof teaching ***,most existingmulti-face expressionrecognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance,and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single faceimages, which are of low quality and lack specificity, also restricting the development of this research. This paperaims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable forsmart classrooms, construct a high-quality multi-face expression dataset to support algorithm research, and applythe model to group emotion assessment to expand its application value. To this end, we propose an end-to-endmulti-face expression recognition algorithm model for smart classrooms (E2E-MFERC). In order to provide highqualityand highly targeted data support for model research, we constructed a multi-face expression dataset inreal classrooms (MFED), containing 2,385 images and a total of 18,712 expression labels, collected from smartclassrooms. In constructing E2E-MFERC, by introducing Re-parameterization visual geometry group (RepVGG)block and symmetric positive definite convolution (SPD-Conv) modules to enhance representational capability;combined with the cross stage partial network fusion module optimized by attention mechanism (C2f_Attention),it strengthens the ability to extract key information;adopts asymptotic feature pyramid network (AFPN) featurefusion tailored to classroomscenes and optimizes the head prediction output size;achieves high-performance endto-end multi-face expression detection. Finally, we apply the model to smart classroom group emotion assessmentand provide design refe

关键词： Multi-face expression recognition smart classroom end-to-end detection group emotion assessment

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Five-user quantum virtual local area network with an AlGaAs entangled photon source

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science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 61-73页

作者： Cheng Qian Hongkai Tian Xu Jing Yichen Liu Zhe Chen Huiran Luo Yuan Du Xiaodong Zheng Tangsheng Chen Yuechan Kong Hua-Lei Yin Dong Jiang Bin Niu Liangliang Lu Key Laboratory of State Manipulation and Advanced Materials in Provincial Universities School of Physical Science and TechnologyNanjing Normal UniversityNanjing210023China School of Internet Anhui UniversityHefei230039China National Key Laboratory of Solid-State Microwave Devices and Circuits Nanjing Chip Valley Industrial Technology InstituteNanjing Electronic Devices InstituteNanjing210016China Research Center for Quantum Optics and Quantum Communication School of ScienceQingdao University of TechnologyQingdao266520China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing100872China Beijing Academy of Quantum Information Sciences Beijing100193China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei230601China National Laboratory of Solid-State Microstructures and School of Physics Nanjing UniversityNanjing210093China Hefei National Laboratory Hefei230088China

The promotion of quantum network applications demands the scalable connection of quantum *** is preferable to set up multiple logical networks coexisting on a single physical network infrastructure to accommodate a larger number of *** we present a quantum virtual network architecture that offers this level of scalability,without being constrained to a fixed physical-layer network relying solely on passive multiplexing *** architecture can be understood as arising from the superposition of a fully connected entanglement distribution network and port-based virtual local area network,which group multiusers by access *** terms of hardware,we leverage a semiconductor chip with a high figure-of-merit modal overlap to directly generate high-quality polarization entanglement,and a streamlined polarization analysis module,which requires only one single-photon detector for each end *** experimentally perform the BBM92 QKD protocol on the five-user quantum virtual network and demonstrate voice and image encryption on a campus area *** results may provide insights into the realization of large-scale quantum networks with integrated and cost-efficient photonic architecture.

关键词： quantum network quantum entanglement distribution integrated photonics image and voice encryption

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Heterogeneous-attributes enhancement deep framework for network embedding

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Frontiers of Computer science 2021年第6期15卷 121-131页

作者： Lisheng QIAO Fan ZHANG Xiaohui HUANG Kai LI Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230022China National Key Laboratory of Blind Signal Processing Chengdu 610041China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230022China

Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network ***,considering the complex structures and heterogeneous attributes in real-world networks,existing methods may fail to handle the inconsistencies between the structure topology and attribute ***,more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more *** that end,in this paper,we propose a heterogeneous-attributes enhancement deep framework(HEDF),which could better capture the non-linear structure and associated information in a deep learningway,and effectively combine the structure information of multi-views by the combining *** this line,the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised *** extensive validations on several real-world datasets show that our model could outperform the baselines,especially for the sparse and inconsistent situation with less training data.

关键词： network embedding heterogeneous-attributes deep framework inconsistent

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Collusion Resistant Identity-based Proxy Re-encryption Scheme on Lattice 6

Collusion Resistant Identity-based Proxy Re-encryption Schem...

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6th International Conference on Computer Information science and Application technology, CISAT 2023

作者： Deng, Xiaohong Xie, Hua Xiong, Weizhi School of Electronics and Information Engineering Gannan University of Science and Technology Ganzhou341000 China College of Information Science Jiangxi University of Science and Technology Ganzhou341000 China Key Laboratory of Cloud Computing and Big Data Ganzhou341000 China

ISBN: (纸本)9781510668546

Proxy re-encryption plays an important role for data security in cloud computing and big data, but the traditional reencryption scheme based on the classical number theory problem cannot resist quantum attack, and the lattice based cryptosystem has been proved to have the ability to resist quantum attack, so the lattice based re-encryption scheme has always been a research hotspot. In view of the problem that collusion-resistance and multi-hop can not be satisfied at the same time in existing schemes, this paper proposes a new identity-based proxy re-encryption scheme on lattice. Firstly, the scheme uses trapdoors as private keys to achieve one-way anti-collusion. In addition, we constructs a variant of learning with errors (LWE), which uses a convert algorithm to transform the user matrix and replace the vector with a uniform matrix, so that the scheme can meet the requirements of multi-hop and encrypt multiple bits of messages at one time. Theoretical analysis and experimental simulation prove that the security of the new scheme can be reduced to a decision LWE problem, which can reach the IND-sID-CPA security in the standard model, and can improve the encryption efficiency while ensuring the security. © 2023 SPIE.

关键词： Cryptography

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Hierarchical Alignment-enhanced Adaptive Grounding Network for Generalized Referring Expression Comprehension 39

Hierarchical Alignment-enhanced Adaptive Grounding Network f...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Yaxian Ding, Henghui He, Shuting Jiang, Xudong Wei, Bifan Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University China Institute of Big Data Fudan University China Shanghai University of Finance and Economics China Nanyang Technological University Singapore School of Continuing Education Xi'an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University China

ISBN: (纸本)157735897X

In this work, we address the challenging task of Generalized Referring Expression Comprehension (GREC). Compared to the classic Referring Expression Comprehension (REC) that focuses on single-target expressions, GREC extends the scope to a more practical setting by further encompassing no-target and multi-target expressions. Existing REC methods face challenges in handling the complex cases encountered in GREC, primarily due to their fixed output and limitations in multi-modal representations. To address these issues, we propose a Hierarchical Alignment-enhanced Adaptive Grounding Network (HieA2G) for GREC, which can flexibly deal with various types of referring expressions. First, a Hierarchical Multi-modal Semantic Alignment (HMSA) module is proposed to incorporate three levels of alignments, including word-object, phrase-object, and text-image alignment. It enables hierarchical cross-modal interactions across multiple levels to achieve comprehensive and robust multi-modal understanding, greatly enhancing grounding ability for complex cases. Then, to address the varying number of target objects in GREC, we introduce an Adaptive Grounding Counter (AGC) to dynamically determine the number of output targets. Additionally, an auxiliary contrastive loss is employed in AGC to enhance object-counting ability by pulling in multi-modal features with the same counting and pushing away those with different counting. Extensive experimental results show that HieA2G achieves new state-of-the-art performance on the challenging GREC task and also the other 4 tasks, including REC, Phrase Grounding, Referring Expression Segmentation (RES), and Generalized Referring Expression Segmentation (GRES), demonstrating the remarkable superiority and generalizability of the proposed HieA2G. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Modal analysis

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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Hepatocyte Nuclear Factor 4α Transcriptionally Activates TM4SF5 Through The DR1 Motif

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生物化学与生物物理进展 2025年第5期52卷 1241-1251页

作者： GUO Yi-Ming ZHANG Xiao-Fei FENG Han ZHENG Li Key Laboratory of RNA Biology Center for Big Data Research in HealthInstitute of BiophysicsChinese Academy of SciencesBeijing 100101China Key Laboratory of Molecular Biophysics of the Ministry of Education College of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Objective Hepatocyte nuclear factor 4-alpha(HNF4A)is a critical transcription factor in the liver and *** of HNF4A lead to maturity onset diabetes of the young 1(MODY1).Notably,MODY1 patients with HNF4A pathogenic mutations exhibit decreased responses to arginine and reduced plasma triglyceride levels,but the mechanisms remain *** study aims to investigate the potential target genes transcriptionally regulated by HNF4A and explore its role in these metabolic *** A stable 293T cell line expressing the HNF1A reporter was overexpressed with *** sequencing(RNA-seq)was performed to analyze transcriptional *** factor binding site prediction was then conducted to identify HNF4A binding motifs in the promoter regions of relevant target *** RNA-seq results revealed a significant upregulation of transmembrane 4 L six family member 5(TM4SF5)mRNA in HNF4A-overexpressing *** factor binding predictions suggested the presence of five potential HNF4A binding motifs in the TM4SF5 ***,we confirmed that the DR1 site in the-57 to-48 region of the TM4SF5 promoter is the key binding motif for *** This study identified TM4SF5 as a target gene of HNF4A and determined the key binding motif involved in its *** the role of TM4SF5 as an arginine sensor in mTOR signaling activation and triglyceride secretion,which closely aligns with phenotypes observed in MODY1 patients,our findings provide novel insights into the possible mechanisms by which HNF4A regulates triglyceride secretion in the liver and arginine-stimulated insulin secretion in the pancreas.

关键词： HNF4A RNA-seq TM4SF5 DR1 motif MODY1

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CITE: Compact Interactive TransformEr for Multilingual Image Captioning 23

CITE: Compact Interactive TransformEr for Multilingual Image...

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6th International Conference on Image and Graphics Processing, ICIGP 2023

作者： Xu, Yueyuan Hu, Zhenzhen Zhou, Yuanen Hao, Shijie Hong, Richang Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781450398572

Current state-of-the-art image captioning models generate captions in a single language, requiring a combination of multiple language specific models to build a multilingual image captioning system. However, as the number of supported languages increases, it leads to the parameters of the multilingual image captioning system grow linearly. To tackle this issue, we propose a single Compact Interactive TransformEr (CITE) model, which can describe an image in multiple languages simultaneously, making the captioning system more compact. Specifically, based on the standard Transformer model, we share the encoder and decoder backbone parameters and replace the self-attention sub-layer in the decoder with the interactive attention sub-layer. In addition, we extend the traditional monolingual reinforcement learning mechanism to a multilingual version to promote better description generation. Due to the wide use of Chinese and English, we evaluate the performance of our CITE model by simultaneously generating English and Chinese captions. We expand the image captions of the whole MSCOCO dataset and release a COCO-EN-CN dataset. Extensive experiments on the COCO-EN-CN dataset show that our single CITE model with more parameter-efficient can maintain the competitive performance or even better than the monolingual captioning models. © 2023 ACM.

关键词： Reinforcement learning

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Sparrow Search Algorithm with Adaptive Collaborative Updating

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1706-1724页

作者： Hong, Chuanbo Liang, Quan Liang, Qiaoxin Yu, Wenjie Yu, Wenze Xiong, Neng School of Mechanical and Electrical Engineering Quanzhou University of Information Engineering Quanzhou362000 China College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China Quanzhou Management Branch Fujian Expressway Group Cp. Ltd Quanzhou362000 China

The research of swarm intelligence shows a rapid development trend. People have studied a lot of swarm intelligence algorithms inspired by biology. Sparrow search algorithm is concerned by many scholars because of its powerful search performance. However, in the case of high-dimension optimization problems, if only a single sparrow search algorithm is used to solve optimization problems, the optimal solution often cannot be found, and the phenomenon of dimension disaster will occur or it is easy to fall into the local optimal solution. In order to make sparrow search algorithm have better performance in solving high-dimensional complex problems, In this paper, we propose a new Sparrow Search Algorithm with Adaptive Collaborative Updating (SSA-ACU). The SSA-ACU algorithm initializes the population through the chaotic search strategy to make the population distribution more diverse. Then, adaptive parameters are introduced to update the position of the leader to provide a more reliable direction for particle search. Finally, in order to enhance the development ability of the population, the position of the adjoint is updated through the pseudo-affine transformation mechanism to change the mode of its position update. Then CEC2013 was used to test and compare SSA-ACU algorithm with other sparrow search algorithm variants and classical algorithms, and it was verified that SSA-ACU algorithm has good performance in convergence speed and optimization ability. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Swarm intelligence

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