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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Xu Chen, Zhikui Ren, Hao Jin, Shan Tian, Yang The Dalian University of Technology School of Software Technology International School of Information Science and Engineering Dalian China

ISBN: (纸本)9798350386226

Multi-modal data feature fusion can effectively improve the accuracy of primary modal pattern recognition and address the issue of missing data through multi-modal collaboration. To some extent, supplementing multi-view information can alleviate the conflict between medical images' reliance on professional annotations and the limited number of annotations. However, there are still several challenges in engineering multimodal feature perception and fusion for medical images, including imbalanced patterns that make it challenging to establish a weighted allocation criterion suitable for different tasks, misleading factors not positively correlated with knowledge richness, and complexities associated with integrating modalities having diverse formats for text and time series data into overall feature engineering and similarity. This paper introduces a multi-modal semantic fusion model that is sensitive to key information represented by contextual cues. Furthermore, it incorporates a dynamic feature engineering scheme capable of acquiring and integrating important insights from three perspectives with an optimized structure. Additionally, it presents a novel similarity evaluation criteria and fusion mode that adaptively adjust weights based on their contributions, facilitating the measurement of target object category likelihoods across various modalities for weighted analyses. The system combines LSTM and CNN models to builda sequence and local information sensitive structure designed to integrate heterogeneous features while embedding the multimodal similarity fusion. Emphasizing small yet compelling pieces of information such as contextual cues that prompt medical representation assignment to the target category, our system places significant importance on specificity removal and accuracy enhancement, exemplified by introducing contextual information demonstrating our sensitivity to contextual cues despite our non-specifically structured approach. © 2024 IEEE.

关键词： Image annotation

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Interfacial engineering for efficient and stable two-terminal perovskite-based tandem solar cells

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Rare Metals 2024年第11期43卷 5573-5624页

作者： Ying Li Di-Sheng Yao Fei Long Guo-Zhen Zhang Feng Yu Guangxi Key Laboratory of Optical and Electronic Material and Devices School of Materials Science and EngineeringGuilin University of TechnologyGuilin 541004China Guangxi Key Laboratory of Optical and Electronic Material and Devices School of Materials Science and EngineeringCollaborative Innovation Center for Exploration of Nonferrous Metal Deposits and Efficient Utilization of ResourcesGuilin University of TechnologyGuilin 541004China School of Chemistry and Materials Science Nanjing University of Information Science and TechnologyNanjing 210044China

Two-terminal(2T)tandem solar cells(TSCs)are optically and electrically connected by two subcells with complementary bandgaps,which are designed to overcome the Shockley-Queisser(S-Q)limit of singlejunction solar ***-inorganic hybrid perovskites are ideal light-absorbing materials for 2T TSCs due to their tunable bandgaps,low-temperature solution-based processing,and excellent light absorption ***,2T perovskite-based TSCs(PTSCs)have aroused widespread interest among the photovoltaic *** present,the key to obtaining efficient and stable 2T PTSCs is establishing efficient interfaces and layers with good photoelectric properties and high compatibility of *** particular,interfacial engineering based on effective recombination layers(RCLs)and buffers has a prominent effect on achieving enhanced power conversion efficiency(PCE)of 2T PTSCs with improved operational *** this article,the current frontier issues of 2T PTSCs including different device structures and properties are reviewed in detail to analyze their merits,demerits and solutions to overcome ***,the component engineering,interface engineering and theoretical PCE analysis for designing 2T PTSCs proposed by material simulations are ***,the scalability of interfacial passivation from single-junction perovskite solar cells to 2T PTSCs is evaluated,and the function mechanisms of RCLs and buffers are also summarized and analyzed ***,the challenges faced by 2T PTSCs are pointed out,and their developme nt directions are *** article aims to provide viable guidance for realizing practical manufacturing technologies for the commercialization of 2T PTSCs.

关键词： Two-terminal tandem solar cells Organic-inorganic perovskites Passivation interfaces Recombination layers and buffers Efficiency and stability

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PBFuzz: Potential-aware Branch-oriented Hybrid Fuzzing 4

PBFuzz: Potential-aware Branch-oriented Hybrid Fuzzing

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4th International Symposium on Computer technology and information science, ISCTIS 2024

作者： Fang, Wenyu Yang, Xia Guo, Wensheng Sun, Haiyong Li, Qiming Lin, Zhongping University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Chengdu Lianan Tech Chengdu China

ISBN: (纸本)9798350354560

Hybrid fuzzing integrates fuzzing and concolic execution to detect software vulnerabilities. It utilizes the rapid exploration of fuzzing and the constraint-solving capabilities of concolic execution. However, current hybrid fuzzing techniques face challenges in coordinating fuzzing seeds with concolic execution inputs, particularly in identifying promising seeds. These methods frequently struggle to efficiently prioritize seeds from the fuzzing seed queue for reaching critical code areas, leading to concolic execution spending significant resources on inputs with minimal coverage improvements. To address these issues, we introduce a Potential-aware Branch-oriented Hybrid Fuzzing approach (PBHF) to enhance testing efficacy. PBHF synchronizes seeds between hybrid fuzzing and concolic execution using a branch-oriented scheduling strategy. We propose a lightweight branch utility evaluation algorithm to assess seed utility based on branch exploration potential. We developed a prototype of PBHF, named PBFuzz, which integrates AFL and QSYM. Experimental results show that PBFuzz achieves superior branch coverage in vulnerability detection compared to existing methods. These findings validate the efficacy and superiority of our approach. © 2024 IEEE.

关键词： Program diagnostics

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Graph Contrastive Learning with Adversarial Structure Refinement (GCL-ASR) 24

Graph Contrastive Learning with Adversarial Structure Refine...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Chen, Jiangwen Guang, Kou Yan, Huang Li, Qiyang Hao, Tan Defense Innovation Institute Beijing China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331506681

The scarcity of labeled data in graph neural networks (GNNs) has driven the development of graph contrastive learning (GCL), which has become the most widely used method in unsupervised representation learning. At present, many GCL approaches in the literature utilize random dropout nodes, edges, and masking features to enhance the two views and subsequently enhance consistency between these views. Although this approach is commendable, it fails to address issues such as unequal edge distribution and the varying sensitivity of node training in different augmented views. Given the complex nature of graph data structures, this paper proposes a new GCL framework called Graph Contrastive Learning with Adversarial Structure Refinement (ASR), which explicitly accounts for the graph topology. Specifically, we employ Generative Adversarial Networks (GANs) as a view augmentation strategy to generate high-quality views. Additionally, we introduce a novel training method that incorporates node sensitivity as a regularization term to optimize the GCL training process. We conduct extensive experiments on eight benchmark datasets for node classification, and the results demonstrate that our method has achieved outstanding performance. © 2024 IEEE.

关键词： Contrastive Learning

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Image Representations of Numerical Simulations for Training Neural Networks

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Computer Modeling in engineering & sciences 2023年第2期134卷 821-833页

作者： Yiming Zhang Zhiran Gao Xueya Wang Qi Liu School of Civil and Transportation Engineering Hebei University of TechnologyTianjin300401China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China

A large amount of data can partly assure good fitting quality for the trained neural *** the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engineering practice,numerical simulations can provide a large amount of controlled high quality *** the neural networks are trained by such data,they can be used for predicting the properties/responses of the engineering objects instantly,saving the further computing efforts of simulation ***,a strategy for efficiently transferring the input and output data used and obtained in numerical simulations to neural networks is desirable for engineers and *** this work,we proposed a simple image representation strategy of numerical simulations,where the input and output data are all represented by *** temporal and spatial information is kept and the data are greatly *** addition,the results are readable for not only computers but also human *** examples are given,indicating the effectiveness of the proposed strategy.

关键词： Numerical simulations neural network pre-/post-processing data compression

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Mutli-focus image fusion based on guided filter and image matting network

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Multimedia Tools and Applications 2025年第3期84卷 1239-1259页

作者： Zhu, Puchao Li, Xue Wang, Puming Jin, Xin Yao, Shaowen School of Software Yunnan University East Outer Ring Road Chenggong District Yunnan Kunming650091 China School of Electronic Information Engineering Henan Institute of Technology Xinxiang453003 China

The problem of limited depth-of-field is one of the major disadvantages in optical imaging devices, whereas multifocus image fusion(MFIF), as an enhancement technique, can effectively solve this situation. The purpose of MFIF method is to fuse the different focus regions from the multifocus image pair so as to obtain a full-clear fusion result. However, the inaccurate decision maps in fusion methods often leads to blurred object boundary or artifact. To solve this problem, we propose a novel method combining guided filter and deep image matting network for multifocus image fusion tasks. The first step is to utilize guided filter to generate high-frequency information of multifocus image pairs, as to get the initial decision maps based on pixel-level maximum comparison. Aiming for the better fusion images, these decision maps need to be further refined. Then, the trimaps corresponding to the initial decision images are gained via the basic morphological operations, and we utilize the deep image matting network to refine the uncertain regions of trimaps, which can effectively reduce the misclassified pixels and yield the final decision maps with less noise. Finally, the fusion results are constructed by using the final decision maps. This approach effectively distinguishes between focused and non-focused areas, preserving the complete and sharp contour information of objects in the fused images. The experiments illustrate the superior visual effect and objective evaluation on public datasets are achieved by the designed fusion method compared with the advanced fusion methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image fusion

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Organic-2D composite material-based RRAM with high reliability for mimicking synaptic behavior

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Journal of Materiomics 2024年第2期10卷 440-447页

作者： Tangyou Sun Fantao Yu Xiaosheng Tang Haiou Li Fabi Zhang Zhimou Xu Qing Liao Zhiqiang Yu Xingpeng Liu Peihua Wangyang Hezhang Li Ying Peng Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic TechnologyGuilin541004China College of Optoelectronic Engineering Chongqing UniversityChongqing400044China School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan430074China School of Electrical and Information Engineering Guangxi University of Science and TechnologyLiuzhou545006China Department of Precision Instrument Tsinghua UniversityBeijing100084China

The field of artificial intelligence and neural computing has been rapidly expanding due to the imple-mentation of resistive random-access memory(RRAM)based artificial ***,the low flexibility of conventional RRAM materials hinders their ability to mimic synaptic behavior *** overcome such limitation,organic-2D composites with high mechanical properties are proposed as the active layer of ***,we enhance the reliability of the device by ZrO_(2)insertion layer,resulting in stable synaptic *** Ag/PVA:h-BN/ZrO_(2)/ITO devices show stable bipolar resistive switching behavior with an ON/OFF ratio of over 5×10^(2),a~2400 cycles endurance and a long retention time(>6×10^(3)s),which are essential for the development of high-performance *** also study the possible synaptic mechanism and dynamic plasticity of the memory device,observing the transition from short-term potentiation(STP)to long-term potentiation(LTP)under the effect of continuous voltage ***,the device exhibits both long-term depression(LTD)and paired-pulse facili-tation(PPF)properties,which have significant implications for the design of organic-2D composite material RRAMs that aim to mimic biological synapses,representing promising avenues for the devel-opment of advanced neuromorphic computing systems.

关键词： Resistive random-access memory Synaptic mimicking Organic-2D composite material Neuromorphic computing

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A G-banded Chromosome Image Enhancement Algorithm Based on B-Spline Curves

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Signal, Image and Video Processing 2025年第8期19卷 1-10页

作者： Wang, Hui Feng, Shengri Yu, Tiantian School of Electronic Science and Engineering Hunan University of Information Technology Changsha410151 China Zoomlion Heavy Industry Science & Technology Co. Ltd. Changsha410013 China

To address the issue that the low contrast of human G-banded metaphase chromosome optical microscopic images hinders chromosome karyotype analysis, a chromosome band region enhancement algorithm based on B-spline curves is proposed. The method integrates a multi-level threshold segmentation algorithm, exhaustively searches for optimal control point parameters by maximizing joint similarity as the optimization objective, and constructs a grayscale mapping table using cubic clamped B-spline curves. Experimental results, with peak signal-to-noise ratio as the cross-validation index, demonstrate that the proposed algorithm achieves enhancement effects closer to the benchmark reference image compared to traditional gamma transformation and contrast-limited adaptive histogram equalization. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image enhancement

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Valley polarization in transition metal dichalcogenide layered semiconductors:Generation,relaxation,manipulation and transport

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Chinese Physics B 2023年第10期32卷 1-14页

作者：马惠朱耀杰刘宇伦白瑞雪张喜林任琰博蒋崇云 School of Physical Science and Technology Tiangong UniversityTianjin 300387China College of Electronic Information and Optical Engineering Nankai UniversityTianjin 300350China

In recent years,valleytronics researches based on 2D semiconducting transition metal dichalcogenides have attracted considerable *** the one hand,strong spin–orbit interaction allows the presence of spin–valley coupling in this system,which provides spin addressable valley degrees of freedom for information storage and *** the other hand,large exciton binding energy up to hundreds of me V enables excitons to be stable carriers of valley *** polarization,marked by an imbalanced exciton population in two inequivalent valleys(+K and-K),is the core of valleytronics as it can be utilized to store binary *** by the potential applications,we present a thorough overview of the recent advancements in the generation,relaxation,manipulation,and transport of the valley polarization in nonmagnetic transition metal dichalcogenide layered *** also discuss the development of valleytronic devices and future challenges in this field.

关键词： valley polarization nonmagnetic transition metal dichalcogenide layered semiconductors exciton

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Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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Computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

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