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International Conference on Display technology, 2024

作者： Li, Xiangjun Li, Kejing Lv, Guoqiang Wang, Zi Feng, Qibin School of Instrument Science and Opto-Electronics Engineering Hefei University of Technology AnHui Hefei China Special Display and Imaging Technology Innovation Center of Anhui Province National Engineering Laboratory of Special Display Technology Academy of Photoelectric Technology Hefei University of Technology AnHui Hefei China

With the requirements of green development, reflective LCDs are gaining more and more applications. When reflective LCDs are used in dark environment, the display luminance cannot meet the requirement that make front light modules necessary. As the fact that front light module does not have optical films as used in backlight module, the light guide plate is directly observed by human eyes, which makes the performance of the light guide plate have profound influence on the final display. This paper firstly establishes a simulation model of the front light module. The spherical and conical dots are taken as examples to explore the influence of dot shape and parameter changes on the display performance. A kind of analysis and evaluation method of the front light module by comprehensively considering the factors of luminance, uniformity and stray light is put forward in the paper. The simulation results show that in the case of R=25μm, the spherical dots have higher comprehensive evaluation parameters at R:H=3:1, and the conical dots have higher comprehensive evaluation parameters at R:H=1:1. The analysis and evaluation method proposed in this paper can effectively guide the development of front light guides, shorten the development time and reduce the development risk. © 2024 John Wiley and Sons Inc. All rights reserved.

关键词： Liquid crystals

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On the Adversarial Robustness of Hierarchical Classification

On the Adversarial Robustness of Hierarchical Classification

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

作者： Wang, Ran Zeng, Simeng Wu, Wenhui Jia, Yuheng Ng, Wing W. Y. Wang, Xizhao School of Mathematical Sciences Shenzhen University Shenzhen518060 China College of Electronic and Information Engineering Shenzhen University Shenzhen518060 China School of Computer Science and Engineering Southeast University Nanjing211189 China School of Computer Science and Engineering South China University of Technology Guangzhou510006 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781665410205

Deep neural networks (DNNs) have demonstrated remarkable success on various learning problems, but they face a formidable challenge in the form of adversarial attacks. Especially, when dealing with complex classification tasks for numerous classes with a hierarchical structure, the adversarial robustness of a DNN model may drop seriously. In this paper, we investigate the adversarial robustness of DNN models on such complex classification tasks. In response, we propose a two-stage hierarchical classification framework, which is composed of a coarse-grained classifier and a series of fine-grained classifiers. A data correction sampling module is designed between the two stages, in order to mitigate the influence of misclassification caused by the coarse-grained classifier;and a discriminative filter learning module is employed in the fine-grained classification, in order to gain better distinguish abilities among fine-grained categories. Experiments on the well-known dataset CIFAR-100 and a newly-constructed hierarchical dataset mini-ImageNet76 demonstrate that employing a hierarchical framework can effectively improve the model robustness on such complex classification tasks. © 2024 IEEE.

关键词： Generative adversarial networks

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Analysis of CPI Film on Bnding Performance of Flexible OLED Panels

Analysis of CPI Film on Bnding Performance of Flexible OLED ...

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International Conference on Display technology, 2024

作者： Zheng, Chen Dong, Zhe Feng, Qibin Wang, Zi Lv, Guoqiang School of Instrument Science and Opto-Electronics Engineering Hefei University of Technology Anhui Hefei China Special Display and Imaging Technology Innovation Center of Anhui Province National Engineering Laboratory of Special Display Technology Academy of Photoelectric Technology Hefei University of Technology Anhui Hefei China

Flexible OLED displays have become a development trend. CPI film in a flexible panel can improve the bending performance of the panel. However, CPI film is prone to plastic deformation in the bending process, which will affect the reliability and service life of OLED panel. Finite element analysis is employed in this paper to analyze how CPI film affects the bending performance of a flexible OLED panel. The simulation results show that the optimization of the structure of water-drop-shaped bending can improve the plastic deformation of CPI film. The reasonable position of CPI film in the stacked structure can also improve its plastic deformation. The increase of the thickness of the OCA located below the CPI film can significantly reduce its plastic deformation. The analysis method used in this paper can help improve the bending performance of a flexible OLED panel. © 2024 John Wiley and Sons Inc. All rights reserved.

关键词： Plastic deformation

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STGAT: Spatial-Temporal Graph Attention Networks for Traffic Flow Prediction 29

STGAT: Spatial-Temporal Graph Attention Networks for Traffic...

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

作者： Ruan, Chang Tan, Xianchao Liao, Zhuofan Kuang, Lidan Li, Ping Changsha University of Science & Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798350330717

Accurate traffic flow prediction is of great importance in Intelligent Transportation System (ITS) for improving traffic efficiency, reducing congestion and so on. However, due to the complex spatial and temporal dependencies, achieving the accurate prediction is challenging. Traditional attention-based networks for traffic flow prediction typically use sine and cosine functions to do position encoding, which fail to capture the spatial and temporal dependencies and do not contain the graph structure information. In this paper, we propose a novel model named Spatial-Temporal Graph ATtention networks (STGAT), which leverages structure-aware self-attention mechanism to predict future traffic flow. The model introduces temporal multi-head self-attention modules, and designs spatial multi-head graph self-attention modules with structure-aware graph filters to extract more temporal and spatial information. Besides, our model adopts temporal and spatial position embedding layers to capture the spatial-temporal dependencies in traffic flow data. Experimental results show that STGAT shows better prediction performance than the state-of-art models on three real-world datasets PEMS04, PEMS07 and PEMS08. For example, we observe up to 10.38% improvement in terms of Mean Absolute Percentage Error (MAPE) compared with the well-known model ASTGNN. © 2023 IEEE.

关键词： graph attention network self-attention spatial-temporal Traffic flow prediction

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Effective In-Context Learning for Named Entity Recognition

Effective In-Context Learning for Named Entity Recognition

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Jin Guo, Qian Liang, Jiaqing Li, Zhixu Xiao, Yanghua Fudan University Shanghai Key Laboratory of Data Science School of Computer Science Shanghai China Fuyang Normal University College of Information Engineering Anhui China Renmin University of China School of Information Suzhou China

ISBN: (纸本)9798350386226

Named entity recognition aims to identify meaningful words or phrases from text. Traditional NER methods depend heavily on training datasets to learn entity features, which often leads to poor performance in recognizing low-resource entities. Large language models offer a promising solution in low-resource scenarios, but they struggle with entity boundary recognition and often suffer from hallucination issues due to their reliance on general-purpose corpora. In this paper, we propose a novel approach that enhances LLMs' performance in NER by integrating a boundary regularity extraction algorithm and an explanatory path extraction method. The boundary regularity algorithm leverages ChatGPT's summarization capabilities to articulate entity boundaries in natural language, improving boundary detection accuracy. The explanatory path method constructs logical reasoning steps from text to entity recognition, guiding LLMs to generate more accurate predictions and mitigating hallucination problems. Extensive experiments on three public datasets demonstrate that our method outperforms current mainstream LLMs in accurately recognizing entities from unstructured text. These findings validate the effectiveness of our approach and its potential to address key challenges in NER. © 2024 IEEE.

关键词： Large language models Low-resource Named entity recognition

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Machine Learning Based Phishing Website Detection System - Natural Language Processing Approach 2

Machine Learning Based Phishing Website Detection System - N...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Patni, Jagdish Chandra Naik, S. Vinay Harika, K. Bahadure, Nilesh Bhaskarrao Verma, Kamal Kant Dept. of CSE Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering School of Technology GSFC University Gujarat Vadodara India School of Computer Science IILM University Greater Noida India

ISBN: (纸本)9798331530389

Natural language processing (NLP) methods can be used to identify phishing websites in addition to static and dynamic features. Phishing sites frequently include certain phrases, misspellings, or misleading text patterns in their material, as well as other visual cues and language that match those of authentic websites. Through the examination of textual components such anchor texts, meta-descriptions, and homepage titles. The proposed model is able to comprehend the linguistic traits that are frequently seen in phishing websites using NLP-based features like sentiment analysis, keyword matching, and phrase frequency to increase model's sensitivity. In addition, we use real-time URL parsing to detect possible dangers as users come across them, guaranteeing prompt notifications and lowering the possibility that phishing attempts would be successful. Our study emphasizes how crucial it is to keep improving and upgrading phishing detection models because attackers usually change their strategies. © 2025 IEEE.

关键词： Phishing

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Object Detection with Augmented Reality 21

Object Detection with Augmented Reality

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21st OITS International Conference on Information technology, OCIT 2023

作者： Tyagi, Arnav Rajpal, Dev David, Annie Singh, Jagendra Thakur, Hardeo Kumar Upreti, Kamal Bennett University School of Computer Science Engineering and Technology Greater Noida India Christ University Department of Computer Science Ghaziabad India

ISBN: (纸本)9798350358230

This study describes an artificial intelligence (AI)-based object identification system for detecting real-world items and superimposing digital information in Augmented Reality (AR) settings. The system evaluates the camera stream from an AR device for real-Time recognition using deep learning algorithms trained on a collection of real-world items and their related digital information. Object recognition applications in AR include gaming, education, and marketing, which provide immersive experiences, interactive learning, and better product presentations, respectively. However, challenges such as acquiring larger and more diverse datasets, developing robust deep learning algorithms for varying conditions, and optimizing performance on resource-constrained devices remain. The AI-based object recognition system demonstrates the potential to transform AR experiences across domains, while emphasizing the need for ongoing research and development to fully realize its capabilities. © 2023 IEEE.

关键词： Augmented reality

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Adversarial Attacks on Facial Images Based on Attribute-Conditioned High-Camouflage Editing 1

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14th International Conference on Brain Inspired Cognitive Systems, BICS 2024

作者： Zhang, Jingjing Wang, Huabin Shang, Dongxu Yuan, Hongrui Tao, Liang Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (数字)9789819628827

ISBN: (纸本)9789819628810

Research on adversarial attacks for facial recognition identifies and mitigates vulnerabilities in facial recognition systems, enhancing their security. Current adversarial attacks targeting facial attributes lack precision in controlling specific attributes, often altering multiple facial features instead of a single target attribute. Such imprecise modifications reduce the attack’s effectiveness and stealthiness. To address these issues, this paper proposes a method for generating adversarial examples based on multi-layer feature map fusion. First, we design a framework called StarAdv, which uses fused multi-layer feature maps with high concealment to achieve realistic facial image transformations under different attributes. This approach generates adversarial images visually similar to the originals while possessing specific misleading properties, enabling transferable attacks on facial recognition systems. Second, we introduce a Multi-layer Fusion module that captures weight information from each layer of the network’s residual blocks to adaptively fuse feature maps from different layers, producing high-concealment feature maps. Finally, we propose a Bilinear Feature Map Interpolation (BFMI) algorithm to interpolate the high-concealment features with the original image features, ensuring the final decoded adversarial samples maintain a natural appearance, further reducing the likelihood of detection. We conduct comprehensive experiments on the CelebA dataset, demonstrating that the adversarial facial images generated by our method possess semantic plausibility and authenticity in appearance and achieve a high attack success rate in both white-box and black-box settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Photointerpretation

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Retrieval Across Any Domains via Large-scale Pre-trained Model 41

Retrieval Across Any Domains via Large-scale Pre-trained Mod...

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41st International Conference on Machine Learning, ICML 2024

作者： Yan, Jiexi Yin, Zhihui Xu, Chenghao Deng, Cheng Huang, Heng School of Computer Science and Technology Xidian University Shaanxi Xi'an China School of Electronic Engineering Xidian University Shaanxi Xi'an China Department of Computer Science University of Maryland College Park United States

In order to enhance the generalization ability towards unseen domains, universal cross-domain image retrieval methods require a training dataset encompassing diverse domains, which is costly to assemble. Given this constraint, we introduce a novel problem of data-free adaptive cross-domain retrieval, eliminating the need for real images during training. Towards this goal, we propose a novel Text-driven Knowledge Integration (TKI) method, which exclusively utilizes a pre-trained vision-language model to implement an "aggregation after expansion" training strategy. Specifically, we extract diverse implicit domain-specific information through a set of learnable domain word vectors. Subsequently, a domain-agnostic universal projection, equipped with a non-Euclidean multi-layer perceptron, can be optimized using these assorted text descriptions through the text-proxied domain aggregation. Leveraging the cross-modal transferability phenomenon of the shared latent space, we can integrate the trained domain-agnostic universal projection with the pre-trained visual encoder to extract the features of the input image for the following retrieval during testing. Extensive experimental results on several benchmark datasets demonstrate the superiority of our method. Copyright 2024 by the author(s)

关键词： Visual languages

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Highly dispersed 1 nm Pt Pd bimetallic clusters for formic acid electrooxidation through a CO-free mechanism

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Journal of Energy Chemistry 2023年第3期78卷 554-564,I0015页

作者： Zhongying Fang Ziwei Zhang Shemsu Ligani Fereja Jinhan Guo Xinjie Tong Yue Zheng Rupeng Liu Xiaolong Liang Leting Zhang Zongjun Li Wei Chen State Key Laboratory of Electroanalytical Chemistry Changchun Institute of Applied ChemistryChinese Academy of SciencesChangchun 130022JilinChina School of Applied Chemistry and Engineering University of Science and Technology of ChinaHefei 230026AnhuiChina School of Material Science and Technology Jilin Institute of Chemical TechnologyJilin 132022JilinChina School of Chemistry and Pharmaceutical Sciences Guangxi Normal UniversityGuilin 541004GuangxiChina

Direct formic acid fuel cell(DFAFC) is an important research project in clean energy ***,commercialization of DFAFC is still largely limited by the available catalysts with unsatisfied activity,durability and cost for formic acid electrooxidation(FAEO).Using Pt-and Pd-based nanoclusters as electrocatalysts is a particularly promising strategy to solve the above problem,but two attendant problems need to be solved firstly.(Ⅰ) The controllable synthesis of practicable and stable sub-2 nm clusters remains challenging.(Ⅱ) The catalyzing mechanism of sub-2 nm metal clusters for FAEO has not yet completely ***,different from traditional solution synthesis,by designing a novel supporting material containing electron-rich and electron-deficient functional groups,size-and dispersioncontrollable synthesis of ~1 nm PtPd nanoclusters is realized by an electrochemical *** electrocatalytic properties and reaction mechanism of the PtPd nanoclusters for the FAEO were studied by different electrochemical techniques,in-situ fourier transform infrared(FTIR) spectra and density functional theory(DFT) *** tiny PtPd nanoclusters have much higher catalytic activity and durability than commercial Pt/C,Pd/C and 3.5 nm PtPd *** present study shows that the metalreactant interaction plays a decisive role in determining the catalytic activity and cluster-support interaction plays a decisive role in enhancing the durability of *** ratio and arrangement of Pt and Pd atoms on the surface of 1 nm PtPd cluster as well as the overall valence state,d-band center and specific surface area make them exhibit different catalytic performance and reaction mechanism from nanoparticle *** addition,in situ FTIR and DFT calculations showed that on the surface of PtPd clusters,the generation of CO_(2)through trans-COOH intermediate is the most optimal reaction pathway for the FAEO.

关键词： Cluster Formic acid oxidation Electrocatalysis In situ FTIR Reaction mechanism

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