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International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Yulong Cai Xiaopan Chen Qi Hu Lingyun Dong Caihong Yuan School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Segmentation of rat heart section images and infarct area quantization are essential components of infarct size measurement in processing data from animal experiments on ischemic heart disease. Previously, the task of measuring the infarct size of rat heart sections was performed manually by experienced researchers, which was very costly and time-consuming. In this paper, we propose a two-stage semi-supervised approach to solve the task of ischemic heart infarct size calculation in images of rat section images. The approach uses a semi-supervised semantic segmentation model and a semi-automated quantization algorithm to separate the differently stained regions of rat heart sections. We demonstrate the results of the approach on a rat heart section dataset, and experiments on this dataset emphasize the effectiveness of our approach compared to other methods.

关键词： Heart Training Quantization (signal) Semantic segmentation Manuals Rats Size measurement

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Quiz-based Knowledge Tracing

arXiv

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arXiv 2023年

作者： Shen, Shuanghong Chen, Enhong Xu, Bihan Liu, Qi Huang, Zhenya Zhu, Linbo Su, Yu Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science School of Computer Science and Techonology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Anhui Hefei230026 China Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science School of Computer Science and Techonology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230026 China School of Computer Science and Technology Hefei Normal University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230026 China

Knowledge tracing (KT) aims to assess individuals’ evolving knowledge states according to their learning interactions with different exercises in online learning systems (OIS), which is critical in supporting decision-making for subsequent intelligent services, such as personalized learning source recommendation. Existing researchers have broadly studied KT and developed many effective methods. However, most of them assume that students’ historical interactions are uniformly distributed in a continuous sequence, ignoring the fact that actual interaction sequences are organized based on a series of quizzes with clear boundaries, where interactions within a quiz are consecutively completed, but interactions across different quizzes are discrete and may be spaced over days. In this paper, we present the Quiz-based Knowledge Tracing (QKT) model to monitor students’ knowledge states according to their quiz-based learning interactions. Specifically, as students’ interactions within a quiz are continuous and have the same or similar knowledge concepts, we design the adjacent gate followed by a global average pooling layer to capture the intra-quiz short-term knowledge influence. Then, as various quizzes tend to focus on different knowledge concepts, we respectively measure the inter-quiz knowledge substitution by the gated recurrent unit and the inter-quiz knowledge complementarity by the self-attentive encoder with a novel recency-aware attention mechanism. Finally, we integrate the inter-quiz long-term knowledge substitution and complementarity across different quizzes to output students’ evolving knowledge states. Extensive experimental results on three public real-world datasets demonstrate that QKT achieves state-of-the-art performance compared to existing methods. Further analyses confirm that QKT is promising in designing more effective quizzes. Copyright © 2023, The Authors. All rights reserved.

关键词： Students

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Lightweight Transformer Network and Self-supervised Task for Kinship Verification

Lightweight Transformer Network and Self-supervised Task for...

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International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Yunwei Li Danyang Li Lingyun Dong Xiaopan Chen School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Kinship verification is one of the interesting and critical problems in computer vision research, with significant progress in the past decades. Meanwhile, Vision Transformer (VIT) has recently achieved impressive success in many domains, including object detection, image recognition, and semantic segmentation, among others. Most of the previous work on kinship verification are based on convolutional or recurrent neural networks. Compared with the local processing of images like convolutions, transformers can effectively understand and process images globally. However, due to overuse, there are many Transformer layers of fully connected layers, and VIT speed is still an issue. Therefore, in this paper, inspired by the recent success of Transformer models in vision tasks, we propose a Transformer-based kinship verification for training and optimizing kinship verification models. We first train the basic vision transformer (VIT-B) with 12 transformer layers, then we reduce the transformer layers to 6 layers, namely VIT-S (Small Vit) and 4 layers, namely VIT-T (Tiny Vit), to make a tradeoff between optimization accuracy and efficiency. As the first attempt to apply Transformer to the kinship verification task, it provides a feasible strategy for kinship research topics and verifies the effectiveness of the method in terms of the accuracy of the experimental results.

关键词： Training Knowledge engineering Recurrent neural networks Semantic segmentation Self-supervised learning Object detection Transformers

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Label Incorporated Graph Neural Networks for Text Classification

Label Incorporated Graph Neural Networks for Text Classifica...

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International Conference on Pattern Recognition

作者： Yuan Xint Linli Xu Junliang Guo Jiquan Li Xin Sheng Yuanyuan Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China IFLYTEK Co. Ltd. Information Science Center University of Science and Technology of China

Graph Neural Networks (GNNs) have achieved great success on graph-structured data, and their applications on traditional data structures such as natural language processing and semi-supervised text classification have been extensively explored in recent years. While previous works only consider the text information while building the graph, heterogeneous information such as labels is ignored. In this paper, we consider incorporating the label information while building the graph by adding text-label-text paths, through which the supervision information will propagate among the graph more directly. Specifically, we treat labels as nodes in the graph which also contains text and word nodes, and then connect labels with texts belonging to that label. Through graph convolutions, label embeddings are jointly learned with text embeddings in the same latent semantic space. The newly incorporated label nodes will facilitate learning more accurate text embeddings by introducing the label information, and thus benefit the downstream text classification tasks. Extensive results on several benchmark datasets show that the proposed framework outperforms baseline methods by a significant margin.

关键词： Text categorization Buildings Semantics Benchmark testing data structures Graph neural networks Natural language processing

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Point-Level and Set-Level Deep Representation Learning for Cross-Modal Person Re-identification

Point-Level and Set-Level Deep Representation Learning for C...

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International Conference on Computer and Communications (ICCC)

作者： Jihui Hu Pengfei Ye Danyang Li Lingyun Dong Xiaopan Chen Xiaoke Zhu Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University

In practice, significant modality differences usually exist between visible and infrared images, which makes visible-infrared Person Re-Identification (VI-ReID) a challenging research task. Due to the existing influence of pose variation, background changes, and occlusion, there are often outlier samples within the set of images from the same person. These outlier samples can adversely affect the process of learning the cross-modal matching model. The existing VI-ReID methods mainly focus on learning cross-modal feature representation by using image-level discriminant constraints, i.e., the distance between the truly-matching cross-modal images should be smaller than that between wrong-matching cross-modal images. However, most of these methods ignore the adverse influence caused by outliers. To solve the above problems, we proposed a Point-level and Set-level Deep Representation Learning (PSDRL) approach for VI-ReID in this paper. By using the set-level constraint in the process of deep representation learning, the discrepancy between visible and infrared modalities can be decreased, and the adverse effect of outliers can be weakened. By employing the image-level constraint, the discriminability of the obtained deep feature representations can be improved. Extensive experiments are conducted on the publicly available cross-modal Person Re-Identification datasets, including SYSU-MM01 and RegDB. Experimental results demonstrate the effectiveness of the proposed approach.

关键词： Representation learning Training Image color analysis Computational modeling Task analysis

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Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing

arXiv

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arXiv 2024年

作者： He, Liyang Chen, Enhong Huang, Zhenya Wang, Fei Wang, Shijin Liu, Jiayu Sha, Jing School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China State Key Laboratory of Cognitive Intelligence Hefei China School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China iFLYTEK AI Research State Key Laboratory of Cognitive Intelligence Hefei China

Unsupervised semantic hashing has emerged as an indispensable technique for fast image search, which aims to convert images into binary hash codes without relying on labels. Recent advancements in the field demonstrate that employing large-scale backbones (e.g., ViT) in unsupervised semantic hashing models can yield substantial improvements. However, the inference delay has become increasingly difficult to overlook. Knowledge distillation provides a means for practical model compression to alleviate this delay. Nevertheless, the prevailing knowledge distillation approaches are not explicitly designed for semantic hashing. They ignore the unique search paradigm of semantic hashing, the inherent necessities of the distillation process, and the property of hash codes. In this paper, we propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method, specifically devised for the distillation of semantic hashing models. To ensure the effectiveness of two kinds of search paradigms in the context of semantic hashing, BRCD first aligns the semantic spaces between the teacher and student models through a contrastive knowledge distillation objective. Additionally, to eliminate noisy augmentations and ensure robust optimization, a cluster-based method within the knowledge distillation process is introduced. Furthermore, through a bit-level analysis, we uncover the presence of redundancy bits resulting from the bit independence property. To mitigate these effects, we introduce a bit mask mechanism in our knowledge distillation objective. Finally, extensive experiments not only showcase the noteworthy performance of our BRCD method in comparison to other knowledge distillation methods but also substantiate the generality of our methods across diverse semantic hashing models and backbones. The code for BRCD is available at https://***/hly1998/BRCD. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Temporal Transformation Network Based On Scale Sequences for Cloth-Changing Person Re-Identification in Video datasets

Temporal Transformation Network Based On Scale Sequences for...

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International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Bo Zhao Zhiwei Dong Lingyun Dong Danyang Li School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

This paper introduces the Temporal Transform Network based on Scale Sequences (TTS) for cloth-changing person re-identification in video datasets. The TTS network is designed to capture multi-scale temporal cues within video sequences. It accomplishes this by initially modeling short-term temporal cues between adjacent frames, followed by capturing long-term relationships between non-consecutive frames. In more detail, short-term temporal cues are modeled through parallel inflated convolutions with different time dilation rates, enabling the representation of pedestrian movement and appearance dynamics. Long-term relationships are effectively captured using a temporal self-attention model, mitigating challenges such as occlusion and noise within the video sequence. The TTS network outperforms existing methods across cloth-changing video ReID datasets such as CCVID. For instance, under general settings, our approach exhibits a 1.1% improvement in top-1 accuracy and a corresponding 1.1% increase in mAP compared to the baseline. In cloth-changing settings, we observe a 0.2% enhancement in top-1 accuracy and a notable 1.3% increase in mAP relative to the baseline.

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Spatiotemporal distribution and inter-media transfer of polycyclic aromatic hydrocarbons in Shanghai, China: Historical patterns and future trends

引用

Science of the Total Environment 2024年 945卷 173938页

作者： Li, Ting Nie, Ning Miao, Yiyi Zhao, Dengzhong Liu, Min Key Laboratory of Geographic Information Science of the Ministry of Education School of Geographic Sciences East China Normal University Shanghai200241 China Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources Shanghai200241 China Changjiang River Scientific Research Institute Wuhan430010 China

Polycyclic Aromatic Hydrocarbons (PAHs) represent pervasive pollutants, posing health risks in urban environments. It is essential to comprehend the spatiotemporal distributions, composition profiles, and inter-media transfer processes of PAHs in various environmental compartments, influenced by both natural changes and anthropogenic activities. This study integrates historical and future spatiotemporally changing environmental parameters, including climate data, GDP, population data, land-use types, and hydrological variables, into the Multimedia Urban Model (MUM). This integration enables the simulation of spatiotemporal distributions and inter-media transfer fluxes of PAHs among six different media from the 2010s to the 2100s under two distinct Shared Socio-economic Pathways (SSP) scenarios in the megacity of Shanghai, China. The MUM model, featuring diverse gridded parameters, effectively captures PAH concentrations and movement across environmental compartments. Results indicate a decreasing trend in PAHs concentrations in the 2100s compared to the 2010s, with PAH concentrations in water, sediment, vegetation, and organic film covering impermeable surfaces under the SSP3–7.0 scenario higher than those of the SSP1–2.6 scenario. Low molecular weight PAHs dominate in the sediment, water, and air, whereas high molecular weight PAHs prevail in the organic film, vegetation, and soil. Sediment and soil serve as the predominant sinks for PAHs. The primary transport processes for PAH movement include air-film, air-soil, film-water, soil-air, and water-air. Almost all transfer fluxes exhibit a declining trend in future periods except for the air-film transport pathway. The principal input and removal routes for PAHs in Shanghai involve the advection of air and water. The study provides essential insights into the environmental behavior of PAHs and informs targeted pollution control in Shanghai. Additionally, it serves as a technical reference for similar pollution predic

关键词： Soils

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Oriented Object Detection in Aerial Images with Double Midpoint Vectors

Oriented Object Detection in Aerial Images with Double Midpo...

引用

International Conference on Computer and Communications (ICCC)

作者： Xiaoke Zhu Qi Hu Xiaopan Chen Caihong Yuan Minghao Li School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

In recent years, oriented object detection technology has made significant progress. Most oriented object detectors represent oriented bounding boxes by adding additional angle information to the horizontal bounding box representation, which leads to serious angle periodicity issues. To address these issues, this paper proposes double midpoint vectors (DMvectors) to represent oriented objects. For any oriented bounding box, we align the center point with the origin in the Cartesian coordinate system, and evenly distribute the midpoints of the box edges across the four quadrants. We use the midpoint of the first and second quadrants, as well as the width and height of the box, to represent the oriented bounding box. DMvectors solve the periodic problem of angles by implicitly including angle information in vectors. To demonstrate the effectiveness of our proposed method, we conducted extensive experiments on two common remote sensing datasets (DOTA and HRSC2016), and our method achieved competitive results.

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FEDGT: FEDERATED NODE CLASSIFICATION WITH SCALABLE GRAPH TRANSFORMER

arXiv

引用

arXiv 2024年

作者： Zhang, Zaixi Hu, Qingyong Yu, Yang Gao, Weibo Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Hong Kong University of Science and Technology Hong Kong

Graphs are widely used to model relational data. As graphs are getting larger and larger in real-world scenarios, there is a trend to store and compute subgraphs in multiple local systems. For example, recently proposed subgraph federated learning methods train Graph Neural Networks (GNNs) distributively on local subgraphs and aggregate GNN parameters with a central server. However, existing methods have the following limitations: (1) The links between local subgraphs are missing in subgraph federated learning. This could severely damage the performance of GNNs that follow message-passing paradigms to update node/edge features. (2) Most existing methods overlook the subgraph heterogeneity issue, brought by subgraphs being from different parts of the whole graph. To address the aforementioned challenges, we propose a scalable Federated Graph Transformer (FedGT) in the paper. Firstly, we design a hybrid attention scheme to reduce the complexity of the Graph Transformer to linear while ensuring a global receptive field with theoretical bounds. Specifically, each node attends to the sampled local neighbors and a set of curated global nodes to learn both local and global information and be robust to missing links. The global nodes are dynamically updated during training with an online clustering algorithm to capture the data distribution of the corresponding local subgraph. Secondly, FedGT computes clients' similarity based on the aligned global nodes with optimal transport. The similarity is then used to perform weighted averaging for personalized aggregation, which well addresses the data heterogeneity problem. Finally, extensive experimental results on 6 datasets and 2 subgraph settings demonstrate the superiority of FedGT. © 2024, CC BY.

关键词： Graph neural networks

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