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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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CONIC10K: A Challenging Math Problem Understanding and Reasoning Dataset

arXiv

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arXiv 2023年

作者： Wu, Haoyi Hui, Wenyang Chen, Yezeng Wu, Weiqi Tu, Kewei Zhou, Yi School of Information Science and Technology ShanghaiTech University China Shanghai Engineering Research Center of Intelligent Vision and Imaging China School of Information Science and Technology University of Science and Technology of China China National Engineering Laboratory for Brain-inspired Intelligence Technology and Application China Key Laboratory of Brain Cognition and Education Sciences Ministry of Education China Shanghai Innovation Center for Processor Technologies China Department of Computer Science and Engineering Shanghai Jiao Tong University China

Mathematical understanding and reasoning are crucial tasks for assessing the capabilities of artificial intelligence (AI). However, existing benchmarks either require just a few steps of reasoning, or only contain a small amount of data in one specific topic, making it hard to analyse AI's behaviour with reference to different problems within a specific topic in detail. In this work, we propose CONIC10K, a challenging math problem dataset on conic sections in Chinese senior high school education. Our dataset contains various problems with different reasoning depths, while only the knowledge from conic sections is required. Since the dataset only involves a narrow range of knowledge, it is easy to separately analyse the knowledge a model possesses and the reasoning ability it has. For each problem, we provide a high-quality formal representation, the reasoning steps, and the final solution. Experiments show that existing large language models, including GPT-4, exhibit weak performance on complex reasoning. We hope that our findings could inspire more advanced techniques for precise natural language understanding and reasoning. Our dataset and codes are available at https://***/whyNLP/Conic10K. © 2023, CC BY.

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DGLNet: A Cross-Modal Global-Local Collaborative Network for Chest Radiology Report Generation

SSRN

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SSRN 2022年

作者： Liu, Lijun Wang, Lizhen Liu, Li Peng, Wei Fu, Xiaodong Huang, Qingsong Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Department of Computer Science and Engineering School of Information Science and Engineering Yunnan University Kunming650228 China Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China

Automatic chest radiology report generation is crucial for improving diagnostic efficiency, enhancing diagnostic quality, and reducing misdiagnoses or missed diagnoses. Existing approaches either resize the high-resolution radiology image into smaller sizes or segment it into multiple patches for separate processing, which may lose fine local details or global contextual information, and the accuracy of report generation will be limited. In addition, the direct fusion of visual features with sentence features reduces the accuracy of aligning fine-grained visual features with text features. In this paper, we focus on designing a cross-modal global-local collaborative network to capture more local fine details and global contextual information, which comprises a global-to-local network and a local-to-global network that takes the resized entire image and segmented image patches as respective inputs. The former handles the extraction of global visual features, the fusion of global and local visual features, and the fusion of visual features into report sentences. The latter implements the extraction of local visual features, the fusion of local to global visual features, and the fusion of visual features to report words. Experimental results on two publicly available datasets, IU X-Ray and MIMIC-CXR, show that our proposed network outperforms state-of-the-art image captioning-based methods in most evaluation metrics. © 2022, The Authors. All rights reserved.

关键词： Extraction

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VCSE: Time-Domain Visual-Contextual Speaker Extraction Network

arXiv

引用

arXiv 2022年

作者： Li, Junjie Ge, Meng Pan, Zexu Wang, Longbiao Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Department of Electrical and Computer Engineering National University of Singapore Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan

Speaker extraction seeks to extract the target speech in a multi-talker scenario given an auxiliary reference. Such reference can be auditory, i.e., a pre-recorded speech, visual, i.e., lip movements, or contextual, i.e., phonetic sequence. References in different modalities provide distinct and complementary information that could be fused to form top-down attention on the target speaker. Previous studies have introduced visual and contextual modalities in a single model. In this paper, we propose a two-stage time-domain visual-contextual speaker extraction network named VCSE, which incorporates visual and self-enrolled contextual cues stage by stage to take full advantage of every modality. In the first stage, we pre-extract a target speech with visual cues and estimate the underlying phonetic sequence. In the second stage, we refine the pre-extracted target speech with the self-enrolled contextual cues. Experimental results on the real-world Lip Reading Sentences 3 (LRS3) database demonstrate that our proposed VCSE network consistently outperforms other state-of-the-art baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction

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A Software Defect Prediction Method Based on Learnable Three-Line Hybrid Feature Fusion

SSRN

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SSRN 2023年

作者： Tang, Yu Dai, Qi Du, Ye Chen, Lifang Niu, Xuanwen School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Automation College of Information Science and Engineering China University of Petroleum-Beijing Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100044 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Key Laboratory of Data Science and Application Hebei Tangshan063210 China

Software defect prediction plays a crucial role in ensuring the security and quality of software systems. However, it faces challenges posed by high-dimensional features present in software defect datasets and the limited effectiveness of traditional nonlinear dimensionality reduction methods in extracting essential feature information. To address these issues, we propose a novel approach called learnable three-line hybrid feature fusion (LTHFFA), which incorporates the principle of three-line hybrid breeding into feature fusion for the first time. In this method, three distinct dimensionality reduction techniques are initially employed to obtain three separate sets of features. Subsequently, a learnable weight factor feature fusion method is proposed to facilitate automatic learning and dynamic adjustment of feature weights. By integrating the three feature sets based on the principle of three-line hybrid breeding, we derive learnable three-line hybrid fusion features. These features are then utilized in the context of software defect prediction. Experimental results demonstrate the superior performance of LTHFFA compared to nine other dimensionality reduction methods across seventeen publicly available software defect datasets. LTHFFA exhibits the ability to effectively integrate multiple feature sets, reduce feature redundancy, and enhance predictive accuracy. Moreover, statistical analysis using Friedman ranking and Holm's post-hoc test confirms the significant advantage of LTHFFA over alternative dimensionality reduction methods. © 2023, The Authors. All rights reserved.

关键词： Defects

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Iterative Sound Source Localization for Unknown Number of Sources

arXiv

引用

arXiv 2022年

作者： Fu, Yanjie Ge, Meng Yin, Haoran Qian, Xinyuan Wang, Longbiao Zhang, Gaoyan Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Department of Electrical and Computer Engineering National University of Singapore Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan

Sound source localization aims to seek the direction of arrival (DOA) of all sound sources from the observed multichannel audio. For the practical problem of unknown number of sources, existing localization algorithms attempt to predict a likelihood-based coding (i.e., spatial spectrum) and employ a pre-determined threshold to detect the source number and corresponding DOA value. However, these threshold-based algorithms are not stable since they are limited by the careful choice of threshold. To address this problem, we propose an iterative sound source localization approach called ISSL, which can iteratively extract each source's DOA without threshold until the termination criterion is met. Unlike threshold-based algorithms, ISSL designs an active source detector network based on binary classifier to accept residual spatial spectrum and decide whether to stop the iteration. By doing so, our ISSL can deal with an arbitrary number of sources, even more than the number of sources seen during the training stage. The experimental results show that our ISSL achieves significant performance improvements in both DOA estimation and source number detection compared with the existing threshold-based algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Direction of arrival

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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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Research on Fast Adaptive Transmission Models for International Inland Port Based on Edge Intelligence

Research on Fast Adaptive Transmission Models for Internatio...

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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Liu Yiwen Zhu Zhirong Tangyan Wen Wenkan Peng Xiaoning Shi Yuanquan School of Computer and Artificial Intelligence Huaihua University Huaihua Hunan P. R. China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Application in Hunan Province Universities Huaihua Hunan P. R. China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan P. R. China

In this paper, we propose a logistics International Inland Port resource allocation method based on intelligent edge scheduling, and we do a reasonable pre-allocation of logistics resources by building an edge network in the urban system. By using multiple integrated learning models and regional prevalence classification algorithms, the distribution demand of each sub-distribution point is predicted. The proposed method is able to cope with uncertainties such as high distribution volume, variable distribution situations or long transportation distances. We use the HuaiHua International Inland Port as the simulation object, and the simulation results show that the proposed method has the highest efficiency in logistics distribution and is still highly adaptive in emergency situations.

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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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Continuous-variable measurement-device-independent quantum key distribution with passive state in oceanic turbulence

Continuous-variable measurement-device-independent quantum k...

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International Symposium on Distributed Computing and applications to Business, Engineering and Science (DCABES)

作者： Guojun Chen Jianmin Yi Ying Guo Jiangsu Key Laboratory of IoT Application Technology Wuxi Taihu University Wuxi China College of Computer Internet of Tings Engineering Wuxi Taihu University Wuxi China School of Automation Central South University Changsha China

ISBN: (纸本)9781665454636

Quantum communication, as an absolutely secure communication method, has been widely studied in recent years. While CVQKD is easier to realize and has efficient information transmission, has attracted more attention than DVQKD. And there is another kind of detection strategy which places the detector on a third party called continuous-variable measurement device independent quantum key distribution(CV -MDI), can lower the cost of implementation because it don't need such a high precision Gaussian modulator and thus can be commercialized easily. We apply a CV -MDI with passive-state preparation protocol to a oceanic turbulence model, and then we analyze the secret key rate and other performance of the protocol in the particular scenario.

关键词： Underwater communication Protocols Quantum computing System performance Oceans Sea measurements Modulation

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