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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haonan Miao Peizheng Zhao Yuqi Sun Fang Nan Xiaolong Zhang Yaqiang Wu Feng Tian School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students’ gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods.

关键词： Three-dimensional displays Estimation Focusing Signal processing Acoustics Speech processing

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A Baseline for Early Classification of Time Series in An Open World

A Baseline for Early Classification of Time Series in An Ope...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Junwei Lv Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Early classification of time series aims to accurately predict the class label of a time series as early as possible, which is significant but challenging in many time-sensitive applications. Existing early classification methods hold a basic closed-world assumption that the classifier must have seen the classes of test samples. However, new samples that do not belong to any trained class may appear in the real world. In this paper, we first address the early classification in an open world and design two detectors to identify which known class or unknown class a sample belongs to. Specifically, based on the observed data, an early known-class detector is designed to determine the known-class confidence and an early unknown-class detector is designed to determine the unknown-class confidence according to the Minimum Reliable Length (MRL) and the Weibull distribution of each class. Experimental results evaluated on real-world datasets demonstrate that the proposed model can identify samples of unknown and known classes accurately and early.

关键词： Computational modeling Conferences Time series analysis Detectors Reliability engineering Software Weibull distribution

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Look Around Before Locating: Considering Content and Structure Information for Visual Grounding 39

Look Around Before Locating: Considering Content and Structu...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zheng, Shiyi Zhao, Peizhi Zheng, Zhilong He, Peihang Cheng, Haonan Cai, Yi Huang, Qingbao School of Electrical Engineering Guangxi University Nanning China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Key Laboratory of Big Data and Intelligent Robot of Ministry of Education SCUT Guangzhou China Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning China

ISBN: (纸本)157735897X

As a long-term challenge and fundamental requirement in vision and language tasks, visual grounding aims to localize a target referred by a natural language query. The regional annotations form a superficial correlation between the subject of expression and some common visual entities, which hinder models from comprehending the linguistic content and structure. However, current one-stage methods struggle to uniformly model the visual and linguistic structure due to the structural gap between continuous image patches and discrete text tokens. In this paper, we propose a semi-structured reasoning framework for visual grounding to gradually comprehend the linguistic content and structure. Specifically, we devise a cross-modal content alignment module to effectively align unlabeled contextual information into a stable semantic space corrected by token-level prior knowledge obtained with CLIP. A multi-branch modulated localization module is also established to obtain modulation grounding by linguistic structure. Through a soft split mechanism, our method can destructure the expression into a fixed semi-structure (i.e., subject and context) while ensuring the completeness of linguistic content. Our method is thus capable of building a semi-structured reasoning system to effectively comprehend the linguistic content and structure by content alignment and structure modulated grounding. Experimental results on five widely-used datasets validate the performance improvements of our proposed method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantics

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Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection 41

Size-Invariance Matters: Rethinking Metrics and Losses for I...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright 2024 by the author(s)

关键词： Object detection

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Optimization of Gene Translation Using SD Complementary Sequences and Double Codons 11th

Optimization of Gene Translation Using SD Complementary Sequ...

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11th International Conference on Computer engineering and Networks, CENet2021

作者： Liang, Dingfa Huang, Zhumian Zheng, Liufeng Ye, Yuannong Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical Engineering School of Biology and Engineering Guizhou Medical University Guiyang550025 China Bioinformatics and Biomedical Big Data Mining Laboratory Department of Medical Informatics School of Big Health Guizhou Medical University Guiyang550025 China Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of Education Guizhou Medical University Guiyang550025 China

ISBN: (纸本)9789811665530

Studies have confirmed that the growth, development and maintenance structure and function of all living organisms are determined by their genes, but the speed of gene translation products is affected by various internal and external factors. Therefore, studying the gene translation speed is extremely important for production and improving the speed through a variety of ways has always been an important subject. In recent years, the found of codon usage bias, tRNA recycling model and SD sequence-like structure provide another way to optimize the translation efficiency. These methods are almost based on codon usage theory to optimize gene sequence to speed up or slow down the translation speed so as to regulate the cell expression and metabolism. This paper is combined the three theories above, aimed at *** to design some reasonable algorithm to optimize gene sequence and thus using codon adaptation index (CAI) to evaluate the optimized effect. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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Isothiocyanate Extracted from Moringa oleifera Improves Insulin Resistance of C2C12 Myotube Cells

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Journal of Chinese Institute of Food Science and Technology 2023年第1期23卷 32-40页

作者： Mao, Jiaying Bai, Yuying Peng, Linjie Xie, Jing Tian, Yang College of Food Science and Technology Yunnan Agricultural University Kunming650201 China Yunnan Key Laboratory of Biological Big Data Kunming650201 China Development and Utilization of Food and Drug Homology Resources Engineering Center Ministry of Education Kunming650201 China

Objective: A C2C12 myotube cell insulin resistance (C2C12-IR) model was established to explore the effect of isothiocyanate extracted from Moringa oleifera (MIC-1) on C2C12 myotube cell insulin resistance. Methods: Palmitic acid (PA) was used to induce C2C12 myotube cells to establish a stable C2C12-IR model, and the cells were treated with different concentrations of MIC-1 (0, 2, 4, 8, 16 μmol/L) for 16 h. The cell viability of C2C12-IR cells was detected by MTT assay, and the effect of MIC-1 on glucose metabolism of C2C12-IR cells was observed. Cellular oxidative damage [nitric oxide (NO), malondialdehyde (MDA) and glutathione peroxidase (GSH)] were tested. The expression levels of related proteins in the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway were detected by using Western blot assay. Results: The optimal modeling conditions for the C2C12-IR model were determined, that is, C2C12 myotube cells were treated with 0.5 mmol/L PA for 16 h. Compared with the C2C12-IR group, MIC-1 significantly increased the glucose consumption and glycogen content of C2C12-IR cells in a dose-dependent manner. MIC-1 significantly decreased the level of MDA and NO, and significantly increased the level of GSH in C2C12-IR cells. In addition, MIC-1 significantly increased the expression levels of phosphoinositide-dependent protein kinase-1 (PDK1), p-AKT, and glucose transporter 4 (GLUT4). Conclusion: MIC-1 improves insulin resistance by inhibiting oxidative stress. © 2023 Chinese Institute of Food Science and Technology. All rights reserved.

关键词： Nitric oxide

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An application-oriented multi-granularity vehicle trajectory data indexing structure based on concept lattice 5

An application-oriented multi-granularity vehicle trajectory...

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5th International Conference on Computer Information Science and Application Technology, CISAT 2022

作者： Xu, Tao Zhang, Zixiang Ma, Xiaqing Deng, Jiaming Zhao, Yingying Li, Xiang Key Laboratory of Geographical Information Science Ministry of Education East China Normal University Shanghai200241 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China International Education College Henan University Zhengzhou450046 China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan University Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China

ISBN: (纸本)9781510660076

Massive amount of vehicle trajectory data is an important data source and has been widely used in many research fields. However, due to the huge volume and variety of application scenarios, it is still not easy to achieve the application-oriented and efficient retrieval of vehicle trajectory. The paper proposes a multi-granularity vehicle trajectory index method based on the concept lattice model. The method identifies the multi-granularity feature regions for application requirements to segment vehicle trajectory and employs the concept lattice model to maintain the complex relationships between vehicle trip and the feature regions, to realize the vehicle travel trajectory data retrieval for the multi-granularity feature regions. In this experiment, shanghai city is divided into 21 feature regions according to the county-level area of Shanghai, and the concept lattice index structure of vehicle trips is constructed. The availability and efficiency of the proposed method are verified by the comparative experimental results. © 2022 SPIE.

关键词： Vehicles

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The Algorithms of Predicting Bacterial Essential Genes and NcRNAs by Machine Learning 11th

The Algorithms of Predicting Bacterial Essential Genes and N...

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11th International Conference on Computer engineering and Networks, CENet2021

作者： Ye, Yuannong Liang, Dingfa Zeng, Zhu Bioinformatics and Biomedical Big Data Mining Laboratory Department of Medical Informatics School of Big Health Guizhou Medical University Guiyang550025 China Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical Engineering School of Biology and Engineering Guizhou Medical University Guiyang550025 China Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of Education Guizhou Medical University Guiyang550025 China

ISBN: (纸本)9789811665530

Essential genes are indispensable for biological survival. Thus it is of great significance to identify and study essential genes. A machine learning method, K-Nearest Neighbor, is used for development of predicting essential bacterial genes. The homologous features, including sequence homology and functional homology, of the bacterial genomes are extracted for determining essential genes. Based on the features, we use K-Nearest Neighbor algorithm for determining of gene function. And we tune the minimum matching parameter (K) in the essential gene predicted model for building an optimal model of the Escherichia coli specificity model. The corresponding optimal parameter (K) is then extended to other bacterial essential genes predicting models. After cross validation, the highest accuracy is 0.89 while K between 5 and 7. Therefore, the features we extracted can increase the accuracy of the bacterial essential gene prediction. In the premise, we found that the prediction accuracy of the prediction model based on K-Nearest Neighbor was not significantly different in different evolutionary distances between organisms in the database and the investigated species. That means the machine learning model can be extended to more distant species. It wills have a better predictive performance for predicting essential genes of distant species than the usual sequence-based methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Escherichia coli

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High-Quality Noise Detection for knowledge Graph Embedding with Rule-Based Triple Confidence 18th

High-Quality Noise Detection for Knowledge Graph Embedding w...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Hong, Yan Bu, Chenyang Wu, Xindong Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Mininglamp Academy of Sciences Mininglamp Technology Beijing China

ISBN: (纸本)9783030891879

knowledge representation learning is usually used in knowledge reasoning and other related fields. Its goal is to use low-dimensional vectors to represent the entities and relations in a knowledge graph. In the process of automatic knowledge graph construction, the complexity of unstructured text and the incorrect text may make automatic construction tools unable to accurately obtain the semantic information in the text. This leads to high-quality noise with matched entity types but semantic errors. Currently knowledge representation learning methods assume that the knowledge in knowledge graphs is completely correct, and ignore the noise data generated in the process of automatic construction of knowledge graphs, resulting in errors in the vector representation of entities and relations. In order to reduce the negative impact of noise data on the construction of a representation learning model, in this study, a high-quality noise detection method with rule information is proposed. Based on the semantic association between triples in the same rule, we propose the concept of rule-based triple confidence. The calculation strategy of triple confidence is designed inspired by probabilistic soft logic (PSL). The influence of high-quality noise data in the training process of the model can be weakened by this confidence. Experiments show the effectiveness of the proposed method in dealing with high-quality noise. © 2021, Springer Nature Switzerland AG.

关键词： knowledge graph

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Hierarchical Multi-Granular Decision Networks for Hierarchical Classification

SSRN

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SSRN 2024年

作者： Liu, Haoyang Hu, Xue gang Bai, Shengxing Lin, Yaojin Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230009 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Anhui Hefei230009 China School of Computer Science Minnan Normal University Fujian Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Fujian Zhangzhou363000 China

Hierarchical classification learning is an effective means of solving large-scale classification problems, which models classification problems at different levels of granularity according to a hierarchical structure. In hierarchical classification tasks, classification results at different levels of granularity are mutually beneficial, where coarse-grained results can reduce the number of fine-grained candidate categories, and fine-grained results can provide a basis for coarse-grained classification. However, for the final decision, the contribution of the granularity levels in the different hierarchies is different and unknown. Feedback from different granularity levels should be flexibly integrated. In addition, errors in hierarchical classification are not equivalent, and some errors can have serious results for practical applications. In order to address the above challenges, we propose a new hierarchical network to integrate multi-granularity knowledge for decision making. On the one hand, the network computes the decision scores of instances at each granularity level and learns a set of weights to combine all scores. Based on the learnable weights, the model can effectively utilize the multi-granularity classification results to obtain the final decision. On the other hand, we design local and global classification losses to guide model optimization, which allows the model to gain a hierarchical perspective and the ability to understand the degree of hierarchical misclassification. As a result, the extent to which the method results in misclassification consequences is limited. Experimental results on five benchmark datasets show that our method arises as a state-of-the-art hierarchical classification method. © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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