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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Yadi Wang Xiaoding Guo Yibo Zhang Yiyuan Ren Wendi Huang Zunyan Liu Yuming Feng Xiangguang Dai Wei Zhang Hangjun Che School of Computer and Information Engineering Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Chongqing China College of Electronic and Information Engineering Southwest University Chongqing China

Multimodal emotion recognition has great application scenarios in the field of human-computer interaction, and the main task lies in how to capture and handle the consistency and complementarity of multimodal signals. However, in numerous scenarios, multimodal fusion is significantly challenged by variances in the strengths and weaknesses of different modality signals in carrying emotional information. This paper aims to learn the essential information of strong modalities and inter-modality correlations thus achieving better emotion recognition. We propose an adaptive multimodal emotion recognition framework based on collaborative discriminative learning (AMCDL), a framework for emotion recognition that engages in collaborative learning and adjusts to the information-processing capabilities of various emotion recognition models. AMCDL calculates the inter-modality correlation using Canonical Correlation Analysis (CCA) and uses it as an influencing factor to boost the multiplicative combination method to create a class loss function. When trained on several modalities, the class loss function can integrate complementary information to enable strong modal expression, enhancing emotion perception. On the benchmark dataset CMU-MOSI, AMCDL significantly improves classification accuracy when compared to the state-of-the-art models. The experiments showed the flexibility and generality of AMCDL by applying several emotion recognition models to it and producing better classification results than the original model.

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Experimental Study on the Zebra Crossing Behavior of Mixed Bicycles and Pedestrians

SSRN

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SSRN 2022年

作者： Ma, Jian Wang, Qiao Chen, Juan Jiang, Rui Song, Weiguo Li, Ruoyu Lian, Liping School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu610031 China Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University Chengdu610031 China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China State Key Laboratory of Fire Science University of Science and Technology of China Hefei230027 China School of Urban Planning and Design Peking University Shenzhen Graduate School School of Architectural Engineering Shenzhen Polytechnic Shenzhen518055 China

Slow transportation mode, consisting of walking and cycling, plays an important role in urban traffic system. Based on the fact that bicycles and pedestrians often share the common road facility, we conducted bicycle-pedestrian mixed flow experiments to investigate the interaction effect among individuals in a 10 m-long, 3 m-wide passageway, mimicking the zebra crossing area. The goal is to explore the influence of bicycles proportion ( p ) on traffic efficiency and investigate the spatial distribution of agents in the mixed flow. Our analyses showed that the increase of bicycle proportion results in longer passing time and less travel time because of large personal space requirements of cyclists. When the bicycle proportion is small or large, the majority pedestrians (bicycles) were divided into several parts by minor bicycles (pedestrians) along the motion direction. We also recorded the following- and lateral- distance between two nearest individuals to calculate the theoretical formula of the passing time for unidirectional bicycle-pedestrian mixed flow based on the existing formula. Comparing the passing time between numerical calculation and experimental data, the effect coefficients for bicycle and pedestrian were obtained. Focusing on the overtaking bicycles, the through gap, lateral and headway distance that decide the overtaking behavior were analyzed. These findings are the first step toward the development of bicycle-pedestrian mixed flow on zebra crossing, which is contribute to provide practical implications for the planning and design of bicycle and pedestrian shared infrastructures. © 2022, The Authors. All rights reserved.

关键词： Travel time

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Incorporating Entity Type Information into Knowledge Representation Learning

Incorporating Entity Type Information into Knowledge Represe...

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IEEE International Conference on data Science in Cyberspace (DSC)

作者： Wenyu Huang Guohua Wang Huakui Zhang Feng Chen School of Software Engineering South China University of Technology Key Laboratory of Big Data and Intelligent Robot (South China University of Technology) Ministry of Education Guangzhou China

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195599

Knowledge Representation Learning (KRL), which is also known as Knowledge Embedding, is a very useful method to represent complex relations in knowledge graphs. The low-dimensional representation learned by KRL models makes a contribution to many tasks like recommender system and question answering. Recently, many KRL models are trained using square loss or cross entropy loss based on Closed World Assumption (CWA). Although CWA is an easy way for training, it violates the link prediction task which exploits KRL. To overcome the drawback, in this paper, we introduce a new method, Type-based Prior Possibility Assumption (TPPA). TPPA calculates type based prior possibilities for missing triplets instead of zeros in the training process of KRL to weaken the bad influence of CWA. We compare TPPA with the baseline method CWA in ConvE and TuckER, two common frameworks for knowledge representation learning. The experiment results on FB15k-237 dataset show that TPPA based training method outperforms CWA based training method in link prediction task.

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Learning Task-aware Robust Deep Learning Systems

arXiv

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arXiv 2020年

作者： Han, Keji Li, Yun Long, Xianzhong Ge, Yao Nanjing University of Posts and Telecommunications China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing

Many works demonstrate that deep learning system is vulnerable to adversarial attack. A deep learning system consists of two parts: the deep learning task and the deep model. Nowadays, most existing works investigate the impact of the deep model on robustness of deep learning systems, ignoring the impact of the learning task. In this paper, we adopt the binary and interval label encoding strategy to redefine the classification task and design corresponding loss to improve robustness of the deep learning system. Our method can be viewed as improving the robustness of deep learning systems from both the learning task and deep model. Experimental results demonstrate that our learning task-aware method is much more robust than traditional classification while retaining the accuracy. © 2020, CC BY.

关键词： Deep learning

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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HAM:a deep collaborative ranking method incorporating textual information

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Frontiers of Information Technology & Electronic Engineering 2020年第8期21卷 1206-1216页

作者： Cheng-wei WANG Teng-fei ZHOU Chen CHEN Tian-lei HU Gang CHEN The Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Hangzhou 310027China State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China

The recommendation task with a textual corpus aims to model customer preferences from both user feedback and item textual *** is highly desirable to explore a very deep neural network to capture the complicated nonlinear ***,training a deeper recommender is not as effortless as simply adding layers.A deeper recommender suffers from the gradient vanishing/exploding issue and cannot be easily trained by gradient-based ***,textual descriptions probably contain noisy word *** extracting feature vectors from them can harm the recommender’s *** overcome these difficulties,we propose a new recommendation method named the HighwAy reco Mmender(HAM).HAM explores a highway mechanism to make gradient-based training methods stable.A multi-head attention mechanism is devised to automatically denoise textual ***,a block coordinate descent method is devised to train a deep neural *** studies show that the proposed method outperforms state-of-the-art methods significantly in terms of accuracy.

关键词： Deep learning Recommendation system Highway network Block coordinate descent

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Building Cooling load prediction based on LightGBM

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IFAC-PapersOnLine 2022年第11期55卷 114-119页

作者： RuoChen Zhao Dong Wei YiBing Ran Guang Zhou YuChen Jia ShiLun Zhu YouQuan He School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Xicheng District Beijing 100032 China Beijing Municipal Science and Technology Commission Key Laboratory of Intelligent Processing for Building Big Data Xicheng District Beijing 100032 China Siemens Ltd. China 100020 China Zhongkai University of Agriculture and Engineering Guangzhou Tevo Energy Management Co. Ltd.

In building Heating Ventilation and Air Conditioning (HVAC) control systems, the HVAC must be controlled in real-time according to the actual building cooling load demand while minimizing energy consumption. Given that the building cooling load is affected by several factors and that historical data are sparse and have outliers due to sensor errors, existing prediction methods cannot accurately calculate the building's cooling load demand. Therefore, this paper first pre-processes the historical building cooling load data, fills in the missing data utilizing the KNNImputer algorithm, and then locates and replaces the outlier data through the 3-sigma method, solving the poor data availability problem. Moreover, to address the problem of small feature variety in historical data, this paper analyzes the time features and the climatic features separately. Specifically, our method splits the time features into several independent features for describing the impact of pedestrian flow on the building cooling load and expands the climatic features to increase the variety of the features that can be used for training and improving the model's prediction accuracy. Finally, the LightGBM algorithm is exploited to train the processed data and build a building cooling load prediction model. The proposed data pre-processing method and model training algorithm are validated through Python programming, with the results highlighting that the proposed method effectively improves the prediction accuracy of building cooling loads.

关键词： Building cooling load prediction LightGBM data pre-processing KNN 3Sigma

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Reconstructing Missing Modalities in Multi-Modal Endoscopic Ultrasound Via Cross-Modal Feature Replacement Representation

SSRN

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SSRN 2024年

作者： Zheng, Cenyang Gong, Xun Fan, Lin Li, Jiao School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China Department of Gastroenterology The Third People’s Hospital of Chendu Affiliated Hospital of Southwest Jiaotong University Chengdu610031 China

Endoscopic ultrasound (EUS) is the primary diagnostic imaging technique for gastrointestinal stromal tumors. However, accurately identifying these tumors through EUS imaging alone remains challenging, even for experienced clinicians. They often rely on additional data, such as white light endoscopy (WLE) and EUS text reports, to aid in the diagnosis. Unfortunately, in practice, these supplementary modalities are sometimes overlooked during patient evaluation. To address these issues, we propose the Cross-modal Feature Reconstruction Network (CFRN), which integrates multimodal data modeling for medical diagnostic processes and performs specific learning for different modalities with missing data. The network fuses the multi-modal features based on the clinical medical process, realizes the feature-level missing modality reconstruction, and learns the refined features of different modal true cases by decoding the mode-specific missing. We trained and tested the model on two publicly available datasets, Audiovision-MNIST, MM-IMDb, and data sets collected from real clinical scenarios at three hospitals. Our results show that CFRN outperforms state-of-the-art methods, achieving higher prediction accuracy than using EUS alone. By simultaneously incorporating multimodal or reconstructed modal data for disease diagnosis, our single-modal EUS model proves to be more suitable for clinical scenarios. © 2024, The Authors. All rights reserved.

关键词： Endoscopy

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Three-way conflict analysis model via the best-worst method: Balancing subjective preferences and objective data on incomplete and dispersed systems

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International Journal of Approximate Reasoning 2025年 186卷

作者： Zhu, Junjie Zhang, Qinghua Luo, Nanfang Liu, Fan Yin, Longjun School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing400065 China Tourism Multi-source Data Perception and Decision Technology Key Laboratory of the Ministry of Culture and Tourism Chongqing400065 China

In real-world environments, different issues contribute to conflicts with varying weights. However, current conflict analysis weighting models face significant limitations when dealing with incomplete data and dispersed knowledge. Objective methods are sensitive to missing values and struggle to accurately capture authentic preferences, while subjective approaches lack systematic evaluation criteria, leading to substantial randomness in weight assignments. Therefore, a three-way conflict analysis model via the best-worst method is proposed, which is combined with a correlation coefficient method to balance subjective preferences and objective data. First, the trisection of agent pairs is derived through Bayesian minimum risk. Subsequently, a new conflict distance function is defined on the incomplete information system to provide a more precise measurement of conflict degrees. Then, for incomplete and dispersed information systems, a maximal coalition-based agent partitioning algorithm is designed, along with a new weighted voting mechanism to aggregate dispersed knowledge. Finally, the scientific transparency of the weighting process, as well as the robustness and feasibility of the model, are demonstrated through experimental analysis. © 2025 Elsevier Inc.

关键词： Distributed database systems

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An Orbital Angular Momentum Mode Estimation Method with an Unknown Beam Axis

An Orbital Angular Momentum Mode Estimation Method with an U...

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IEEE SENSORS

作者： Gaofeng Shu Bingxu Chen Ning Li School of Computer and Information Engineering Henan University Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Orbital angular momentum (OAM) beam is very difficult to receive due to the divergence of the beam. The position of the beam axis must be known by the existing receiving methods, including the single point method and the phase gradient method. This article proposes an OAM mode estimation method with an unknown beam axis. This method first estimates the position of the beam axis according to the phase characteristics and then uses the phase gradient method to estimate the OAM mode. The simulation results verify the effectiveness of the method.

关键词： Gradient methods Simulation Estimation Orbits Sensors

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