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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Wang, Hao Wang, Yincan Fang, Baofu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Anhui230009 China School of Computer Science and Information Engineering HeFei University of Technology Anhui Hefei230009 China

ISBN: (纸本)9783030930486

Various algorithms of traditional visual Simultaneous Localization and Mapping (SLAM) can well match with static scenes, but mismatches will occur in dynamic scenes, which makes the positioning and mapping of the SLAM system produce large errors. Therefore, this paper proposed a visual odometry algorithm based on semantic feature points, which can improve the positioning accuracy in dynamic scenes. The algorithm combined semantic information to detect dynamic objects, and then detects and eliminates dynamic feature points. This paper conducted an extensive evaluation of the system and compared it with ORB-SLAM3 and other dynamic scene SLAM systems. The experimental results show that this method greatly improves the positioning accuracy of the camera and the robustness of the system in a highly complex dynamic environment, which verifies the advancement and effectiveness of the algorithm in this paper. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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Transfer Learning based Efficient Schizophrenia Classification on Electroencephalogram (EEG) Signals: A Cross-dataset Study 23

Transfer Learning based Efficient Schizophrenia Classificati...

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15th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2023

作者： Liu, Tengjun Zhang, Yaoyun Wu, Yunying Li, Tuoru Li, Zihao Chen, Weidong Zhang, Shaomin Zhejiang University Qiushi Academy for Advanced Studies Department of Biomedical Engineering China Alibaba Damo Academy Alibaba Group China Zhejiang University Qiushi Academy for Advanced Studies Department of Computer Science and Technology China Zhejiang University Qiushi Academy for Advanced Studies Zhejiang Prov. Key Lab. of Cardio-Cerebral Vascular Detection Technology Effectiveness Appraisal Key Laboratory of Biomedical Engineering of Ministry of Education Department of Biomedical Engineering China

ISBN: (纸本)9798400700385

Although EEG classification for schizophrenia has shown promising results on individual datasets, the cross-dataset generalizability of such classification remains unknown. This study aimed to assess this generalizability through transfer learning at segment and individual levels, by employing the spectral convolutional neural network (CNN-S) on two distinct EEG datasets for schizophrenia classification. While direct cross-decoding only obtained baseline transfer accuracies of 54.72% ± 2.77% and 47.78% ± 3.62% at the segment and individual levels, the fine-tuned CNN-S achieved average cross-dataset transfer accuracies of 76.46% ± 1.17% and 56.71% ± 5.76, respectively. To improve the limited generalizability at the individual level, we applied transfer component analysis (TCA), a domain adaptation approach, to the two datasets, leading to an average cross-dataset transfer accuracy of 62.12% ± 4.86%. By combining fine-tuning and TCA, the study obtained an average cross-dataset transfer accuracy of 69.15% ± 4.09% at the individual level. Overall, transfer learning proves useful for cross-dataset EEG schizophrenia classification. © 2023 ACM.

关键词： Electroencephalography

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An Efficient Customized Blockchain System for Inter-Organizational Processes

An Efficient Customized Blockchain System for Inter-Organiza...

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IEEE International Conference on Web Services (ICWS)

作者： Puwei Wang Zhouxing Sun Rui Li Jinchuan Chen Ping Gong Xiaoyong Du School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering (Ministry of Education) Renmin University of China School of Mathematics Renmin University of China College of Computer and Cyber Security Fujian Normal University

Blockchain technologies pave a promising way for implementing the inter-organizational processes. Most of the current research works translate the execution logic in the process models into the smart contracts, which can run independently on the blockchain without the outside process engine. However, the works usually suffer from the execution and storage costs, since the translation needs to be done when the processes are deployed. In this paper, we customize a process engine for executing the inter-organizational business processes via a blockchain-style procedure, i.e., checking the validity of transactions, adding the valid transactions into the blockchain through the consensus mechanism, and then updating the process states according to the committed transactions. And then, we build a blockchain system by embedding the customized process engine into the blockchain nodes. Moreover, in order to realize the interactions between the inter-organizational processes running on blockchain and the services outside blockchain, we propose a blockchain-based approach for service registration, binding and invocation, and design a lease-based concurrency control protocol to logically isolate transactions from each other when invoking the services simultaneously. Finally, we implement a prototype system based on a permissioned blockchain platform Hyperledger Fabric and a process engine Activiti. The experimental results show the proposed blockchain system can execute the inter-organizational processes correctly and efficiently.

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Mamba Adapter: Efficient Multi-Modal Fusion for Vision-Language Tracking

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Shi, Liangtao Zhong, Bineng Liang, Qihua Hu, Xiantao Mo, Zhiyi Song, Shuxiang Ministry of Education Guangxi Normal University Key Laboratory of Education Blockchain and Intelligent Technology Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Wuzhou University School of Data Science and Software Engineering Wuzhou543002 China

Utilizing the high-level semantic information of language to compensate for the limitations of vision information is a highly regarded approach in single-object tracking. However, most existing vision-language (VL) trackers employ full-parameter fine-tuning, which can easily lead to catastrophic forgetting. Therefore, they fail to fully exploit the prior knowledge of pre-trained models from upstream tasks, resulting in unsatisfactory tracking performance. To alleviate the above problem, we propose a simple yet effective Vision-Language Tracking pipeline based on Mamba Adapter, named MAVLT, which adopts the idea of parameter-efficient fine-tuning (PEFT) to realize the interaction between vision-language modalities. This novel approach offers the following advantages: (1)The knowledge of the upstream pre-trained model is efficiently inherited by freezing its parameters. This ensures that the VL tracking framework only learns the modules for vision and language interaction, with a focus on the fusion between modalities. (2)The modal interaction between language and vision encoders is flexibly bridged in each encoder layer via proposed mamba adapter, enabling efficient interaction of visual and language information at multiple levels. Extensive experiments on five popular vision-language tracking benchmarks validate the effectiveness of the proposed MAVLT. Particularly, the MAVLT achieves 73.4% AUC score on the LaSOT benchmarks with only 0.18%(0.32M) of the total parameters updates. Code and models are availab.e at https://***/GXNU-Zhonglab.MAVLT. © 1991-2012 IEEE.

关键词： Semantics

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A Zero-shot Learning Method with a Multi-Modal knowledge Graph

A Zero-shot Learning Method with a Multi-Modal Knowledge Gra...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuhong Zhang Haitao Shu Chenyang Bu Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Zero-shot learning aims to recognize unseen-classes using some seen-class samples as training set. It is challenging owing to that the feature representations of unseen-class samples are unavailab.e. Existing methods transfer the mapping from seen-classes to unseen-classes with the correlation as a bridge, in which, the semantic representations are used to discriminate the classes. However, the unavailab.lity of visual representations for unseen-classes and the insufficient discrimination of semantic representations make the zero-shot learning challenging. Therefore, the visual representations are learned as complements to semantic representations to construct a multi-modal knowledge graph (KG), and a zero-shot learning method based on multi-modal KG is proposed in this paper. Specially, a semantic KG is introduced to capture the correlation of classes, and with the correlation, the visual feature representations of all classes are learned. Then, the discriminative visual representations and the semantic representations are used together to construct a multi-modal KG. With the multi-modal KG, the classifier for seen-classes is transferred to unseen classes. Extensive experimental results show the effectiveness of our method.

关键词： Learning systems Representation learning Training Bridges Visualization Correlation Semantics

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xinzheng Li Yuhong Zhang Peipei Li Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Cross-network node classification aims to use a lab.led source network to classify nodes in an unlab.led target network. Most of the existing cross-network node classification methods learn the network representations by capturing the node neighborhood and train the classifier on these representations. The performance is highly dependent on the high-quality neighborhood in the network. However, in applications, the degree of nodes generally follows a long-tail distribution, i.e., a significant proportion of nodes are tail nodes with sparse neighborhood. It poses a challenge to existing methods. To this end, a structure similarity graph for cross-network node classitication method (SCNC) is proposed in this paper. Firstly, the potential links between nodes are predicted with the structural similarity metric to construct structure similarity graph, which can enrich the neighborhood of tail nodes. Then, the embedding representations of the structural similarity graph are learned to capture more neighborhood information. Finally, the adversarial is used to learn the domain invariant representations to address cross-network divergence. Extensive experimental results show that our SCNC outperforms the state-of-the-art methods.

关键词： Network topology Tail Learning (artificial intelligence) Prediction algorithms Classification algorithms Noise measurement Task analysis

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Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection 39

Each Fake News is Fake in its Own Way: An Attribution Multi-...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Hao Ma, Zihan Zeng, Zhi Luo, Minnan Zeng, Weixin Tang, Jiuyang Zhao, Xiang Laboratory for Big Data and Decision Nation University of Defense Technology China School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China

ISBN: (纸本)157735897X

Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary lab.ls of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset AMG, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model MGCA to achieve multimodal fake news detection and attribution. Experimental results demonstrate that AMG is a challenging dataset, and its attribution setting opens up new avenues for future research. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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KAN v.s. MLP for Offline Reinforcement Learning

arXiv

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arXiv 2024年

作者： Guo, Haihong Li, Fengxin Li, Jiao Liu, Hongyan School of Information Renmin University of China China Institute of Medical Information Medical Library Chinese Academy of Medical Sciences Peking Union Medical College China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Economics and Management Tsinghua University China

Kolmogorov-Arnold Networks (KAN) is an emerging neural network architecture in machine learning. It has greatly interested the research community about whether KAN can be a promising alternative of the commonly used Multi-Layer Perceptions (MLP). Experiments in various fields demonstrated that KAN-based machine learning can achieve comparable if not better performance than MLP-based methods, but with much smaller parameter scales and are more explainable. In this paper, we explore the incorporation of KAN into the actor and critic networks for offline reinforcement learning (RL). We evaluated the performance, parameter scales, and training efficiency of various KAN and MLP based conservative Q-learning (CQL) on the the classical D4RL benchmark for offline RL. Our study demonstrates that KAN can achieve performance close to the commonly used MLP with significantly fewer parameters. This provides us an option to choose the base networks according to the requirements of the offline RL tasks. © 2024, CC BY.

关键词： Adversarial machine learning

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Analysis of the Impact of Newly Built Grounding Grid on the Original Grounding Grid Based on the Method of Moments 3

Analysis of the Impact of Newly Built Grounding Grid on the ...

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3rd International Conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Wang, Letian Liu, Zhen Song, Mengqing Zheng, Xuan Ma, Chenglian Shandong Electric Power Engineering Consulting Institute Co. Ltd. Jinan China Key Lab. of Mod. Pwr. Syst. Simulat. and Contr. and Renewable Ener. Technol. Ministry of Education Northeast Electric Power University Jilin China

On the basis of the method of moments, the resistive coupling effect between the newly built grounding network. When the horizontal distance between two grounding networks of a substation changes, this original grounding network of a certain substation is analyzed. And establish corresponding soil models based on actual engineering soil resistivity data. Use the grounding grid analysis function of CDEGS software and Powerstation software simulation software to simulate and calculate some parameters of the new and old grounding grids. The analysis parameters mainly include the grounding resistance, step voltage, and contact voltage of the grounding grid system. And the obtained parameter indicators are compared and analyzed with safety limits to determine the safety performance of the newly built grounding grid system in the substation. And the impact on the grounding performance of the original grounding network of the substation. Provide certain theoretical data support for the actual promotion of substation grounding network engineering. © 2023 The Authors. Published by Elsevier B.V.

关键词： Method of moments

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Deep Application of BIM Visualization Intelligent Technology in Construction 7

Deep Application of BIM Visualization Intelligent Technology...

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7th International Technical Conference on Frontiers of Hydraulic and Civil engineering Technology, HCET 2022

作者： Qiao, Xueliang Qiao, Guangyu Li, Mingke Wei, Suyao Wang, Xiyan Wang, Yuanqing The Third Construction Engineering Company Ltd of China Construction Second Engineering Bureau Beijing100070 China China National Botanical Garden Beijing100093 China Tsinghua University Department of Civil Engineering Key Lab. of Civil Eng. Safety and Durability of China Education Ministry Beijing100084 China

ISBN: (纸本)9781643683546

At present, the application of domestic BIM technology is basically concentrated on the integration of three-dimensional visualization of buildings, progress simulation and comprehensive arrangement of pipes. In order to further explore the in-depth application of BIM technology in the fields of traditional construction engineering, restoration of ancient buildings and 3D printing, this paper integrates key information such as 3D scanning, progress simulation, load debugging, business management and control, and refined management through the BIM model to help the construction process perform simulation to provide core data in time such as accurate resource consumption, technical requirements, and other important links in the construction process such as materials, business, schedule, production, etc, and to improve communication and decision-making efficiency, thereby saving time and costs and improving project management quality. © 2022 The authors and IOS Press.

关键词： Project management

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