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9th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2023

作者： Yuan, Jianfeng Zhang, Na Yang, Bo Zhao, Xin Wu, Xu Wang, Lin University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China Shandong Provincial Key Laboratory of Preparation and Measurement of Building Materials University of Jinan Jinan China

ISBN: (纸本)9798350342239

Cement, as an important material, is widely applied in the world. The macroscopic properties of cement are strongly related to its microstructure. To acquire cement microstructure images, scanning electron microscopy (SEM) is a popular scanning technique for obtaining high-quality backscattered electrons (BSE) images of cement microstructure. Nevertheless, this technique generally cannot acquire large-scale microstructural images due to the limited scanning area. Therefore, this paper proposes a BSE texture synthesis framework based on convolutional neural network (CNN) technology. In this framework, the generator can generate BSE images. The framework also optimizes the generator by perceptual loss. Ultimately, experiments demonstrate that our method can generate arbitrary size BSE textures similar to a given BSE texture. © 2023 IEEE.

关键词： Cements

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A Method of Computer Automatic Scoring for Subjective Questions 2

A Method of Computer Automatic Scoring for Subjective Questi...

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2nd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics, AIHCIR 2023

作者： Jiang, Yan Liu, Hua Li, Jinping Xia, Yingjie School of Information Science and Engineering China University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9798350360363

In response to the limitations of question types and difficulties of automatic scoring, a method of scoring subjective questions automatically in the computer exams is proposed, and then it is implemented. In general, the meth-ods of solving a certain subjective question are only a few, and their ideas and processes are relatively common and fixed. Our method is to evaluate the idea of solving subjective questions, not to evaluate the process of solving subjective questions, that is, to evaluate the application orders or relationships of relevant knowledge points or operations, including the definitions, formulas, theorems, inferences, lemmas, laws, etc., and their application orders using in the solution process of calculation or proof questions. They have already been numbered when setting the subjective questions. Their contents are displayed according to human habits on the system interface, and their numbers are used during the process of the storages and scoring in the com-puters. This method can change the current form of the computer examination and scoring. It can achieve high effi-ciency with low cost, reducing manual labor intensity. In addition, the scoring of subjective questions will become fairer and more transparent. © 2023 IEEE.

关键词： Automatic Scoring Computer Exams Subjective Questions

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INSIDER: A framework for assessing confidence in psychological scales based on multi-modal physiological signal fusion 19

INSIDER: A framework for assessing confidence in psychologic...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Wang, Youmian Hu, Bin Chen, Zhenxiang Jiang, Xiaoqing Liu, Wenjuan Wang, Peicheng University of Jinan Shandong Provincial Key Laboratory of NetWork Based Intelligent Computing Jinan China

ISBN: (纸本)9798350346558

The majority of polygraph questions used in psychometric objectivity assessments have inadequate applicability, low validity, and lengthy assessment times. In order to improve the Trusted Multi-View Classification (TMC) and produce more objective scale confidence results, this paper proposes a framework for assessing confidence in psychological scales based on multi-modal physiological signal fusion (INSIDER). This framework uses physiological signals (i.e. ECG, Eye Movement, and ECG) to identify lying response behaviors. By using the CNN (Temporal&Spatial) deep learning framework, in the three modalities, the EEG has the best accuracy (92.67%) in detecting lying answer behaviors;the TMC-based hybrid fusion method had both high accuracy (96.32%) and high noise immunity;and high accuracy (93.59%) can be attained by using LR classifier in the confidence assessment of the combined scale. Experimental resluts demonstrated that it can replace polygraph questions. When choosing the product, INSIDER takes into account the necessity for portability, which might serve as a model for the development of future mental health scale assessment systems. © 2022 IEEE.

关键词： Electrocardiograms

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Shrinking Mechanism as an Alternative to Cumulative Step-size Adaptation for Evolution Strategy

Shrinking Mechanism as an Alternative to Cumulative Step-siz...

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2023 International Conference on networks, Communications and intelligent computing, NCIC 2023

作者： Zhang, Bo Wang, Lin Yang, Bo Quan Cheng Laboratory Shandong Jinan250103 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China

ISBN: (纸本)9798350371949

In this paper, an adaptive step-size decay mechanism based on the required accuracy of the problem is proposed to replace the traditional cumulative step-size adaptation (CSA). By simply setting the accuracy requirement of the target problem, our method achieves better results than the existing CSA methods. Compared with CSA, our decay mechanism pays more attention to exploration and avoids falling into local optima. In the mid and late stages, we smooth the decay step to converge to the threshold set by the expert, thus progressively refining the search process. We validate this on the last 10 complex multimodal test problems of the COCO dataset. © 2023 IEEE.

关键词： Shrinkage

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A knowledge-enhanced interest segment division attention network for click-through rate prediction

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Neural computing and Applications 2024年第34期36卷 21817-21837页

作者： Liu, Zhanghui Chen, Shijie Chen, Yuzhong Su, Jieyang Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Click-through rate (CTR) prediction aims to estimate the probability of a user clicking on a particular item, making it one of the core tasks in various recommendation platforms. In such systems, user behavior data are crucial for capturing user interests, which has garnered significant attention from both academia and industry, leading to the development of various user behavior modeling methods. However, existing models still face unresolved issues, as they fail to capture the complex diversity of user interests at the semantic level, refine user interests effectively, and uncover users’ potential interests. To address these challenges, we propose a novel model called knowledge-enhanced Interest segment division attention network (KISDAN), which can effectively and comprehensively model user interests. Specifically, to leverage the semantic information within user behavior sequences, we employ the structure of a knowledge graph to divide user behavior sequence into multiple interest segments. To provide a comprehensive representation of user interests, we further categorize user interests into strong and weak interests. By leveraging both the knowledge graph and the item co-occurrence graph, we explore users’ potential interests from two perspectives. This methodology allows KISDAN to better understand the diversity of user interests. Finally, we extensively evaluate KISDAN on three benchmark datasets, and the experimental results consistently demonstrate that the KISDAN model outperforms state-of-the-art models across various evaluation metrics, which validates the effectiveness and superiority of KISDAN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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Flexible TiO_(2)-WO_(3−x) hybrid memristorwith enhanced linearity and synaptic plasticity for precise weight tuning in neuromorphic computing

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npj Flexible Electronics 2024年第1期8卷 180-191页

作者： Jianyong Pan Hao Kan Zhaorui Liu Song Gao Enxiu Wu Yang Li Chunwei Zhang Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinanChina State Key Lab of Transducer Technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghaiChina School of Integrated Circuits Shandong UniversityJinanChina

Tungsten oxide(WO_(3))-based memristors show promising applications in neuromorphic ***,single-layer WO_(3) memristors suffer from issues such as weak memory performance and nonlinear conductance *** this work,a functional layer based on the hybrids of WO_(3−x) and TiO_(2) is proposed for constructing flexible memristors featuring outstanding synaptic *** diverse electrical stimulations to the memristor enables a range of synaptic functions,elucidating its conduction mechanism through the conductive filament *** incorporation of TiO_(2) not only enhances the memristor’s memory characteristics but makes its conductance more linear,symmetrical and uniform during the long-term ***,in view of the enhanced device performance by TiO_(2) doping,the potential of this device for simple behavioral simulation and processing of complex computing problems is ***“learning-forgetting-relearning”characteristics and device integrability are visually *** the device to a convolutional neural network,the recognition accuracy of MNIST handwritten digits reaches 98.7%.

关键词： computing tuning precise

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EHR2HG: Modeling of EHRs Data based on Hypergraphs for Disease Prediction

EHR2HG: Modeling of EHRs Data Based on Hypergraphs for Disea...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Sun, Ziyou Yang, Xiaohui Feng, Zhiquan Xu, Tao Fan, Xue Tian, Jinglan University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9781665468190

EHRs contain the patient's historical disease information, and a natural idea is to predict the patient's disease or diagnose the patient based on EHRs. However, existing deep learning models using EHRs are not satisfactory in solving several key challenges: 1) most of the existing methods are lack of priori knowledge assistance during model learning;and 2) higher-order relationships among diseases and patients are not explored sufficiently. To address these issues, in this paper we propose a hypergraph-based deep learning model for disease prediction, namely EHR2HG. First, we propose to utilize the existing disease classification information to give the model a better initialization condition. Second, by constructing hypergraphs, we consider all of the patients together instead of separate individuals, so our models can model entity relationships such as comorbidities or patient class groups. We have evaluated the proposed model on a real-world EHRs dataset and the results demonstrated that EHR2HG can achieve comparable and better performance than several state-of-the-art baseline methods. © 2022 IEEE.

关键词： Forecasting

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Video Frame Interpolation based on Lightweight Convolutional Unit and Three-scale Encoder 23

Video Frame Interpolation Based on Lightweight Convolutional...

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15th International Conference on Digital Image Processing, ICDIP 2023

作者： Liu, Weijing Yang, Xiaohui Feng, Zhiquan Xu, Tao Guo, Qingbei School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9798400708237

Video frame interpolation (VFI) achieves temporal super-resolution by synthesizing intermediate frame between two original adjacent frames. This paper proposes a lightweight VFI network based on lightweight convolutional unit and three-scale encoder. We first introduce a three-scale encoding-decoding structure with two-level attention cascades to represent the multi-scale motion information. Then, recurrent convolutional layer (RCL) and residual operation are adopted to design the recurrent residual convolutional unit (RRCU) to refine the three-scale structure. Finally, we propose a lightweight unit by combining the depth separable convolution and RRCU, and introduce the local lightweight idea to reduce the model parameters. Experimental results show that the proposed method achieves a better performance against the state-of-the-art methods with fewer parameters. © 2023 ACM.

关键词： Convolution

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Joint Learning based on Discriminant Representation and Group Collaborative Measurement for Image Set Classification 2nd

Joint Learning Based on Discriminant Representation and Grou...

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2nd International Conference on Image, Vision and intelligent Systems, ICIVIS 2022

作者： Liu, Xingrui Feng, Zeming Gao, Xizhan School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9789819909223

In daily life, it is easy to obtain a large number of images in the form of sets. How to extract discriminative information from a wide range of images become very important. Most of the existing image set classification methods based on collaborative representation assume that the images in the same set can affect each other, ignoring the interaction between sets. In order to solve the above problems, a joint learning based on discriminative representation and group collaborative measurement (DRGCM) is proposed. Through the deep subspace clustering algorithm, the set is accurately divided, and multiple local models are found to form a dictionary. In the training stage, a global projection matrix learning model is designed to make the dictionary have stronger representation ability of prototype points in the embedded space. In the testing phase, a class-specific projection matrix model is constructed around the test set to make the dictionary more discriminative to the test set in a specific embedding space. Finally, the excellent performance on Honda, Mobo and other database proves the effectiveness of the algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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Graph Spatial-Spectral Deep Subspace Clustering for Hyperspectral Image Classification 2nd

Graph Spatial-Spectral Deep Subspace Clustering for Hyperspe...

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2nd International Conference on Image, Vision and intelligent Systems, ICIVIS 2022

作者： Han, Tianhao Dong, Jiwen Niu, Sijie Yu, Wenyue Gao, Xizhan School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9789819909223

Deep subspace clustering (DSC) has become a research hotspot and achieved considerable success in unsupervised hyperspectral image (HSI) classification domain. However, previous researches seldom consider global spatial-spectral information, which leads to an unsatisfactory performance when processing HSI with background (can be seen as a type of noise). In this work, we propose a Graph Spatial-Spectral Deep Subspace Clustering (GS2DSC) method which can make full use of the spatial-spectral information of HSI data. Specifically, a graph attention auto-encoder is utilized to extract the graph-structed features of bands and the spectral information. Then, we employ a fusion strategy to fuse the band-level global spatial-spectral features and the patch-level local spatial-spectral features into joint features to enhance the spatial-spectral information. Our proposed method alleviates the bad influence of the background information and produce an informative affinity matrix. The experimental results on two classic HSI datasets show that the overall accuracy of our approach is improved by 5.31% and 3.05% respectively compared with other DSC-based methods, which demonstrates that the structure can enhance the spatial-spectral information and perform better classification results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

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