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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Sun, Shuqi Yang, Xiaohui Peng, Jingliang University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China University of Jinan School of Information Science and Engineering Jinan250022 China

ISBN: (纸本)9781728163277

In this paper we propose a lightweight object detector by optimizing the structure of YOLOv3. The optimization is conducted in two aspects: simplifying the structural components by lightweight substitutes and introducing the attention mechanism to increase the detection accuracy. For the simplification, we remodel the backbone based on MobileNet v2 and replace every 3×3 convolution in the detection neck and head by the fusion of a 3 × 3 depthwise separable convolution and a squeeze and excitation block (DSConv+SE);for the attention enhancement, we introduce the high-frequency wavelets of the original image to the input, a simplified non- local block to the simplified backbone and convolutional block attention modules to the simplified detection neck. In addition, local 3×3 convolution branches are introduced to the simplified backbone for enhanced learning capability. Experiments demonstrate that the proposed detector outperforms each compared state-of-the-art work in one or more aspects. © 2023 IEEE.

关键词： Object detection

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Self-attention Mechanism Fusion for Recommendation in Heterogeneous Information network 8th

Self-attention Mechanism Fusion for Recommendation in Hetero...

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8th International Conference on Signal and Information Processing, network and Computers, ICSINC 2021

作者： Zang, Zhenyu Yang, Xiaohui Feng, Zhiquan Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China School of Information Science and Engineering University of Jinan Jinan250022 China

ISBN: (纸本)9789811933868

In recent years, lots of recommendation methods based on heterogeneous information network has been proposed. However, traditional heterogeneous information network mainly relies on the similarity of meta-path to recommend, which cannot fully mine the user’s potential preference features. We propose a heterogeneous information network model using self-attention mechanism for recommendation (HINS). In our method, local neighbor information is modeled for users and items respectively via collaborative attention mechanism. Moreover, by optimizing the relationship representation between learning nodes of multi label classification problem based on meta-path, self-attention mechanism is added to the relationship representation to make the model personalized. Finally, the two parts are unified into one model for top-N recommendation. We do evaluation experiments on four different datasets, and our model achieves the best results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Information services

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Multi-View Transfer Clustering Algorithm based on Gaussian Mixture Model

Multi-View Transfer Clustering Algorithm Based on Gaussian M...

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International Conference on Fuzzy Theory and it's Applications (iFUZZY)

作者： Yan Jiang Xingchen Li Jin Zhou Shiyuan Han Tao Du Cheng Yang Bowen Liu Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China

ISBN: (数字)9798350352788

ISBN: (纸本)9798350352795

It is well known that the Gaussian mixture model (GMM) is an exemplary model-based clustering approach that captures data distribution patterns to facilitate clustering. However, when it faces to multi-view clustering tasks, the classical GMM algorithm often encounters limitations in scalability. Multi-view clustering entails employing diverse views or feature sets to cluster data, thereby achieving comprehensive and accurate clustering outcomes. The classical GMM algorithm fails to fully exploit the correlated information across multiple views, resulting in a decrease in clustering accuracy. This paper proposes a new GMM-based trans-fer multi-view clustering algorithm named MT-GMM. This algorithm transfers valuable knowledge from other views to guide and enhance the clustering task of the current view, thereby improving the clustering performance specifically for the current view. Experimental results conducted on real-world datasets demonstrate that the proposed MT-GMM outperforms several other multi-view clustering methods, as it can effectively harness the complementary information between different views accurately capture the structures of data.

关键词： Accuracy Clustering methods Scalability Clustering algorithms Entropy Data models Gaussian mixture model

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A Novel Grades Prediction Method for Undergraduate Students by Learning Explicit Conditional Distribution

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4837-4848页

作者： Zhang, Na Liu, Ming Wang, Lin Liu, Shuangrong Sun, Runyuan Yang, Bo Zhu, Shenghui Li, Chengdong Yang, Cheng Cheng, Yuhu China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Shandong Key Laboratory of Intelligent Buildings Technology Jinan250101 China University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China Quan Cheng Laboratory Jinan250100 China Shandong Jianzhu University Shandong Key Laboratory of Intelligent Buildings Technology Jinan250101 China Shandong Jianzhu University School of Information and Electrical Engineering Jinan250101 China Ministry of Education Engineering Research Center of Intelligent Control for Underground Space Xuzhou221116 China

Educational data mining (EDM) offers an effective solution to predict students' course grades in the next term. Conventional grade prediction methods can be viewed as regressing an expectation of the probability distribution of the student's grade, typically called single-value grade prediction. The reliable prediction outcomes of these methods depend on the complete input information related to students. However, next-term grade prediction often encounters the challenge of incomplete input information due to the inaccessibility of future data and the privacy of data. In this scenario, single-value grade prediction struggles to assess students' academic status, as it may not be represented and assessed by relying on a singular expectation value. This limitation increases the risk of misjudgment, and may lead to errors in educational decision-making. Considering the challenge of collecting complete input information, we shift from traditional single-value predictions to forecasting the explicit probability distribution of the course grade. The probability distribution of the grade can assess the students' academic status by providing probabilities corresponding to all possible grade values rather than relying solely on an expectation value, which offers the foundation to support the educators' decision-making. In this article, the course grade distribution prediction (CGDP) model is proposed, aiming to estimate an explicit conditional probability distribution of course grades in the next term. This model can identify at-risk students, offering comprehensive decision-making information for educators and students. To ensure precise distribution predictions, a calibration method is also employed to improve the alignment between predicted and actual probabilities. Experimental results verify the effectiveness of the proposed model in early grade warning for undergraduates, based on real university data. © 2020 IEEE.

关键词： Probability distributions

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UAV Visual Localization based on SFA-Resnet and Contrastive Learning

UAV Visual Localization Based on SFA-Resnet and Contrastive ...

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Electronic Technology and Information Science (ICETIS), International Conference on

作者： Pan Zhu Lin Wang Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China

ISBN: (数字)9798350388343

ISBN: (纸本)9798350388350

At present, UAVs mainly rely on global navigation satellite system to achieve high-precision positioning. However, the positioning signals of UAVs often face the threat of electromagnetic interference as well as spoofing attacks. Visual localization, as an important complement to traditional UAV localization methods, has attracted increasing attention from both industry and academia, in the wake of the booming development of deep learning technologies and computer vision. Most current UAV visual localization methods focus on extracting local features or edge features that are helpful for the localization task. These geometric metric-based features are overly concerned with the details of the gain to the localization task or classification task, while often ignoring the macro-semantic information that is more useful for solving the localization task. The rapid development of cities and the fact that the satellite images of the cities that UAVs rely on for visual localization cannot be updated fast enough have posed a great challenge to UAV visual localization methods that simply use geometric feature information. Current deep convolutional neural networks often lose the contextual and spatial information of images while acquiring semantic information. To address the aforementioned issues, a spatial feature enhancement module is proposed to enable the model to retain spatial information that is helpful for solving visual localization tasks while acquiring high-dimensional semantic information. The proposed spatial feature enhancement module utilizes a set of learnable parameters to flexibly assign different weights to different channels within the feature maps. The experimental results indicate that the proposed method achieves an accuracy of 91.88% on the Recall@1 and 92.13% on the AP.

关键词： Location awareness Visualization Computer vision Urban areas Semantics Contrastive learning Feature extraction

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The Secondary Isolated Data Island: Isolated Data Island Caused by Blockchain in Federated Learning

The Secondary Isolated Data Island: Isolated Data Island Cau...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Chenghan Wang, Shanshan Zhao, Chuan Cao, Xueyang Yan, Shuhao Han, Yadi Huang, Limei Chen, Zhenxiang University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China University of Jinan School of Information Science and Engineering Jinan China Quan Cheng Laboratory Jinan China

ISBN: (纸本)9798350337488

Compared to other fields, data in the medical field has a higher degree of sensitivity. Therefore, in order to ensure that when using medical data for machine learning, a Federated Learning (FL) framework is usually used. However, the traditional centralized FL framework greatly increases the risk of leaking privacy of clients. For this reason, many researchers have developed and designed a Federated Learning based on Blockchain (FLchain) to enhance the privacy of this framework. Usually, each hospital will use an independent FLchain. But the researchers overlooked a crucial issue: the application of blockchain in FLchain can create isolated data island. Different FLchain use different blockchains, so the data between the two frameworks cannot be used to train a model at the same time. We call this problem the secondary isolated data island (SIDI). The problem leads to hospitals using FLchain architecture being unable to use each other's data for model training while ensuring information security. We first discovered and proposed the issue of SIDI, and solved it by using Federated Learning based on Blockchain with Inter-Blockchain Technology (IBT-FLchain) framework. We designed an Hashed Timelock Contract of global model aggregation (HTLC-M) in FLchain by improving the Hashed Timelock Contract (HTLC). We have demonstrated through experiments that the IBT-FLchain framework can effectively solve SIDI problem and greatly alleviate the storage cost of clients in the blockchain. © 2023 IEEE.

关键词： Blockchain Federated Learning Inter-Blockchain Technology Isolated Data Island

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Multi-attribute wearable pressure sensor based on multilayered modulation with high constant sensitivity over a wide range

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Nano Research 2023年第5期16卷 7583-7592页

作者： Ning Li Song Gao Yang Li Jianwen Liu Wenhao Song Guozhen Shen School of Information Science and Engineering University of JinanJinan 250022China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China School of Aeronautical and Astronautical Engineering Purdue University West LafayetteIN 47907USA

Flexible pressure sensors capable of monitoring diverse physiological signals and body movements have garnered tremendous attention in wearable electronic ***,high constant sensitivity over a wide pressure range combined with breathability,biocompatibility,biodegradability is pivotal for manufacturing of reliable pressure sensors in practical sensing *** this work,inspired by the multilayered structure of skin epidermis,we propose and demonstrate a multi-attribute wearable piezoresistive pressure sensor consisting of multilayered gradient conductive poly(ε-caprolactone)nanofiber membranes *** response to externally applied pressure,a layer-by-layer current path is activated inside the multilayered membranes composites,leading to the most salient sensing performance of high constant sensitivity of 33.955 kPa^(−1) within the pressure range of 0–80 *** proposed pressure sensor also exhibits a fast response–relaxation time,a low detection limit,excellent stability,which can be successfully used to measure human physiological ***,an integrated sensor array system that can locate objects’positions is constructed and applied to simulate sitting posture *** results indicate that the proposed pressure sensor holds great potential in health monitoring and wearable electronic devices.

关键词： wearable pressure sensors multilayer structure high constant sensitivity multi-attribute physiological signals monitoring

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PESTA: An Elastic Motion Capture Data Retrieval Method

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Journal of Computer Science & Technology 2023年第4期38卷 867-884页

作者：蒋子飞李伟黄艳尹义龙郭宗杰彭京亮 School of Software Shandong UniversityJinan 250101China Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles 90089U.S.A Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan 250022China School of Information Science and Engineering University of JinanJinan 250022China

Prevalent use of motion capture(MoCap)produces large volumes of data and MoCap data retrieval becomes crucial for efficient data *** clips may not be neatly segmented and labeled,increasing the difficulty of *** order to effectively retrieve such data,we propose an elastic content-based retrieval scheme via unsupervised posture encoding and strided temporal alignment(PESTA)in this *** retrieves similarities at the sub-sequence level,achieves robustness against singular frames and enables control of tradeoff between precision and *** firstly learns a dictionary of encoded postures utilizing unsupervised adversarial autoencoder techniques and,based on which,compactly symbolizes any MoCap ***,it conducts strided temporal alignment to align a query sequence to repository sequences to retrieve the best-matching sub-sequences from the ***,it extends to find matches for multiple sub-queries in a long query at sharply promoted efficiency and minutely sacrificed *** performance of the proposed scheme is well demonstrated by experiments on two public MoCap datasets and one MoCap dataset captured by ourselves.

关键词： motion capture(MoCap) content-based retrieval adversarial autoencoder temporal alignment

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Open-vocabulary 3D Semantic Understanding via Affinity Neural Radiance Fields 6

Open-vocabulary 3D Semantic Understanding via Affinity Neura...

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6th International Conference on Next Generation Data-Driven networks, NGDN 2024

作者： Fan, Yujie Luo, Huan Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9798350388374

Three-dimensional semantic understanding using only several multi-view images can largely reduce the communication burden on the network. In addition, while point clouds are extensively studied for 3D scene understanding, utilization of multi-view image data offers rich visual details and texture information. However, challenges persist in lifting 2D semantic features to 3D space and leveraging language for segmentation. Inspired by recent advancements, this paper proposes a method that combines CLIP features and SAM masks to create a feature field capable of segmenting objects via natural language text across 2D multi-view and 3D Gaussian splatting. It offers a promising function for extracting 3D assets for game engines and the metaverse. Our method involves mask generation from video frames, extracting physical scales via RGB Nerf with masks, and organizing hierarchical information for semantic comprehension. In the training process, affinity features maintain scale properties and guide CLIP feature generation with auto weights blending for semantic robustness. A straightforward 3D splatting CLIP feature approach and canonical text methodology enhance query robustness across 2D multi-view and 3D splatting through relevance score calculation based on text CLIP features for inference. Experimental results demonstrate promising improvements in semantic understanding of 3D scenes. © 2024 IEEE.

关键词： Semantic Segmentation

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High color saturation and angle-insensitive ultrathin color filter based on effective medium theory

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Chinese Optics Letters 2023年第3期21卷 146-151页

作者：李欣婷李阳李超高嵩岳文静 School of Information Science and Engineering University of JinanJinan 250022China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China

An ultrathin angle-insensitive color filter enabling high color saturation and a wide color gamut is proposed by relying on magnesium hydride-hydrogenated amorphous silicon[MgH2-a-Si:H]lossy dielectric *** on effective medium theory,the MgH2-a-Si:H layer with an ultrathin thickness can be equivalent to a quasi-homogeneous dielectric layer wit an effective complex refractive index,which can be tuned by altering the thickness of MgH2to obtain the targeted value o the imaginary part,corresponding to the realization of high color *** is verified that the proposed color filte offers highly enhanced color saturation in conjunction with a wide color gamut by introducing a few-nanometer thic *** the MgH2-a-Si:H layer retains the advantages of high refractive index and tiny thickness,the proposed colo filter exhibits large angular tolerance up to±60°.In addition,MgH2with an unstable property can interconvert with Mg unde a dehydrogenation/hydrogenation reaction,which empowers the proposed color filter with dynamically tunable outpu *** proposed scheme shows great promise in color printing and ultracompact display devices with high color sat uration,wide gamut,large angular tolerance,and dynamic tunability.

关键词： color filter effective medium theory lossy dielectric layer color saturation angle insensitivity

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