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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 1196-1207页

作者： Shenyang Xu Yuan Wang Zijin Li Feng Yu Wei Li Department of Music AI and Information Technology Central Conservatory of Music Beijing China School of Computer Science and Technology Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

Timbre, a perceptual attribute of musical sound, plays a critical role in both instrument recognition and timbre perception research. However, these fields are rarely integrated, and as a result, their findings rarely benefit one another. This study investigates how integrating timbre semantic descriptors as an auxiliary task can enhance musical instrument recognition under challenging cross-dataset conditions via multi-task learning (MTL). A standard flat MTL framework is introduced, alongside an asymmetric MTL proposed to amplify the influence of timbre semantic descriptors on instrument recognition. Experiments are conducted on Western and Chinese instrument datasets to evaluate cross-cultural performance. The results demonstrate significant improvements on both datasets using the flat MTL, with further analysis revealing the varying contributions of individual timbre descriptors and demonstrating its robustness across different hyperparameter settings. The asymmetric MTL achieves additional performance gains on Western datasets but not on Chinese ones. A dedicated analysis is carried out to investigate this discrepancy, offering insights into potential dataset-specific or cultural challenges.

关键词： Instruments Timbre Semantics Vectors Training Accuracy Testing Multitasking Standards Speech processing

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A hybrid feature selection method based on novel binary emperor penguin optimization for IoT intrusion detection system 3

A hybrid feature selection method based on novel binary empe...

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3rd International Conference on Algorithms, Network, and Communication technology, ICANCT 2024

作者： Liu, Xinqian Sun, Shengting Xu, Benda Feng, Zhiquan School of Computer Science and Technology Shandong University of Technology Shandong255000 China Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing Shandong250022 China Finance Office Tianjin University of Traditional Chinese Medicine Tianjin301617 China School of Information Science and Engineer University of Jinan Shandong250022 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

Due to the limited resources of IoT devices, massive redundant traffic features result in IoT-IDSs facing higher resource and time consumption. Therefore, this paper proposes a hybrid feature selection method RFI-BEPO to effectively select the least and useful features. Firstly, the gini index contribution value of each feature is calculated. The top 50% features with high contribution values are retained as initial important features. On this basis, a novel binary emperor penguin optimization algorithm (BEPO) is designed for select optimal features. BEPO algorithm utilizes S-shaped function and its variants to find effective solutions in the binary search space. The distance between other emperor penguins and the optimal penguin is recalculated by the idea of the cosine similarity. Then, random forest model is as the classifier to detect abnormal traffic. Finally, RFI-BEPO method is evaluated on latest ToN-IoT and N-BaIoT datasets. Compared with multiple feature selection methods, RFI-BEPO method outperforms the existing methods in feature number, and has great anomaly detection effect. © 2025 SPIE.

关键词： Random forests

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Enhancing Scalability: A Complete Tree Sharding Architecture Towards IoT 18th

Enhancing Scalability: A Complete Tree Sharding Architecture...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Luyi Wang, Yujue Ding, Yong Liang, Hai Yang, Changsong Zheng, Haibin Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Hangzhou Innovation Institute of Beihang University Hangzhou310052 China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen518055 China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783031714665

With the rapid proliferation of integration of blockchain and IoT, traditional centralized architectures and single-node processing have been effectively solved, but scalability of blockchain have become inadequate to meet the demands of high-load environments. Sharded blockchain brings an innovative approach to tackle the scalability of blockchain and has the potential to greatly enhance the performance of IoT based on blockchain. This paper introduces CTS towards IoT – complete tree sharding that overlaps shards to enable a little shards to retain records of other shards, facilitating the validation of cross-shard transactions as intra-shard transactions. A tree sharding consensus mechanism that relies on the complete tree architecture is proposed to ensure consistency among interconnected shards during the execution of cross-shard transactions. A prototype for CTS towards IoT is simulated and the findings demonstrate that CTS can enhance transaction throughput by a factor of 1.3 when compared to state of the art layered sharding systems observed in a system consisting of 15 shards and 1500 nodes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distributed ledger

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Seeking a Sense of Meaning and Companionship in Life: Informal Learning on Douyin Among Chinese Older Adults

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Proceedings of the ACM on Human-computer Interaction 2025年第2期9卷 1-24页

作者： Zhigu Qian Jiaojiao Fu Yangfan Zhou School of Computer Science Fudan University Shanghai China & Shanghai Key Laboratory of Intelligent Information Processing Shanghai China School of Information Science and Engineering East China University of Science and Technology Shanghai China

This study examines how Chinese older adults leverage Douyin, a short video platform, for informal learning purposes, analyzing their usage patterns, motivations, and encountered challenges. Although Douyin was not explicitly designed with educational features, it has emerged as a significant informal learning platform for this demographic. Through a qualitative investigation comprising participant observations and semi-structured interviews with 17 participants, we reveal the distinctive learning experience that Douyin facilitates. The platform's unique combination of short-form videos, live streaming capabilities, and interactive community features creates an engaging learning environment that particularly resonates with older adults. Our findings demonstrate that participants derive substantial social-emotional benefits beyond knowledge acquisition, including strengthened social connections, enhanced companionship, and a reinforced sense of purpose through their Douyin engagement. These social-emotional aspects emerge as crucial factors driving older adults' selection of Douyin as their preferred learning platform. By providing comprehensive insights into older adults' engagement with digital platforms for lifelong learning, this research offers valuable implications for HCI, particularly in understanding how technology can be optimized to support the learning needs of older populations.

关键词： older adults social feature in e-commerce platform social-emotional support technology adoption technology engagement technology-inclusive design

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VIPNet: Combining Viewpoint information and Shape Priors for Instant Multi-view 3D Reconstruction 17th

VIPNet: Combining Viewpoint Information and Shape Priors fo...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Ye, Weining Li, Zhixuan Jiang, Tingting School of Computer Science Peking University Beijing100871 China College of Computing and Data Science Nanyang Technological University Singapore Singapore National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing100871 China

ISBN: (纸本)9789819609680

While the multi-view 3D reconstruction task has made significant progress, existing methods simply fuse multi-view image features without effectively leveraging available auxiliary information, especially the viewpoint information for guiding and associating features of different views. To this end, we propose to enhance multi-view 3D reconstruction with the power of viewpoint information. Specifically, a simple-yet-effective viewpoint estimator is designed to learn and provide comprehensive viewpoint knowledge for locating and associating learned features from different views. Moreover, to improve the 3D reconstruction quality when 2D images of only very few viewpoints are available, we propose to learn the shape prior knowledge to provide sufficient shape information for compensating the limited 2D observations. Overall, we present VIPNet, benefiting from Viewpoint information and Shape Prior learning for high-quality multi-view 3D reconstruction. Extensive experiments validate the effectiveness of the proposed VIPNet, which achieves state-of-the-art performance on challenging datasets and shows well generalization ability in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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Multiple Encoding Coloring Network for Regong Architectural Colored Drawing Images Combining Sketch Hints

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computer Engineering and Applications 2025年第9期61卷 277-287页

作者： Cheng, Miao Zhang, Xiaojuan Zhao, Yang Fan, Hong School of Computer Science Qinghai Normal University Xining810016 China State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810016 China School of Computer and Information Hefei University of Technology Hefei230002 China School of Computer and Science Shaanxi Normal University Xi’an710062 China

Architectural colored drawing is an important component of the human intangible cultural heritage of Regong art. Some ancient Regong architectural colored drawings have the phenomenon of fading and oxidation, which cannot be maintained promptly due to environmental factors and the limitations of inheritance status. This seriously affects the appreciation and protection of the Regong architectural colored drawings art. Aiming at the problems of color confusion and color leakage when using existing image coloring methods to color architectural colored drawing images, this thesis proposes an end- to- end multiple encoding coloring network for Regong architectural colored drawing images that combines sketch hints. This network supplements the ignored texture details in the feature transfer process with extracted sketch as prior knowledge of the image, enhances the network’s understanding of global semantics, and improves the phenomenon of color distortion and leakage. The multipath parallel encoding module is structured with three encoding branches arranged in different ways using dilated convolutions and traditional convolutions. By obtaining receptive fields of various scales, it better captures contextual information and improves the ability to extract color features. The attention residual module, which combines spatial attention and channel attention, helps the network comprehensively understand the spatial distribution of image color and texture information through the fusion processing of feature information, thereby improving the quality of coloring. Finally, a comparative experiment is conducted on the constructed Regong architectural colored drawing dataset and animation dataset using the proposed method. Compared with existing coloring methods, the proposed method performs well in qualitative and quantitative analysis and achieves the most consistent results with human subjective aesthetics and professional requirements in subjective evaluation comparison. Ex

关键词： Coloring

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Event-based Video Person Re-identification via Cross-Modality and Temporal Collaboration

Event-based Video Person Re-identification via Cross-Modalit...

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

作者： Renkai Li Xin Yuan Wei Liu Xin Xu School of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Video-based person re-identification (ReID) has become increasingly important due to its applications in video surveillance applications. By employing events in video-based person ReID, more motion information can be provided between continuous frames to improve recognition accuracy. Previous approaches have assisted by introducing event data into the video person ReID task, but they still cannot avoid the privacy leakage problem caused by RGB images. In order to avoid privacy attacks and to take advantage of the benefits of event data, we consider using only event data. To make full use of the information in the event stream, we propose a Cross-Modality and Temporal Collaboration (CMTC) network for event-based video person ReID. First, we design an event transform network to obtain corresponding auxiliary information from the input of raw events. Additionally, we propose a differential modality collaboration module to balance the roles of events and auxiliaries to achieve complementary effects. Furthermore, we introduce a temporal collaboration module to exploit motion information and appearance cues. Experimental results demonstrate that our method outperforms others in the task of event-based video person ReID.

关键词： Data privacy Correlation Event detection Collaboration Transforms Streaming media Signal processing Video surveillance Speech processing Identification of persons

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VAGeo: View-specific Attention for Cross-View Object Geo-Localization

VAGeo: View-specific Attention for Cross-View Object Geo-Loc...

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

作者： Zhongyang Li Xin Yuan Wei Liu Xin Xu School of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Cross-view object geo-localization (CVOGL) aims to locate an object of interest in a captured ground- or drone-view image within the satellite image. However, existing works treat ground-view and drone-view query images equivalently, overlooking their inherent viewpoint discrepancies and the spatial correlation between the query image and the satellite-view reference image. To this end, this paper proposes a novel View-specific Attention Geo-localization method (VAGeo) for accurate CVOGL. Specifically, VAGeo contains two key modules: view-specific positional encoding (VSPE) module and channel-spatial hybrid attention (CSHA) module. In object-level, according to the characteristics of different viewpoints of ground and drone query images, viewpoint-specific positional codings are designed to more accurately identify the click-point object of the query image in the VSPE module. In feature-level, a hybrid attention in the CSHA module is introduced by combining channel attention and spatial attention mechanisms simultaneously for learning discriminative features. Extensive experimental results demonstrate that the proposed VAGeo gains a significant performance improvement, i.e., improving acc@0.25/acc@0.5 on the CVOGL dataset from 45.43%/42.24% to 48.21%/45.22% for ground-view, and from 61.97%/57.66% to 66.19%/61.87% for drone-view.

关键词： Location awareness Accuracy Attention mechanisms Image coding Signal processing Encoding Satellite images Object recognition Speech processing Drones

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BeatKAN: An Efficient and Drum-Attuned Beat Tracking Method Using Kolmogorov-Arnold Networks

BeatKAN: An Efficient and Drum-Attuned Beat Tracking Method ...

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

作者： Zhicheng Zhang Ganghui Ru Wei Li School of Computer Science and Technology Fudan University Shanghai China School of Computer Science and Technology Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we propose an efficient and drum-attuned beat tracking method based on Kolmogorov-Arnold networks (KAN). Traditional MLP-based frameworks struggle with complex musical signals due to limited capacity in modeling intricate patterns. Inspired by KAN’s efficient ability to capture complex time-frequency relationships, we leverage it to enhance our model by employing learnable nonlinear activation functions on convolutional kernels. Additionally, we utilize music source separation techniques to extract drum tracks from the original audio, thereby expanding the existing beat datasets and simulating the human perception of beats through drum sounds. Experimental results demonstrate that our approach significantly reduces the parameter count while maintaining high accuracy in beat tracking.

关键词： Time-frequency analysis Accuracy Source separation Convolution information retrieval Acoustics Kernel Speech processing

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SPRGAN: Streamlined Progressive Refinement for Adversarial Point Cloud Video Upsampling

SPRGAN: Streamlined Progressive Refinement for Adversarial P...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhang, Zhiyong Liu, Ruyu Wang, Chaochao Zhang, Xianchao Zhang, Jianhua Liu, Xiufeng School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Information Science and Technology Hangzhou Normal University Hangzhou China Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing University Zhejiang China Department of Technology Management and Economics Technical University of Denmark Lyngby Denmark

ISBN: (纸本)9798350368741

Getting dense, uniform, time-series point cloud data is critical for effective rendering. However, due to the limited computational power of edge devices, existing methods cannot achieve real-time results, which affects the visual quality of the consumer experience. To effectively address this issue, this paper presents a self-supervised adversarial upsampling method for point cloud video streams called SPR-GAN. In the generator, we design the Temporal Iterative Graph module to learn local features for each frame and captures long-range spatial information using three iterations of graph convolution operations. Then the Contextual Temporal Fusion module is developed to merge information between different frames, synthesizing temporal information and enriching the dynamic feature representation of the point cloud. Meanwhile, in the discriminator, we introduce the Efficient Shape module. Through dynamic graph convolution operations and stacked learning, it significantly improves the resolution efficiency of global shape information in point clouds. The final experiments show that the proposed method exhibits high practicality and superiority. The model achieves a good result on both the D-FAUST and DeformingThing4D-Animals datasets. © 2025 IEEE.

关键词： point cloud sequence Point cloud upsampling self-supervised temporal aggregation

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