检索结果-内蒙古大学图书馆

Predicting Lactobacillus delbrueckii ***-Streptococcus thermophilus interactions based on a highly accurate semi-supervised learning method

引用

Science China(Life Sciences) 2025年第2期68卷 558-574页

作者： Shujuan Yang Mei Bai Weichi Liu Weicheng Li Zhi Zhong Lai-Yu Kwok Gaifang Dong Zhihong Sun Key Laboratory of Dairy Biotechnology and Engineering Ministry of EducationInner Mongolia Agricultural UniversityHohhot 010018China Key Laboratory of Dairy Products Processing Ministry of Agriculture and Rural AffairsInner Mongolia Agricultural UniversityHohhot 010018China Inner Mongolia Key Laboratory of Dairy Biotechnology and Engineering Inner Mongolia Agricultural UniversityHohhot 010018China Collaborative Innovative Center for Lactic Acid Bacteria and Fermented Dairy Products Ministry of EducationInner Mongolia Agricultural UniversityHohhot 010018China College of Computer and Information Engineering Inner Mongolia Agricultural UniversityHohhot 010018China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Hohhot 010018China

Lactobacillus delbrueckii ***(***)and Streptococcus thermophilus(***)are commonly used starters in milk *** experiments revealed that *** interactions(Lb St I)substantially impact dairy product quality and *** biological humidity experiments are time-consuming and labor-intensive in screening interaction combinations,an artificial intelligence-based method for screening interactive starter combinations is ***,in the current research on artificial intelligence based interaction prediction in the field of bioinformatics,most successful models adopt supervised learning methods,and there is a lack of research on interaction prediction with only a small number of labeled ***,this study aimed to develop a semi-supervised learning framework for predicting Lb St I using genomic data from 362 isolates(181per species).The framework consisted of a two-part model:a co-clustering prediction model(based on the Kyoto Encyclopedia of Genes and Genomes(KEGG)dataset)and a Laplacian regularized least squares prediction model(based on K-mer analysis and gene composition of all isolates datasets).To enhance accuracy,we integrated the separate outcomes produced by each component of the two-part model to generate the ultimate Lb St I prediction results,which were verified through milk fermentation *** through milk fermentation experiments confirmed a high precision rate of 85%(17/20;validated with 20 randomly selected combinations of expected interacting isolates).Our data suggest that the biosynthetic pathways of cysteine,riboflavin,teichoic acid,and exopolysaccharides,as well as the ATP-binding cassette transport systems,contribute to the mutualistic relationship between these starter bacteria during milk ***,this finding requires further experimental *** presented model and data are valuable resources for academics an

关键词： Lactobacillus delbrueckii subsp.bulgaricus and Streptococcus thermophilus interaction prediction semi-supervised learning dairy starter artificial intelligence milk fermentation

来源：评论

学校读者我要写书评

暂无评论

Graph-Enhanced Multi-Scale Contrastive Learning for Graph Anomaly Detection with Adaptive Diffusion Models

引用

IEEE Transactions on big data 2025年

作者： Xiao, Chunjing Tong, Yao Lu, Jiahui Xue, Yuxia Tai, Wenxin Cheng, Zhangtao Zhou, Fan The Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China The University of Electronic Science and Technology of China Chengdu610054 China

Graph anomaly detection has gained significant research interest across various domains. Due to the lack of labeled data, contrastive learning has been applied in detecting anomalies and various contrastive strategies have been initiated. However, these methods might force two instances (e.g., node-level and subgraph-level representations) with different category labels to be consistent during model training, which can adversely impact the model robustness. Also, they extract node-level representations only based on node attributes, which are inadequate in reflecting the information of the structural anomaly. To tackle this problem, we present a Graph-enhanced multi-scale Contrastive Learning framework for Anomaly Detection, GCLAD. In this framework, we design a diffusion probabilistic model-based graph enhancement module to adaptively manipulate neighbors to generate enhanced graphs, which can efficiently enhance subgraph-level representations and alleviate the inconsistent problem. Further, we present a multi-scale contrastive module where we introduce meta-paths to exploit a few relevant neighbors to boost node-level representations, and build the multi-scale contrastive losses to promote anomaly detection performance. Experimental results demonstrate the superiority of GCLAD compared with state-of-the-art baselines. © 2015 IEEE. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

MSMAE-Net: multi-semantic and multi-attention enhanced network for image forgery localization

引用

Signal, Image and Video processing 2025年第7期19卷 1-11页

作者： Liao, Jianjun Su, Lichao Lu, Menghan Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

The popularization of modern digital image technology has brought convenience to us, but it also poses many risks. The advancement of image editing software allows anyone to modify image content effortlessly. If these modified images are abused, they can severely impact societal safety and stability. To address these risks, we propose an end-to-end multi-scale collaborative enhancement image forgery localization network, termed MSMAE-Net. The method first employs a multi-branch feature extractor to initially capture both global and local information, with each branch optimized for different contextual information and features of the image. Next, to enhance the model’s ability to capture forged traces at various levels while maintaining the correlation between different spatial hierarchies, a boundary information aggregation module is designed. By constructing multiple branches with different receptive fields and collaborative learning with each other, a nested collaborative enhancement branch is designed to make the backbone network pay more attention to key features, so as to obtain better feature representation capability. Furthermore, to further fuse different semantic information, a novel strongly compatible semantic attention fusion module is proposed in this paper. Finally, an attention enhancement module is introduced in the localization of forgery regions to adaptively concentrate on key forged areas in the image and effectively capture the relationships between different pixels. In the experiments, we demonstrate that MSMAE-Net exhibits significant advantages in both localization accuracy and robustness in complex forgery scenarios when compared to other SOTA image forgery detection methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Human Activity Recognition Based on Fine-Grained Capture Spatiotemporal Features of Body RFID Skeleton 18th

Human Activity Recognition Based on Fine-Grained Capture Sp...

引用

18th China Conference on Wireless Sensor Networks, CWSN 2024

作者： Liu, Meng Chen, Yihong Zhao, Yilin The Internet of Things Perception and Big Data Analysis Key Laboratory of Nanchong City China West Normal University Nanchong637002 China

ISBN: (纸本)9789819621859

Skeleton-based human activity recognition has become one of the research hotspots in the field of pattern recognition. However, existing methods have limitations in achieving fine-grained capture of human activity spatiotemporal features, such as (1) being unable to distinguish which neighboring nodes are important to the current node;(2) Unable to capture local temporal features. In this article, we utilize RFID, which has the advantages of privacy protection, identifiability, and battery free maintenance, to perceive human activities. We design a body RFID skeleton graph to express human activities and propose a body RFID skeleton spatiotemporal graph convolutional network (BRS-GCN) to capture the spatiotemporal features of the body RFID skeleton graph at a fine-grained level, thereby achieving human activity recognition. The recognition performance advantage of BRS-GCN compared to existing HARs has been verified through experiments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Musculoskeletal system

来源：评论

学校读者我要写书评

暂无评论

ASSD: An AI-Synthesized Speech Detection Scheme Using Whisper Feature and Types Classification

IEEE Transactions on Audio, Speech and Language Processing

引用

IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 542-556页

作者： Chang Liu Xiaolong Xu Fu Xiao Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

Current efforts in AI-synthesized speech detection often suffer from decreased performance due to detectors overfitting to irrelevant features or fitting features specific to certain synthesis methods. This issue becomes severe when facing unseen synthesis methods or real-world scenarios. To address these challenges, we first construct a multi-scenario dataset named PolyFake. Subsequently, we propose an AI-Synthesized Speech Detection Scheme using Whisper Feature and Types Classification (ASSD). In the feature processing stage, this method adopts a dual-stream network. Firstly, it utilizes the proposed SC_Encoder to separate and reconstruct redundant features, thereby reducing attention on irrelevant features. Meanwhile, it extracts features through a pre-trained Whisper_Encoder to augment feature generalization ability. Then, features extracted by the two encoders are decomposed, concatenated, and fused in both time and frequency domains, concurrently constructing time and frequency domain graphs. Through heterogeneous stacking graph attention, temporal and spectral information is fused to obtain generalized features. In the detection stage, employing a multi-task learning strategy, we introduce a multi-classification task for predicting synthesis method types to further segregate generalized features, obtaining common features of different synthesis methods for synthesized speech detection. The proposed method achieves lower equal error rate compared to state-of-the-art methods, with a relative improvement of 27% in generalization performance under a 2% increase in parameter count, on the In_the_wild and ASVspoof 2021 DF dataset and the testing scenarios of PolyFake.

关键词： Feature extraction Voice activity detection Training Computational modeling Speech enhancement Multitasking Accuracy Telecommunications Spectrogram Security

来源：评论

学校读者我要写书评

暂无评论

Uniform Attention Maps: Boosting Image Fidelity in Reconstruction and Editing

Uniform Attention Maps: Boosting Image Fidelity in Reconstru...

引用

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Wenyi Mo Tianyu Zhang Yalong Bai Bing Su Ji-Rong Wen Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Du Xiaoman Technology

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Text-guided image generation and editing using diffusion models have achieved remarkable advancements. Among these, tuning-free methods have gained attention for their ability to perform edits without extensive model adjustments, offering simplicity and efficiency. However, existing tuning-free approaches often struggle with balancing fidelity and editing precision. Reconstruction errors in DDIM Inversion are partly attributed to the cross-attention mechanism in U-Net, which introduces misalignments during the inversion and reconstruction process. To address this, we analyze reconstruction from a structural perspective and propose a novel approach that replaces traditional cross-attention with uniform attention maps, significantly enhancing image reconstruction fidelity. Our method effectively minimizes distortions caused by varying text conditions during noise prediction. To complement this improvement, we introduce an adaptive mask-guided editing technique that integrates seamlessly with our reconstruction approach, ensuring consistency and accuracy in editing tasks. Experimental results demonstrate that our approach not only excels in achieving high-fidelity image reconstruction but also performs robustly in real image composition and editing scenarios. This study underscores the potential of uniform attention maps to enhance the fidelity and versatility of diffusion-based image processing methods. Code is available at https://***/Mowenyii/Uniform-Attention-Maps.

关键词： Computer vision Accuracy Codes Image synthesis Noise Reconstruction algorithms Distortion Diffusion models Robustness Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Design, Performance, and Applications of AMMIS: A Novel Airborne Multimodular Imaging Spectrometer for High-Resolution Earth Observations

引用

Engineering 2025年第4期47卷 38-56页

作者： Jia, Jianxin Wang, Yueming Zheng, Xiaorou Yuan, Liyin Li, Chunlai Cen, Yi Si, Fuqi Lv, Gang Wang, Chongru Wang, Shengwei Zhang, Changxing Zhang, Dong He, Daogang Zhuang, Xiaoqiong Han, Guicheng Zhang, Mingyang Hyyppä, Juha Wang, Jianyu Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai200083 China Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute EspooFI-02150 Finland Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis Guangdong School of Computer Science and Engineering South China University of Technology Guangzhou510641 China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Environmental Optics and Technology Anhui Institute of Optics and Fine Mechanics Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China Department of Mechanical Engineering Aalto University EspooFI-02150 Finland

Airborne hyperspectral imaging spectrometers have been used for Earth observation over the past four decades. Despite the high sensitivity of push-broom hyperspectral imagers, they experience limited swath and wavelength coverage. In this study, we report the development of a push-broom airborne multimodular imaging spectrometer (AMMIS) that spans ultraviolet (UV), visible near-infrared (VNIR), shortwave infrared (SWIR), and thermal infrared (TIR) wavelengths. As an integral part of China's High-Resolution Earth Observation Program, AMMIS is intended for civilian applications and for validating key technologies for future spaceborne hyperspectral payloads. It has been mounted on aircraft platforms such as Y-5, Y-12, and XZ-60. Since 2016, AMMIS has been used to perform more than 30 flight campaigns and gather more than 200 TB of hyperspectral data. This study describes the system design, calibration techniques, performance tests, flight campaigns, and applications of the AMMIS. The system integrates UV, VNIR, SWIR, and TIR modules, which can be operated in combination or individually based on the application requirements. Each module includes three spectrometers, utilizing field-of-view (FOV) stitching technology to achieve a 40° FOV, thereby enhancing operational efficiency. We designed advanced optical systems for all modules, particularly for the TIR module, and employed cryogenic optical technology to maintain optical system stability at 100 K. Both laboratory and in-flight calibrations were conducted to improve preprocessing accuracy and produce high-quality hyperspectral data. The AMMIS features more than 1400 spectral bands, with spectral sampling intervals of 0.1 nm for UV, 2.4 nm for VNIR, 3 nm for SWIR, and 32 nm for TIR. In addition, the instantaneous fields of view (IFoVs) for the four modules were 0.5, 0.25, 0.5, and 1 mrad, respectively, with the VNIR module achieving an IFoV of 0.125 mrad in the high-spatial-resolution mode. This study reports on land

关键词： Fighter aircraft

来源：评论

学校读者我要写书评

暂无评论

Task Offloading Scheduling and Privacy Protection Optimization in Vehicular Edge Computing Based on Double Deep Q-Network 18th

Task Offloading Scheduling and Privacy Protection Optimizat...

引用

18th China Conference on Wireless Sensor Networks, CWSN 2024

作者： Weifeng, Lu Saijun, Yang Jia, Xu Lijie, Xu Lingyun, Jiang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Xianlin Street Jiangsu Nanjing210023 China

ISBN: (纸本)9789819621859

As the applications and services within vehicular networking systems become increasingly diverse, vehicles with limited computing resources face challenges in handling these computationally intensive and latency-sensitive tasks. In this paper, we propose an improved Task Offloading Scheduling Strategy based on a Double Deep Q-Network (TOSDDQN) for a dynamic multi-vehicle, multi-edge unit task offloading environment in vehicular networking. This strategy designs communication models and computational models aimed at minimizing the total system overhead in processing tasks. To further protect the privacy of vehicle users, a Distributed Training Algorithm based on Federated Learning (DTAFL) is proposed. Comparative experimental results indicate that, compared to three other task offloading schemes, our approach reduces the average system overhead in processing tasks by 11%–36%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computation offloading

来源：评论

学校读者我要写书评

暂无评论

Exploring the Robustness: Hierarchical Federated Learning Framework for Object Detection of UAV Cluster

引用

IEEE Transactions on Mobile Computing 2025年

作者： Li, Xingyu Zhang, Wenzhe Liu, Linfeng Xu, Jia Nanjing University of Posts and Telecommunications School of Computer Science and Technology Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

The deployment of Unmanned Aerial Vehicle (UAV) cluster is an available solution for object detection missions. In the harsh environment, UAV cluster could suffer from some significant threats (e.g., forest fire hazards, electromagnetic interference, and ground-to-air attacks), which could lead to the destruction of UAVs and loss of data. To this end, we propose a Hierarchical Federated Learning Framework for Object Detection (HFL-OD) to enhance the robustness of UAV cluster conducting object detection missions. In HFL-OD, UAVs are grouped through a Three-Dimensional (3D) graph coloring method, and an intragroup backup mechanism is provided to prevent the data loss caused by the destruction of UAVs. Besides, a dynamic server selection mechanism deals with the potential destruction of servers (cluster server and group servers) by adaptively reassigning the server roles. To further improve the robustness and mission efficiency of UAV cluster, a twotier federated learning framework is introduced to make a proper trade-off between object detection accuracy and communication/computational overhead. This framework is built on the concept of hierarchical federated learning by implementing both intragroup parameter aggregation and global parameter aggregation. Extensive simulations and comparisons demonstrate the superior performance of our proposed HFL-OD, i.e., the robustness of UAV cluster conducting object detection missions can be significantly improved, and the communication/computational overhead is effectively reduced. © 2002-2012 IEEE.

关键词： Fire hazards

来源：评论

学校读者我要写书评

暂无评论

UNIGS: UNIFIED LANGUAGE-IMAGE-3D PRETRAINING WITH GAUSSIAN SPLATTING

arXiv

引用

arXiv 2025年

作者： Li, Haoyuan Zhou, Yanpeng Tang, Tao Song, Jifei Zeng, Yihan Kampffmeyer, Michael Xu, Hang Liang, Xiaodan Shenzhen campus of Sun Yat-sen University China Huawei Noah’s Ark Lab Canada UiT The Arctic University of Norway Norway Peng Cheng Laboratory China Guangdong Key Laboratory of Big Data Analysis and Processing China

Recent advancements in multi-modal 3D pre-training methods have shown promising efficacy in learning joint representations of text, images, and point clouds. However, adopting point clouds as 3D representation fails to fully capture the intricacies of the 3D world and exhibits a noticeable gap between the discrete points and the dense 2D pixels of images. To tackle this issue, we propose UniGS, integrating 3D Gaussian Splatting (3DGS) into multi-modal pre-training to enhance the 3D representation. We first rely on the 3DGS representation to model the 3D world as a collection of 3D Gaussians with color and opacity, incorporating all the information of the 3D scene while establishing a strong connection with 2D images. Then, to achieve Language-Image-3D pertaining, UniGS starts with a pre-trained vision-language model to establish a shared visual and textual space through extensive real-world image-text pairs. Subsequently, UniGS employs a 3D encoder to align the optimized 3DGS with the Language-Image representations to learn unified multi-modal representations. To facilitate the extraction of global explicit 3D features by the 3D encoder and achieve better cross-modal alignment, we additionally introduce a novel Gaussian-Aware Guidance module that guides the learning of fine-grained representations of the 3D domain. Through extensive experiments across the Objaverse, ABO, MVImgNet and SUN RGBD datasets with zero-shot classification, text-driven retrieval and open-world understanding tasks, we demonstrate the effectiveness of UniGS in learning a more general and stronger aligned multi-modal representation. Specifically, UniGS achieves leading results across different 3D tasks with remarkable improvements over previous SOTA, Uni3D, including on zero-shot classification (+9.36%), text-driven retrieval (+4.3%) and open-world understanding (+7.92%). © 2025, CC BY-NC-SA.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：