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7th International Conference on Ubiquitous Positioning, Indoor Navigation and Location-Based Services, UPINLBS 2022

作者： Zhang, F. Ke, W. Ouyang, H. Qiu, S. Jiangsu Key Laboratory on Opto-Electronic Technology School of Computer and Electronic Information/School of Artificial Intelligence Nanjing Normal University Nanjing210023 China Jiangsu Ctr. for Collab. Innovation in Geographical Information Resource Development and Application Nanjing210023 China

This paper proposes an indoor visible light location method based on embedded platform and optical frequency image recognition technology with artificial intelligence*which can effectively improve the location effect in complex indoor environment. By transplanting the artificial intelligence (AI) based image classification algorithm into the embedded platform, this method uses a forward neural network to analyse the position information coming from the coded optical frequency image received by a camera, and then the positioning results can be obtained. In view of the "motion state"and "occlusion state"that are most likely to fail in traditional visible image localization, we specially supplement the training set of relevant characteristic optical frequency images to enhance the robustness of the method. According to the track and positioning results of the moving platform receiver, the proposed method can provide accurate and reliable navigation and positioning and has stronger anti-interference ability compared with the traditional light intensity or light image positioning methods. © 2022 Copernicus. All rights reserved.

关键词： Natural frequencies

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-...

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

作者： Kaijun Deng Dezhi Zheng Jindong Xie Jinbao Wang Weicheng Xie Linlin Shen Siyang Song Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Department of Computer Science University of Exeter

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code is available at https://***/CVI-SZU/DEGSTalk.

关键词： Hair Training Three-dimensional displays Dynamics Signal processing Rendering (computer graphics) Noise measurement Speech processing Faces Videos

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Multi-view Fusion Enhanced Social Text Representation for Depression Detection 11th

Multi-view Fusion Enhanced Social Text Representation for D...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Chen, Jie You, Xuetong Zhao, Shu Xiao, Jingxuan Zhang, Yanping Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Province Hefei230601 China School of Computer Science and Technology Anhui University Anhui Province Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Province Hefei230601 China Faculty of Innovation Engineering Department of Computer Science Macau University of Science and Technology Macau China

ISBN: (纸本)9783031927409

Depression has become a major global public health challenge. Social media texts reflect users’ emotional and psychological states, providing a valuable data source for depression detection through natural language analysis. However, current research still faces limitations in modeling the collaborative representation of user texts and dynamic behaviors. To address this, we propose MVDep, a model that integrates multi-view features to enhance social text representation for depression detection. Specifically, we enhance text representation by investigating depression from multi-view, including social media texts, user profiles, and social behaviors, enabling more effective depression detection. First, we conduct initial filtering of social media texts using the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and then screen out the uncertain domain of data samples based on the three-way decision (3WD). Next, we extract quantified user profiles and social behavioral features to enhance the representation of depression tendencies for uncertain domain samples. Finally, we fuse the multi-view features derived from social media texts, user profiles and social behaviors, improving the model’s ability to detect depression. We evaluate our model on the SWDD dataset, demonstrating its superiority over state-of-the-art baseline models in depression detection tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tweets

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MV-VTON: Multi-View Virtual Try-On with Diffusion Models

arXiv

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arXiv 2024年

作者： Wang, Haoyu Zhang, Zhilu Di, Donglin Zhang, Shiliang Zuo, Wangmeng State Key Laboratory of Multimedia Information Processing School of Computer Science Peking University China Harbin Institute of Technology China Space AI Li Auto China

The goal of image-based virtual try-on is to generate an image of the target person naturally wearing the given clothing. However, existing methods solely focus on the frontal try-on using the frontal clothing. When the views of the clothing and person are significantly inconsistent, particularly when the person’s view is non-frontal, the results are unsatisfactory. To address this challenge, we introduce Multi-View Virtual Try-ON (MV-VTON), which aims to reconstruct the dressing results from multiple views using the given clothes. Given that single-view clothes provide insufficient information for MV-VTON, we instead employ two images, i.e., the frontal and back views of the clothing, to encompass the complete view as much as possible. Moreover, we adopt diffusion models that have demonstrated superior abilities to perform our MV-VTON. In particular, we propose a view-adaptive selection method where hard-selection and soft-selection are applied to the global and local clothing feature extraction, respectively. This ensures that the clothing features are roughly fit to the person’s view. Subsequently, we suggest joint attention blocks to align and fuse clothing features with person features. Additionally, we collect a MV-VTON dataset MVG, in which each person has multiple photos with diverse views and poses. Experiments show that the proposed method not only achieves state-of-the-art results on MV-VTON task using our MVG dataset, but also has superiority on frontal-view virtual try-on task using VITON-HD and DressCode datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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BDLR:lncRNA identification using ensemble learning

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BIOCELL 2022年第4期46卷 951-960页

作者： LEJUN GONG SHEHAI ZHOU JINGMEI CHEN YONGMIN LI LI ZHANG ZHIHONG GAO Jiangsu Key Lab of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China

Long non-coding RNAs(lncRNAs)play an important role in many life activities such as epigenetic material regulation,cell cycle regulation,dosage compensation and cell differentiation regulation,and are associated with many human *** are many limitations in identifying and annotating lncRNAs using traditional biological experimental *** the development of high-throughput sequencing technology,it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning *** on the Bagging method and Decision Tree algorithm in ensemble learning,this paper proposes a method of lncRNAs gene sequence identification called *** identification results of this classification method are compared with the identification results of several models including Byes,Support Vector Machine,Logical Regression,Decision Tree and Random *** experimental results show that the lncRNAs identification method named BDLR proposed in this paper has an accuracy of 86.61%in the human test set and 90.34%in the mouse for lncRNAs,which is more than the identification results of the other ***,the proposed method offers a reference for researchers to identify lncRNAs using the ensemble learning.

关键词： lncRNAs High-throughput sequencing Ensemble learning Bagging Decision Tree

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HalluDial: A Large-Scale Benchmark for Automatic Dialogue-Level Hallucination Evaluation

arXiv

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arXiv 2024年

作者： Luo, Wen Shen, Tianshu Li, Wei Peng, Guangyue Xuan, Richeng Wang, Houfeng Yang, Xi Beijing Academy of Artificial Intelligence China National Key Laboratory for Multimedia Information Processing Peking University China Wangxuan Institute of Computer Technology Peking University China

Large Language Models (LLMs) have significantly advanced the field of Natural Language processing (NLP), achieving remarkable performance across diverse tasks and enabling widespread real-world applications. However, LLMs are prone to hallucination, generating content that either conflicts with established knowledge or is unfaithful to the original sources. Existing hallucination benchmarks primarily focus on sentence- or passage-level hallucination detection, neglecting dialogue-level evaluation, hallucination localization, and rationale provision. They also predominantly target factuality hallucinations while underestimating faithfulness hallucinations, often relying on labor-intensive or non-specialized evaluators. To address these limitations, we propose HalluDial, the first comprehensive large-scale benchmark for automatic dialogue-level hallucination evaluation. HalluDial encompasses both spontaneous and induced hallucination scenarios, covering factuality and faithfulness hallucinations. The benchmark includes 4,094 dialogues with a total of 146,856 samples. Leveraging HalluDial, we conduct a comprehensive meta-evaluation of LLMs' hallucination evaluation capabilities in information-seeking dialogues and introduce a specialized judge language model, HalluJudge. The high data quality of HalluDial enables HalluJudge to achieve superior or competitive performance in hallucination evaluation, facilitating the automatic assessment of dialogue-level hallucinations in LLMs and providing valuable insights into this phenomenon. The dataset and the code are available at https://***/FlagOpen/HalluDial. Copyright © 2024, The Authors. All rights reserved.

关键词： Quality control

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Efficient Frame Optimization Scheme for PSAM Assisted Transmission over Rician Fading Channel

Efficient Frame Optimization Scheme for PSAM Assisted Transm...

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information Communication and Software Engineering (ICICSE), IEEE International Conference on

作者： Yuqian Yan Zhongyang Yu Mengmeng Xu Hengzhou Xu Hai Zhu Chunhua Zhu School of Computer Science Henan University of Engineering Zhengzhou China Key Laboratory of Grain Information Processing Control (Henan University of Technology) Ministry of Education Zhengzhou China

ISBN: (数字)9798350351699

ISBN: (纸本)9798350351705

In this paper, we discuss the commonly used pilot-symbol-assisted-modulation (PSAM) assisted transmission and its corresponding frame optimization problem over Rician fading channel. Specifically, we design a generalized PSAM (G-PSAM) format based on the standard PSAM (S-PSAM) format and derive its approximate joint data-aided & non-data-aided Cramér-Rao bound (DA&NDA CRB) when the pilot-aided channel estimation is considered. Then, with the approximate DA&NDA CRB and the classical control-variate method (CVM), an efficient optimization scheme of the G-PSAM format is proposed, which is asymptotically optimal in terms of the mean-square error (MSE) estimation performance. Simulation results reveal the superiority of the optimized G-PSAM (OG-PSAM) format over the S-PSAM format for both short-packet and long-packet transmissions. Also, a practical example of the joint DA&NDA aided carrier synchronization is provided.

关键词： Fading channels Simulation Rician channels Estimation Channel estimation Mean square error methods Synchronization

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Character-Level Street View Text Spotting Based on Deep Multisegmentation Network for Smarter Autonomous Driving

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 297-308页

作者： Zhang, Chongsheng Tao, Yuefeng Du, Kai Ding, Weiping Wang, Bin Liu, Ji Wang, Wei Henan Provincial Key Laboratory of Big Data Analysis and Processing Laboratory of the Yellow River Cultural Heritage Henan University Kaifeng475001 China School of Information Science and Technology Nantong University Nantong226019 China School of Electrical Engineering Zhejiang University Hangzhou310058 China Big Data Lab Baidu Inc. Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

Urban scenes are full of street entities with sign boards. Therefore, in autonomous driving, street view text spotting techniques will play a significant role in the precise understanding of surrounding scenes during driving, because texts contained in the images usually provide important clues for accurate image understanding, while it is often ambiguous for existing computer vision algorithms to understand scene images without texts. In this work, we propose a Multi-Segmentation network for character-level scene Text Detection (MSTD). The MSTD introduces a densely connected atrous spatial pyramid pooling module to enlarge the receptive field of the feature extraction layer, so as to localize long as well as large-sized text instances. Moreover, it devises a double segmentation subnetwork to utilize two independent but inherently complementary losses to co-optimize the network and increase the reliability of the confidence scores in predicting the text/nontext areas. With the character instances detected by the MSTD, one can easily perform scene text spotting with classic object recognition networks such as ResNet and DenseNet. We carried out extensive experiments on nine scene text datasets to demonstrate the outstanding performance of the MSTD on character-level and line-level text instance localization and scene text recognition, where the MSTD significantly outperforms the state-of-the-art scene text detection methods and the sequence-to-sequence-learning-based scene text recognizers. © 2020 IEEE.

关键词： Character recognition

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A Multi-Channel Composite Recommendation Method in Federated Learning

A Multi-Channel Composite Recommendation Method in Federated...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Chen, Shi Li, Qianmu Wang, Qi Zhao, Xinjian Ge, Hao Li, Nianzhe State Grid Jiangsu Information & Telecommunication Company Nanjing China School of Computer Science Ana Engineering Nanjing University of Science and Technology Nanjing China State Grid Smart Grid Research Institute Co. Ltd. State Grid Key Laboratory of Information & Network Security Nanjing China

ISBN: (纸本)9798350304602

There are various data security risks in power grid systems, and recommendation systems are a good solution to the selection of security strategies. Federated learning is a machine learning framework that can protect the data privacy of each participant and can effectively solve the data security external collaborative training modeling challenges. This paper proposed a flexible implantable recommendation algorithm based on knowledge graph and multi-channel composite to enhance federated learning on demand. The algorithm designs three different feature extraction modules to learn to construct multi-dimensional features on multi-attribute collaborative knowledge graphs. Among them, the distance translation module utilizes the Trans R recommendation algorithm to learn the internal semantic structure information in knowledge graph triples and terminal-security policy selection history;the random walk module uses the Node2Vec algorithm to learn the homogeneous and Structured information in the multi-attribute collaborative knowledge graph;the graph convolutional network module learns the high-order neighborhood multi-attribute collaborative information of terminal/security policy nodes through multi-layer feature propagation and aggregation. The composite network module adopts a convolutional neural network-like operation to first locally optimize the three modules, and then extracts the highly informative elements to obtain the final feature representation of the terminal/security policy. Finally, the algorithm merges the vectors of terminal-security policy pairs into the multi-layer perceptron to learn and calculate the prediction score for recommendation. © 2023 IEEE.

关键词： Recommender systems

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LuminAIRe: illumination-aware conditional image repainting for lighting-realistic generation 23

LuminAIRe: illumination-aware conditional image repainting f...

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Proceedings of the 37th International Conference on Neural information processing Systems

作者： Jiajun Tang Haofeng Zhong Shuchen Weng Boxin Shi National Key Laboratory for Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University

We present the ilLumination-Aware conditional Image Repainting (LuminAIRe) task to address the unrealistic lighting effects in recent conditional image repainting (CIR) methods. The environment lighting and 3D geometry conditions are explicitly estimated from given background images and parsing masks using a parametric lighting representation and learning-based priors. These 3D conditions are then converted into illumination images through the proposed physically-based illumination rendering and illumination attention module. With the injection of illumination images, physically-correct lighting information is fed into the lighting-realistic generation process and repainted images with harmonized lighting effects in both foreground and background regions can be acquired, whose superiority over the results of state-of-the-art methods is confirmed through extensive experiments. For facilitating and validating the LuminAIRe task, a new dataset CAR-LUMINAIRE with lighting annotations and rich appearance variants is collected.

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