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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Changsheng Chen Liangwei Lin Yongqi Chen Bin Li Jishen Zeng Jiwu Huang Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Alibaba Group Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen MSU-BIT University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The rebroadcasting of screen-recaptured document images introduces a significant risk to the confidential docu-ments processed in government departments and commer-cial companies. However, detecting recaptured document images subjected to distortions from online social networks (OSNs) is challenging since the common forensics cues, such as moiré pattern, are weakened during transmission. In this work, we first devise a pixel-level distortion model of the screen-recaptured document image to identify the robust features of color artifacts. Then, we extract a chromaticity map from the recaptured image to highlight the presence of color artifacts even under low-quality samples. Based on the prior understanding, we design a chromaticity map adapter (CMA) to efficiently extract the chromaticity map, and feed it into the transformer backbone as multi-modal prompt tokens. To evaluate the performance of the pro-posed method, we collect a recaptured office document im-age dataset with over 10K diverse samples. Experimental results demonstrate that the proposed CMA method outper-forms a SOTA approach (with RGB modality only), reducing the average EER from 26.82% to 16.78%. Robustness eval-uation shows that our method achieves 0.8688 and 0.7554 AUCs under samples with JPEG compression $(QF=70)$ and resolution as low as $534\times 503$ pixels.

关键词： Image color analysis Social networking (online) Forensics Splicing Transform coding Feature extraction Distortion

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An Energy-Efficient Virtual Machine Placement Algorithm Based on Improved Grouping GA

An Energy-Efficient Virtual Machine Placement Algorithm Base...

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Taisheng Zeng Xiaodong Wu School of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Quanzhou China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

Virtual machine placement (VMP) plays a crucial role in optimizing the efficiency and sustainability of data centers. By consolidating virtual machines (VMs) onto fewer physical machines (PMs), VMP can significantly reduce power consumption and enhance resource utilization. This not only leads to cost savings but also contributes to environmental sustainability by minimizing energy waste. This paper focuses on addressing the energy-efficient VMP problem, which is critical for modern data centers aiming to balance performance and energy consumption. To tackle this challenge, we propose an improved group-based genetic algorithm (GGA). The GGA employs a novel grouping approach to encode chromosomes, allowing for more effective representation of VM-to-PM assignments. Additionally, optimized crossover and mutation operators are designed to improve the algorithm's performance, ensuring faster convergence and better solutions. Experimental results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of energy-saving efficiency and convergence speed.

关键词： Data centers Power demand Virtual machines Energy efficiency Resource management Sustainable development Biological cells Optimization Genetic algorithms Convergence

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M3GPT: an advanced multimodal, multitask framework for motion comprehension and generation 24

M3GPT: an advanced multimodal, multitask framework for motio...

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Proceedings of the 38th International Conference on Neural Information processing Systems

作者： Mingshuang Luo Ruibing Hou Zhuo Li Hong Chang Zimo Liu Yaowei Wang Shiguang Shan Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS China and Peng Cheng Laboratory China and University of Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS China WeChat Tencent Inc Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS China and University of Chinese Academy of Sciences China Peng Cheng Laboratory China Peng Cheng Laboratory China and Harbin Institute of Technology Shenzhen

ISBN: (纸本)9798331314385

This paper presents M3 GPT, an advanced Multimodal, Multitask framework for Motion comprehension and generation. M3 GPT operates on three fundamental principles. The first focuses on creating a unified representation space for various motion-relevant modalities. We employ discrete vector quantization for multimodal conditional signals, such as text, music and motion/dance, enabling seamless integration into a large language model (LLM) with a single vocabulary. The second involves modeling motion generation directly in the raw motion space. This strategy circumvents the information loss associated with a discrete tokenizer, resulting in more detailed and comprehensive motion generation. Third, M3 GPT learns to model the connections and synergies among various motion-relevant tasks. Text, the most familiar and well-understood modality for LLMs, is utilized as a bridge to establish connections between different motion tasks, facilitating mutual reinforcement. To our knowledge, M3 GPT is the first model capable of comprehending and generating motions based on multiple signals. Extensive experiments highlight M3GPT's superior performance across various motion-relevant tasks and its powerful zero-shot generalization capabilities for extremely challenging tasks. Project page: https://***/M3GPT/.

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A framework for Chinese event semantic constraint predicates and their generation using PEFT LLM and RAG

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International Journal of Machine Learning and Cybernetics 2025年

作者： Huang, Qiaojuan Wang, Shi He, Qing Cao, Cungen Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Event semantic analysis is a crucial area of research in natural language processing. It focuses on deeply understanding the semantics of events and their components. While existing semantic frameworks and event semantic labelling cover a wealth of semantic information, further research is needed to explore the fine-grained semantic relationships across participants in different events. To address this problem, we construct a head–tail event semantic constraint predicate framework that relates the complex semantics between different event components. However, considering the large-scale data, an automated method for generating constraint predicates is essential. Therefore, we combine parameter-efficient fine-tuning (PEFT) of large language models (LLMs) with retrieval-augmented generation (RAG) technologies. Specifically, this paper makes four significant contributions. Firstly, we construct a detailed framework of head–tail events semantic constraint predicates by combining manual definitions with the GPT-4o model. The framework includes 117 types of constraint predicates between head–tail events. Secondly, based on this framework, we annotate a dataset of head–tail events semantic constraint predicates and apply it to PEFT LLMs. Thirdly, we use PEFT LLMs prediction error data to construct an event knowledge base covering event entities, semantic roles, and entity types. Fourth, using the event knowledge base, the PEFT LLMs integrate the dual RAG techniques of keywords and vectors to improve the accuracy of generating the constraint predicates. Experimental results show that the method outperforms the baselines, achieving a precision of 97.18% and recall of 97.39%. This research fills the gap in semantic relationships between components across events and provides a novel approach for complex semantic generation tasks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Domain Knowledge

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Sparse Andrew's Sine Norm Promoting Adaptive Algorithm under Non-Gaussian Noises 5

Sparse Andrew's Sine Norm Promoting Adaptive Algorithm under...

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5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Hadi, Abdul Huang, Xinqi Ali, Burhan Li, Yingsong College of Information and Communications Engineering Harbin Engineering University Harbin150001 China College of Intelligent System and Control Science Engineering Harbin Engineering University Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Hefei China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

Two sparse adaptive filtering (AF) algorithms based on Andrew's sine estimator (ASE) are presented to achieve improved performance for identifying sparse systems, where the ASE is derived within the least-square framework. Furthermore, zero-attracting (ZA) scheme is used in ASE to construct ZA-ASE and its re-weighting form (RZA-ASE) to combat non-Gaussian noises and use the sparse characteristics of the system. Their performance is investigated via simulations and compared with the least-mean square (LMS) and the maximum correntropy criterion (MCC) algorithms to show their superior performance. © 2022 IEEE.

关键词： Adaptive filtering

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Efficient Finite-Difference Time-Domain Method for Solving Electromagnetic Scattering in Layered Anisotropies

Efficient Finite-Difference Time-Domain Method for Solving E...

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Yilin Kang Yuxian Zhang Xiaoli Feng Lixia Yang Zhixiang Huang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education and with the Information Materials and Intelligent Sensing Laboratory of Anhui Province Industry-Education-Research Institute of Advanced Materials and Technology for Integrated Circuits and with School of Electronic and Information Engineering Anhui University Hefei China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

In this article, we propose an efficient numerical method for layered models based on the conventional finite difference time-domain (FDTD) method. The three-dimensional (3-D) layered model is compressed into one-dimensional (1-D) calculation, which improves the problem of large space memory and long running time for 3-D modeling while ensuring the accuracy and feasibility of the algorithm. On the one hand, by employing the transformation relationship between the transverse wave vector and the field in space, the expanded Maxwell's rotation equations are simplified, so that the equation system only retains the spatial transformation in the propagation direction. This processing allows for the calculation of the propagation characteristics of layered media without requiring more computational resources. On the other hand, to ensure the universality of the algorithm, a fully tensor anisotropic medium is selected for detecting the propagation data. At the same time, we design the layered structure and conduct the consistent 3-D modeling in the simulation software COMSOL. The results showed that our proposed efficient numerical algorithm greatly reduced the loss of computing resources while ensuring accuracy.

关键词： Solid modeling Three-dimensional displays Accuracy Tensors Software algorithms Vectors Mathematical models Numerical models Time-domain analysis Finite difference methods

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A Novel Multi-band EBG Structure Using Nested Square Grooves 5

A Novel Multi-band EBG Structure Using Nested Square Grooves

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5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Sun, Guoxing Ren, Xingang Ju, Huping Xueyuan, Cai He, Shuping Xianliang, Wu Huang, Zhixiang Anhui University Key Laboratory of Target Recognition and Feature Extraction of Anhui Province Lu'an China Key Laboratory of Electromagnetic Environmental Sensing of Anhui Province Anqing Normal Unverisity Anqing China Ministry of Education Anhui University Key Laboratory of Intelligent Computing & Signal Processing Hefei China Anhui University Key Laboratory of Electromagnetic Environmental Sensing of Anhui Province Hefei China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

A nested rectangle electromagnetic bandgap structure with dual broadband band is designed to enhance the performance of telecom/mobile 5G antennas operating at 3.3GHz and 4.9GHz, which has a short frequency spacing of about 1.6GHz. through loading EBG structure under dipole antenna, directional radiation, low profile and high gain performance have been achieved. The distance between electromagnetic bandgap (EBG) structure and antenna is only 0.067 wavelength, the first band and the second band of EBG are greatly expanded by etching nested square grooves around the EBG (NSGEBG) as compared with traditional etched ring groove EBG (ERGEBG). The bandwidth of the first band of 0.39GHz is twice wider than that of the ring groove of 0.2GHz, the second band has a bandwidth of 0.46GHz, which perfectly solves the problem that the spacing of the two operating frequency is greater than 2GHz. © 2022 IEEE.

关键词： Energy gap

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SSFam: Scribble Supervised Salient Object Detection Family

arXiv

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arXiv 2024年

作者： Liu, Zhengyi Deng, Sheng Wang, Xinrui Wang, Linbo Fang, Xianyong Tang, Bin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China

Scribble supervised salient object detection (SSSOD) constructs segmentation ability of attractive objects from surroundings under the supervision of sparse scribble labels. For the better segmentation, depth and thermal infrared modalities serve as the supplement to RGB images in the complex scenes. Existing methods specifically design various feature extraction and multi-modal fusion strategies for RGB, RGB-Depth, RGB-Thermal, and Visual-Depth-Thermal image input respectively, leading to similar model flood. As the recently proposed Segment Anything Model (SAM) possesses extraordinary segmentation and prompt interactive capability, we propose an SSSOD family based on SAM, named SSFam, for the combination input with different modalities. Firstly, different modal-aware modulators are designed to attain modal-specific knowledge which cooperates with modal-agnostic information extracted from the frozen SAM encoder for the better feature ensemble. Secondly, a siamese decoder is tailored to bridge the gap between the training with scribble prompt and the testing with no prompt for the stronger decoding ability. Our model demonstrates the remarkable performance among combinations of different modalities and refreshes the highest level of scribble supervised methods and comes close to the ones of fully supervised methods. https://***/liuzywen/SSFam Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Personality recognition based on ECG in response to VR emotional stimuli 23

Personality recognition based on ECG in response to VR emoti...

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3rd International Conference on Bioinformatics and intelligent computing, BIC 2023

作者： Lan, Ping Xie, Jialan Hu, Zhaonian Liu, Guangyuan College of Electronic and Information Engineering Southwest University Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing400715 China Institute of Affective Computing and Information Processing Southwest University Chongqing400715 China Key Laboratory of Cognition and Personality Ministry of Education Faculty of Psychology Southwest University Chongqing400715 China

ISBN: (纸本)9798400700200

This paper explores the feasibility and effect of using ECG signals in response to virtual reality(VR) emotional stimuli for personality recognition. Personality represents an individual''s behavior pattern, so it has extensive research significance to identify personality. In the field of personality recognition, there have been researches on the use of physiological signals to recognize personality recently, but there have been no studies on personality recognition in VR scenarios. VR technology has been applied in many fields, and in the field of emotion recognition, the advantage of VR video as emotional arousal material has been proved. We designed the emotional stimulation experiment in VR scene to collect ECG signals, and constructed the personality recognition model. The results showed that the F1 scores of neuroticism, extraversion, openness, agreeableness and conscientiousness using only ECG were 0.79, 0.80, 0.81, 0.73 and 0.81, respectively, which were much higher than the random level. And we found that in VR scenes, negative emotional stimulation can achieve better personality recognition effect. Our results confirm the feasibility of personality recognition using ECG signals in response to VR emotional stimuli. © 2023 ACM.

关键词： Electrocardiograms

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Talents Training in Antenna Engineering 5

Talents Training in Antenna Engineering

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5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Li, Yingsong Huang, Zhixiang Jiang, Tao Li, Yibing Ye, Fang Chung, Kwok L. College of Information and Communication Engineering Harbin Engineering University Harbin15001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China School of Computer Science and Engineering Huizhou University Huizhou516007 China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

We propose the antenna developments from nature to engineering, which is a natural method to train student for designing antenna. Also, the interest of the students can be stimulated. Using this method, the students can get how to design antennas and will be interested on the antenna engineering. Then, young talents can be brought up. By using this strategy, a nature Logo is introduced and cooperated with the proposed talent training method to get new antennas. Also, the results for the developed antenna is also simulated and analyzed based on the theory from the text book. In the future, the proposed talents training techniques or method can also be expanded to other different research directions. © 2022 IEEE.

关键词： Antennas

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