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10th IEEE International Conference on Computer Science and Network Technology, ICCSNT 2022

作者： Li, Yingsong Huang, Zhixiang Wang, Meiyu Jiang, Tao Sinkevich, Eugene Mordachev, Vladimir 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 Belarusian State University of Informatics and Radioelectronics Emc RandD Laboratory Minsk Belarus

ISBN: (数字)9781665470612

ISBN: (纸本)9781665470612

We present the excellent student training scheme in shielding effectiveness analysis with international cooperation of Belarus. As we know, the shielding is vital for modern information devices due to the radiating around us. Herein, the shielding effectiveness is important for training excellent students to master the design of the frequency selective components. In this method, we teach the students to use the EM simulation tools to construct different EM structures for obtaining the shielding, and the performance of these structures are verified from the simulations and measurements. Using this shielding effectiveness training scheme, a frequency selective surface is presented, simulated and discussed to analyze its performance. In future, the excellent student training methods given herein for shielding effectiveness can be expanded to develop other EM structures. © 2022 IEEE.

关键词： International cooperation

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ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation Recovery using Limited Data

ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation...

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IEEE International Conference on Communications in China (ICCC)

作者： Doncheng Yuan Jianzhe Xue Jinshan Su Wenchao Xu Haibo Zhou School of Electronic Science and Engineering Nanjing University Nanjing China Key Laboratory of Vibration Signal Capture and Intelligent Processing Yili Normal University Yining China Department of Computing the Hong Kong Polytechnic University

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Traffic flow estimation (TFE) is crucial for urban intelligent traffic systems. While traditional on-road detectors are hindered by limited coverage and high costs, cloud computing and data mining of vehicular network data, such as driving speeds and GPS coordinates, present a promising and cost-effective alternative. Furthermore, minimizing data collection can significantly reduce overhead. However, limited data can lead to inaccuracies and instability in TFE. To address this, we introduce the spatial-temporal Mamba (ST-Mamba), a deep learning model combining a convolutional neural network (CNN) with a Mamba framework. ST-Mamba is designed to enhance TFE accuracy and stability by effectively capturing the spatial-temporal patterns within traffic flow. Our model aims to achieve results comparable to those from extensive data sets while only utilizing minimal data. Simulations using real-world datasets have validated our model’s ability to deliver precise and stable TFE across an urban landscape based on limited data, establishing a cost-efficient solution for TFE.

关键词： Deep learning Accuracy Simulation Estimation Detectors Power system stability Data models

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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 Hefei 230601 China School of Computer Science and Technology Anhui University Anhui Province Hefei 230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Province Hefei 230601 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： 3WD Depression Detection Multi-View Feature Fusion Social Media Texts

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Conditional Generative Adversarial Defogging Algorithm Based on Polarization Characteristics 33

Conditional Generative Adversarial Defogging Algorithm Based...

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第33届中国控制与决策会议

作者： Jingjing Zhang Kangsheng Bao Xin Zhang Fudong Nian Teng Li Yuzhou Zeng Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education (Anhui university) Key Laboratory of Polarized Light Imaging and Detection Technology in Anhui Province Hefei No.6 High School Hefei University

ISBN: (纸本)9781665431293

To overcome image degradation under the conditions of haze,and fog,a method using conditional generative adversarial defogging algorithm based on polarization characteristic is *** original images with different polarization angles were obtained from the original image,and then the polarization images were characterized using Stokes *** the relationship between Stokes vector and polarization image,each polarization image with different angles is input into the same network to extract *** the same time,the enhanced network is used to extract the characteristics of the fog area in the polarization image,and the polarization information is extracted by layer jump connection,which is fused with the image features of different *** constructing the loss function and obtaining the optimal solution,the fog-free image is finally *** results show that a clear image can be reconstructed in fog situations by using conditional generative adversarial network-based on polarization characteristics,and the structural similarity is improved by about 10%,the peak signal to noise ratio is increased by about 0.5.

关键词： polarization characteristics conditional generative adversarial network image processing image defogging feature fusion enhanced network

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On four conjectures of Heng-Ding and p-ary linear codes from monomials

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IEEE Transactions on Information Theory 2025年

作者： Qian, Liqin Shi, Minjia Anhui University School of Mathematical Sciences Anhui Hefei230601 China Anhui University Key Laboratory of Intelligent Computing Signal Processing Ministry of Education School of Mathematical Sciences Anhui Hefei230601 China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China

Subfield codes of linear codes over finite fields have recently attracted great attention due to their wide applications in secret sharing schemes, authentication codes and association schemes. There are two major ingredients in this paper. The first ingredient is to solve four conjectures recently proposed by Heng and Ding (IEEE Trans. Inf. Theory, 68(6): 3643-3656, 2022). Besides, we also determine the weight distributions of subfield codes derived from x3 and oval polynomials over F2m. The second ingredient is to obtain two classes of p-ary linear codes from monomials over Fpm. We study the parameters of the constructed codes and determine their weight distributions. Notably, we show that the codes C(p) (xpi, pm) are optimal and almost optimal in many cases with respect to the online Database of Grassl. Finally, we observe that the derived linear codes also have the minimality property for most cases. © 1963-2012 IEEE.

关键词： Linear code monomial oval polynomial subfield code weight distribution

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Suppress content shift: better diffusion features via off-the-shelf generation techniques 24

Suppress content shift: better diffusion features via off-th...

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

作者： Benyuan Meng Qianqian Xu Zitai Wang Zhiyong Yang Xiaochun Cao Qingming Huang Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is available at https://***/Darkbblue/diffusion-content-shift.

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Imputation of Compound Property Assay Data Using a Gene Expression Programming-Based Method 1st

Imputation of Compound Property Assay Data Using a Gene Expr...

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1st International Conference on Applied Intelligence, ICAI 2023

作者： Zhou, Hongliang Lin, Yanmei Chen, Nan Peng, Yuzhong Key Lab of Scientific Computing and Intelligent Information Processing Nanning Normal University Guangxi Nanning5300001 China Guangxi Academy of Sciences Guangxi Nanning530007 China

ISBN: (纸本)9789819709021

Compound property assays are an important part of drug development, but incomplete data may occur for a variety of reasons. To deal with these incomplete data and improve the success rate of drug development, researchers often need to effectively impute the missing data. Therefore, this paper proposes a gene expression programming-based method, called GEP-CPI, for imputing missing compound property assay data. In GEP-CPI, the missing data imputation model is expressed by the parse tree of a chromosome, and then the optimal missing data imputation model is mined by iterative evolution of the chromosome population. Experimental results on three compound property assay related datasets demonstrates that the proposed method generally outperforms the state-of-the-art methods in imputing missing data of compound property assays. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Trees (mathematics)

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Achieving More with Less: Additive Prompt Tuning for Rehearsal-Free Class-Incremental Learning

arXiv

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arXiv 2025年

作者： Chen, Haoran Wang, Ping Zhou, Zihan Zhang, Xu Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China APUS AI Lab China

Class-incremental learning (CIL) enables models to learn new classes progressively while preserving knowledge of previously learned ones. Recent advances in this field have shifted towards parameter-efficient fine-tuning techniques, with many approaches building upon the framework that maintains a pool of learnable prompts. Although effective, these methods introduce substantial computational overhead, primarily due to prompt pool querying and increased input sequence lengths from prompt concatenation. In this work, we present a novel prompt-based approach that addresses this limitation. Our method trains a single set of shared prompts across all tasks and, rather than concatenating prompts to the input, directly modifies the CLS token’s attention computation by adding the prompts to it. This simple and lightweight design not only significantly reduces computational complexity—both in terms of inference costs and the number of trainable parameters—but also eliminates the need to optimize prompt lengths for different downstream tasks, offering a more efficient yet powerful solution for rehearsal-free class-incremental learning. Extensive experiments across a diverse range of CIL benchmarks demonstrate the effectiveness of our approach, highlighting its potential to establish a new prompt-based CIL paradigm. Furthermore, experiments on general recognition benchmarks beyond the CIL setting also show strong performance, positioning our method as a promising candidate for a general parameter-efficient fine-tuning approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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No-Reference Image Quality Assessment Based on Active Reasoning Module 6

No-Reference Image Quality Assessment Based on Active Reason...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Qi, Junwei Deng, Yuhao Wang, Qingchun Yang, Zhen Li, Yingsong College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Shanghai Yanding Information Technology Co. Ltd. Shanghai201210 China College of Computer Science and Technology Harbin Engineering University Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350399059

We present a no-reference image-quality - assessment algorithm based on active reasoning module. This algorithm has three modules: the feature extraction module, the active reasoning module, and the quality assessment module. The active reasoning module incorporates the generator component of the generative adversarial network and enhances its structure with the Res2Net architecture. By integrating this module into the backbone feature extraction network, we improve the receptive field of each convolutional layer, enabling the network to capture information at different scales of the image. To preserve the texture information of the image, we input the gradient map of the distorted image, the distorted image itself, and the image features generated by the generator into the quality assessment module, which has a multi-feature regression networks. This module establishes a mapping model from image-feature to image-quality scores. We conducted experimental analysis of this algorithm on three widely used public datasets, confirming the excellent performance and superiorities of the presented algorithm. The results validate the effectiveness of the designed algorithm and its ability to assess image quality accurately. © 2023 IEEE.

关键词： Image quality

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Double-Local Conditional Probability Based Fast Calculation Method for Approximation Regions of Probabilistic Rough Sets

SSRN

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SSRN 2023年

作者： Xu, Yi Zhou, Meng Sun, Weikang School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China

As an extended rough set model, the probabilistic rough sets can effectively process data with noise. How to effectively calculate three approximation regions, is a crucial issue of probability rough sets. Existing calculation methods for approximation regions are based on conditional probability, the time complexity is O(|U||U||C|). In order to improve the computing efficiency of three approximation regions of probabilistic rough sets, we propose a double-local conditional probability based fast calculation method. First, to improve the computational efficiency of equivalence class, we define the double-local equivalence class. Second, based on the double-local equivalence class, we define the double-local conditional probability. Finally, given the probability thresholds and a local equivalence class, the monotonicity of double-local conditional probability is proved, on this basis, a double-local conditional probability based fast calculation method for approximation regions of probabilistic rough sets is presented, and the time complexity is O(MAX(X||U||C|, |XC||U||C|)). Experimental results based on 12 datasets from UCI demonstrate the effectiveness of the proposed method. © 2023, The Authors. All rights reserved.

关键词： Equivalence classes

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