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

作者： Feifei Zhang Fei Shi Dayong Ren Yue Li School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the field of medical image processing, the presence of noise can often result in the obfuscation of crucial details, which in turn can have a detrimental impact on the accuracy of clinical diagnoses. In order to effectively remove noise and improve segmentation performance, this paper proposes a refined Fuzzy C-Means (FCM) algorithm, designated as MKL-FCM. The method commences with Poisson denoising, which enables more effective handling of the noise characteristics inherent to medical images. Subsequently, multi-scale Kullback-Leibler divergence is utilised to analyse local image information across varying scales, facilitating the differentiation between tissues and pathological regions. Furthermore, tight wavelet frames are capable of capturing fine image details, while reverse optimisation of the objective function serves to correct errors in feature reconstruction, thereby enhancing the accuracy of the segmentation process. The experimental results demonstrate that MKL-FCM enhances both image clarity and segmentation accuracy, and outperforms existing methods in terms of efficiency.

关键词： Image segmentation Accuracy Noise reduction Noise Signal processing algorithms Clustering algorithms Linear programming Partitioning algorithms Speech processing Medical diagnostic imaging

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Adaptive information fusion network for multi-modal personality recognition

Adaptive information fusion network for multi-modal personal...

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作者： Bao, Yongtang Liu, Xiang Qi, Yue Liu, Ruijun Li, Haojie School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Virtual Reality Research Institute Beihang University Qingdao Research Institute Qingdao China School of Software Beihang University Beijing China

Personality recognition is of great significance in deepening the understanding of social relations. While personality recognition methods have made significant strides in recent years, the challenge of heterogeneity between modalities during feature fusion still needs to be solved. This paper introduces an adaptive multi-modal information fusion network (AMIF-Net) capable of concurrently processing video, audio, and text data. First, utilizing the AMIF-Net encoder, we process the extracted audio and video features separately, effectively capturing long-term data relationships. Then, adding adaptive elements in the fusion network can alleviate the problem of heterogeneity between modes. Lastly, we concatenate audio-video and text features into a regression network to obtain Big Five personality trait scores. Furthermore, we introduce a novel loss function to address the problem of training inaccuracies, taking advantage of its unique property of exhibiting a peak at the critical mean. Our tests on the ChaLearn First Impressions V2 multi-modal dataset show partial performance surpassing state-of-the-art networks. © 2024 John Wiley & Sons Ltd.

关键词： Information fusion

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An Investigation of Blockchain-Based Sharding 7th

An Investigation of Blockchain-Based Sharding

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Xiao, Jiahong Liang, Wei Cai, Jiahong Zhu, Hangyu Li, Xiong Xie, Songyou School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (纸本)9783031281235

Nowadays, blockchain distributed ledger technology is becoming more and more prominent, and its decentralization, anonymization, and tampering obvious features have been widely recognized. These excellent technical features of blockchain have also made it a hot issue for global research. With the wide application of blockchain technology in various industries, some defects are gradually exposed, and more prominently, the blockchain system is unable to meet the current demand of explosive growth of data volume and frequent data interaction. As one of the key technologies to solve this problem, sharding technology is gaining attention. This article introduces common blockchain scaling schemes and focuses on an overview of blockchain sharding. Sharding technology is introduced from two perspectives of intra-slice consensus and inter-slice consensus. The current mainstream slicing technology is summarized according to three different slicing methods: network sharding, transaction sharding, and state sharding. Finally, the challenges faced by current blockchain sharding technology are analyzed and the full text is summarized. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Integrating Extractive and Abstractive Models for Code Comment Generation

Integrating Extractive and Abstractive Models for Code Comme...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Weisong Sun Yuling Hu Yingfei Xu Yuchen Chen Chunrong Fang State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

Code comments play an essential role in aiding developers understand and maintain source code. Current code comment generation techniques can be classified into categories: extractive methods and abstractive methods. Extractive methods use text retrieval techniques to extract important code tokens to constitute comments. Such comments contain important factual details articulated explicitly in code tokens, but are poor in naturalness. Abstractive methods usually regard code comment generation as a neural machine translation task. By leveraging powerful deep learning-based language models, abstractive methods can generate comments that resemble human writing. However, compared with natural language, programming language code is more complex. Comments generated by abstractive methods often leave out important factual details. In this paper, we propose a novel method for code comment generation by integrating extractive and abstractive models. Our extractive model is built on the Latent Semantic Analysis (LSA) model, effectively extracting important factual details in code snippets. Meanwhile, our abstractive model is built on a deep learning-based encoder-decoder model, enabling it to generate concise and human-written-like comments. We evaluate the effectiveness of our method, called ICS, by conducting extensive experiments on the CodeSearchNet dataset involving six programming languages. The results demonstrate that ICS outperforms state-of-the-art techniques in three widely used metrics: BLEU, METEOR, and ROUGE-L. Moreover, the outcomes of the human evaluation indicate that the comments generated by ICS exhibit superior naturalness and informativeness, and closely align with the provided code snippets.

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Metric localization for lunar rovers via cross-view image matching

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Visual Intelligence 2024年第1期2卷 1-14页

作者： Chen, Zihao Li, Kunhong Li, Haoran Fu, Zhiheng Zhang, Hanmo Guo, Yulan School of Electronics and Comunication Engineering the Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Shenzhen 510275 China Peng Cheng Laboratory Guangdong Shenzhen 518055 China Department of Computer Science and Software Engineering The University of Western Australia Perth 6009 Australia Department of Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai Aerospace Control Technology Institute Shanghai 201109 China College of Electronic Science and Technology National University of Defense Technology Hunan Changsha 410073 China

Accurate localization is critical for lunar rovers exploring lunar terrain features. Traditionally, lunar rover localization relies on sensor data from odometers, inertial measurement units and stereo cameras. However, localization errors accumulate over long traverses, limiting the rover’s localization accuracy. This paper presents a metric localization framework based on cross-view images (ground view from a rover and air view from an orbiter) to eliminate accumulated localization errors. First, we employ perspective projection to reduce the geometric differences in cross-view images. Then, we propose an image-based metric localization network to extract image features and generate a location heatmap. This heatmap serves as the basis for accurate estimation of query locations. We also create the first large-area lunar cross-view image (Lunar-CV) dataset to evaluate the localization performance. This dataset consists of 30 digital orthophoto maps (DOMs) with a resolution of 7 m/pix, collected by the Chang’e-2 lunar orbiter, along with 8100 simulated rover panoramas. Experimental results on the Lunar-CV dataset demonstrate the superior performance of our proposed framework. Compared to the second best method, our method significantly reduces the average localization error by 26% and the median localization error by 22%. © The Author(s) 2024.

关键词： Aerial patch Cross-view metric localization (CVML) Lunar rover Panorama Perspective projection

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Supervised Contrastive Learning with Prototype Distillation for Data Incremental Learning

SSRN

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SSRN 2024年

作者： Yang, Suorong Zhang, Tianyue Xu, Baile Shen, Furao Zhao, Jian State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Electronic Science and Engineering Nanjing University China

The goal of Data Incremental Learning (DIL) is to enable learning from small-scale data batches from non-stationary data streams without clear task divisions. This approach often encounters the issue of catastrophic forgetting in deep neural networks. Meanwhile, practical applications may involve unbalanced data distributions across classes. To address the challenges of DIL, using prototypes as classifiers offers a balance between stability and flexibility. This facilitates the preservation of information from previous classes while evolving to accommodate new ones. Prototypes are particularly effective for classifying separable embeddings in the feature space, as they draw embeddings from the same class closer together while pushing those from different classes further apart. This aligns with the objective of contrastive learning. In this paper, we propose Supervised Contrastive learning with the Prototype Distillation (SCPD) method for the DIL problem. First, we employ supervised contrastive loss (SCL) for model training. To further mitigate the forgetting problem, we introduce the prototype distillation loss, which ensures that features remain close to their corresponding prototypes. Exemplar memory is utilized to store representative samples, enabling the model to maintain performance on previously learned tasks. Moreover, data augmentation techniques are applied to improve the models' robustness and continual learning capability. Experimental results demonstrate that the SCPD model outperforms prior state-of-the-art approaches across several benchmarks, including those with various imbalanced setups. Code will be made publicly available soon. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classification

arXiv

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arXiv 2024年

作者： Yang, Suorong Shen, Furao Zhao, Jian State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Electronic Science and Engineering Nanjing University China

Data augmentation (DA) has been widely used to improve the generalization of deep neural networks. While existing DA methods have proven effective, they often rely on augmentation operations with random magnitudes to each sample. However, this approach can inadvertently introduce noise, induce distribution shifts, and increase the risk of overfitting. In this paper, we propose EntAugment, a tuning-free and adaptive DA framework. Unlike previous work, EntAugment dynamically assesses and adjusts the augmentation magnitudes for each sample during training, leveraging insights into both the inherent complexities of training samples and the evolving status of deep models. Specifically, in EntAugment, the magnitudes are determined by the information entropy derived from the probability distribution obtained by applying the softmax function to the model’s output. In addition, to further enhance the efficacy of EntAugment, we introduce a novel entropy regularization term, EntLoss, which complements the EntAugment approach. Theoretical analysis further demonstrates that EntLoss, compared to traditional cross-entropy loss, achieves closer alignment between the model distributions and underlying dataset distributions. Moreover, EntAugment and EntLoss can be utilized separately or jointly. We conduct extensive experiments across multiple image classification tasks and network architectures with thorough comparisons of existing DA methods. Importantly, the proposed methods outperform others without introducing any auxiliary models or noticeable extra computational costs, highlighting both effectiveness and efficiency. Code is available at https://***/Jackbrocp/EntAugment. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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MixSignGraph: A Sign Sequence is Worth Mixed Graphs of Nodes

arXiv

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arXiv 2025年

作者： Gan, Shiwei Yin, Yafeng Jiang, Zhiwei Wen, Hongkai Xie, Lei Lu, Sanglu State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science University of Warwick CoventryCV4 7AL United Kingdom

—Recent advances in sign language research have benefited from CNN-based backbones, which are primarily transferred from traditional computer vision tasks (e.g., object identification, image recognition). However, these CNN-based backbones usually excel at extracting features like contours and texture, but may struggle with capturing sign-related features. In fact, sign language tasks require focusing on sign-related regions, including the collaboration between different regions (e.g., left hand region and right hand region) and the effective content in a single region. To capture such region-related features, we introduce MixSignGraph, which represents sign sequences as a group of mixed graphs and designs the following three graph modules for feature extraction, i.e., Local Sign Graph (LSG) module, Temporal Sign Graph (TSG) module and Hierarchical Sign Graph (HSG) module. Specifically, the LSG module learns the correlation of intra-frame cross-region features within one frame, i.e., focusing on spatial features. The TSG module tracks the interaction of inter-frame cross-region features among adjacent frames, i.e., focusing on temporal features. The HSG module aggregates the same-region features from different-granularity feature maps of a frame, i.e., focusing on hierarchical features. In addition, to further improve the performance of sign language tasks without gloss annotations, we propose a simple yet counter-intuitive Text-driven CTC Pre-training (TCP) method, which generates pseudo gloss labels from text labels for model pretraining. Extensive experiments conducted on current five public sign language datasets demonstrate the superior performance of the proposed model. Notably, our model surpasses the SOTA models on multiple sign language tasks across several datasets, without relying on any additional cues. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature Selection

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A Short Review of Truck and Drone Collaborative Delivery Problem

A Short Review of Truck and Drone Collaborative Delivery Pro...

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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Yena Zhu Min Liu Dongyao Jin School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan China

With the emergence of efficient logistics models and the development of drone technology, the last-mile delivery problem has become a focus of attention for scholars in various fields. The advantages of drones, such as low cost, energy saving, and independence from road conditions, have made them increasingly popular in the delivery industry. However, drones have shortcomings such as short endurance, small payload capacity, and susceptibility to interference. Therefore, the cooperative delivery model of vehicles and drones has important theoretical research significance and practical application value. Existing literature reviews focus on static truck-drone delivery problems (TDDPs) and methods such as exact algorithms, heuristics, and metaheuristics. However, real-life truck-drone delivery problems are easily affected by dynamic environmental changes. Accordingly, this article introduces some new dynamic variants of TDDPs proposed in recent years, which consider variable drone flight endurance, urban crowded road conditions, stochastic customer demands, and the influence of weather changes. In recent years, some new methods for solving TDDPs have been introduced, such as the increasingly popular machine learning and reinforcement learning methods. Finally, the possible future research directions and development trends of TDDPs are presented.

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TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts

arXiv

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arXiv 2025年

作者： Huang, Yu-Hao Xu, Chang Wu, Yueying Li, Wu-Jun Bian, Jiang National Key Laboratory for Novel Software Technology School of Computer Science Nanjing University China Microsoft Research Asia China Peking University China

Time series generation models are crucial for applications like data augmentation and privacy preservation. Most existing time series generation models are typically designed to generate data from one specified domain. While leveraging data from other domain for better generalization is proved to work in other application areas, this approach remains challenging for time series modeling due to the large divergence in patterns among different real world time series categories. In this paper, we propose a multi-domain time series diffusion model with domain prompts, named TimeDP. In TimeDP, we utilize a time series semantic prototype module which defines time series prototypes to represent time series basis, each prototype vector serving as "word" representing some elementary time series feature. A prototype assignment module is applied to extract the extract domain specific prototype weights, for learning domain prompts as generation condition. During sampling, we extract "domain prompt" with few-shot samples from the target domain and use the domain prompts as condition to generate time series samples. Experiments demonstrate that our method outperforms baselines to provide the state-of-the-art in-domain generation quality and strong unseen domain generation capability. © 2025, CC BY.

关键词： Time series

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