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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Xiaoqing Lu, Yu Ngiam, Kee Yuan Yu, Zichang Furqan, Mohammad Shaheryar Shenzhen Technology University College of Big Data and Internet Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China National University of Singapore Yong Loo Lin School of Medicine Department of Surgery Singapore

ISBN: (纸本)9798350386226

Accurate analysis of Fetal Heart Rate (FHR) signal is often impeded by challenges such as data scarcity and label imbalance, which affect the reliability and robustness of deep learning models. To address these challenges, this study introduces FHRDiff, a novel diffusion model conditioned on Phase-Rectified Signal Averaging (PRSA) spectrograms for the creation of synthetic FHR signals. Our model integrates time encoding, condition generation from PRSA spectrograms, and residual blocks with dilated convolutions to effectively manage temporal dynamics and long-range dependencies. Extensive qualitative and quantitative experiments on FHR signal synthesis demonstrate the feasibility and effectiveness of FHRDiff. Compared to Generative Adversarial Networks (GANs) and image-based diffusion model, our method achieves the highest signal fidelity and distribution similarity, with key measures including 0.067 maximum mean deviation (MMD), 0.492 percent root mean square difference (PRD), 1.763 relative entropy (RE), and 0.160 Frechet distance (FD). Expert validation confirms the model's capacity to accurately generate data for normal and abnormal FHR signals based on the paired condition. In addition, an ablation study was conducted to highlight that the spectrogram paired condition can guide the diffusion model to produce synthetics FHR signals with greater diversity compared to unconditional models. The results emphasize diffusion models' potential for broad application in biomedical time series analysis, such as generation, imputation and noise removal. © 2024 IEEE.

关键词： Heart

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Enhancing Summarization of Legal Text Documents using Pre-trained Models 5

Enhancing Summarization of Legal Text Documents using Pre-tr...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Kasar, Ashish Matade, Suyash Rasal, Durvankur Shinde, Swapnil Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798331522100

A very relevant challenge of summarizing legal text documents often exceeds the length, therefore being complex, was addressed using pre-trained models such as BART and PEGASUS. In the paper, the issue is discussed-the challenge that these pre-trained models face while being effective in general text summarization tasks, which becomes difficult in legal contexts due to specialized vocabulary and structure of legal documents. The authors fine-tune these models using the BillSum dataset-provided US Congressional bills as a proxy for court documents. Evaluation on ROUGE metrics reveals that BART outperforms PEGASUS in summation of content structures of legal texts to the greatest extent intended. The results suggest that the pre-trained models significantly improve the process of summarizing legal documents, both for professionals in law and general readers who would otherwise have to wade through an extensive amount of legal information. © 2025 IEEE.

关键词： Natural language processing systems

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A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 824-850页

作者： M.Victoria Luzón Nuria Rodríguez-Barroso Alberto Argente-Garrido Daniel Jiménez-López Jose M.Moyano Javier Del Ser Weiping Ding Francisco Herrera Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Communications Engineering University of the Basque Country(UPV/EHU)and also with TECNALIABasque Research&Technology Alliance(BRTA)Spain School of Information Science and Technology Nantong UniversityNantong 226019China

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third *** paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data *** virtue of FL,models can be learned from all such distributed data sources while preserving data *** aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software ***,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL *** ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

关键词： data privacy distributed machine learning federated learning software frameworks

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Exploring the Potential of Large Vision-Language Models for Unsupervised Text-Based Person Retrieval 39

Exploring the Potential of Large Vision-Language Models for ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Zongyi Jianbo, Li Shi, Yuxuan Chen, Jiazhong Huang, Shijuan Tu, Linnan Shen, Fei Ling, Hefei Department of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center of Educational Big Data The Faculty of Artificial Intelligence in Education Central China Normal University China Nanjing University of Science and Technology China

ISBN: (纸本)157735897X

The aim of text-based person retrieval is to identify pedestrians using natural language descriptions within a large-scale image gallery. Traditional methods rely heavily on manually annotated image-text pairs, which are resource-intensive to obtain. With the emergence of Large Vision-Language Models (LVLMs), the advanced capabilities of contemporary models in image understanding have led to the generation of highly accurate captions. Therefore, this paper explores the potential of employing Large Vision-Language Models for unsupervised text-based pedestrian image retrieval and proposes a Multi-grained Uncertainty Modeling and Alignment framework (MUMA). Initially, multiple Large Vision-Language Models are employed to generate diverse and hierarchically structured pedestrian descriptions across different styles and granularities. However, the generated captions inevitably introduce noise. To address this issue, an uncertainty-guided sample filtration module is proposed to estimate and filter out unreliable image-text pairs. Additionally, to simulate the diversity of styles and granularities in captions, a multi-grained uncertainty modeling approach is applied to model the distributions of captions, with each caption represented as a multivariate Gaussian distribution. Finally, a multi-level consistency distillation loss is employed to integrate and align the multi-grained captions, aiming to transfer knowledge across different granularities. Experimental evaluations conducted on three widely-used datasets demonstrate the significant advancements achieved by our approach. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Gaussian distribution

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Dynamic Adapter with Semantics Disentangling for Cross-lingual Cross-modal Retrieval 39

Dynamic Adapter with Semantics Disentangling for Cross-lingu...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Cai, Rui Dong, Zhiyu Dong, Jianfeng Wang, Xun The College of Computer Science and Technology Zhejiang Gongshang University Hangzhou China Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology Hangzhou China

ISBN: (纸本)157735897X

Existing cross-modal retrieval methods typically rely on large-scale vision-language pair data. This makes it challenging to efficiently develop a cross-modal retrieval model for under-resourced languages of interest. Therefore, Cross-lingual Cross-modal Retrieval (CCR), which aims to align vision and the low-resource language (the target language) without using any human-labeled target-language data, has gained increasing attention. As a general parameter-efficient way, a common solution is to utilize adapter modules to transfer the vision-language alignment ability of Vision-Language Pretraining (VLP) models from a source language to a target language. However, these adapters are usually static once learned, making it difficult to adapt to target-language captions with varied expressions. To alleviate it, we propose Dynamic Adapter with Semantics Disentangling (DASD), whose parameters are dynamically generated conditioned on the characteristics of the input captions. Considering that the semantics and expression styles of the input caption largely influence how to encode it, we propose a semantic disentangling module to extract the semantic-related and semantic-agnostic features from the input, ensuring that generated adapters are well-suited to the characteristics of input caption. Extensive experiments on two image-text datasets and one video-text dataset demonstrate the effectiveness of our model for cross-lingual cross-modal retrieval, as well as its good compatibility with various VLP models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Visual languages

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Structure Balance and Gradient Matching-Based Signed Graph Condensation 39

Structure Balance and Gradient Matching-Based Signed Graph C...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Rong Xu, Long Liu, Songbai Ji, Junkai Li, Lingjie Lin, Qiuzhen Ma, Lijia College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Big Data and Internet Shenzhen Technology University Shenzhen China

ISBN: (纸本)157735897X

Training graph neural networks (GNNs) for graph representation has received increasing concerns due to its outstanding performance in the link prediction and node classification tasks, but it incurs much time and storage for tackling large-scale graphs. To alleviate this issue, graph condensation has been emerged to condense the large graph into a small but highly-informative graph, while achieving comparable performance of GNNs trained on the small graph and large graph. However, existing works mainly focus on the gradient or distribution matching under GNN training trajectories to condense simple link structures, while overlooking the structure matching for condensing signed graph that exists conflict links and structural balance among nodes. To bridge this gap, we propose a novel Structure Balance and Gradient Matching-Based Signed Graph Condensation (SGSGC) method for condensing signed graph with node attributes, conflict links and structural balance into informative smaller ones. Specifically, we first propose a structure-balanced matching to match the structural balance between the original and condensed signed graph, and then combine it with the gradient matching to condense signed graph for the link sign prediction task, while preserving both conflicting link structures and node attributes. Moreover, we use the feature smoothing and the graph sparsification technique to improve the robustness for the GNN training, respectively. Finally, a bi-level optimization technique is proposed to simultaneously find the optimal node attributes and conflict structure of the condensed graph. Experiments on six datasets demonstrate that SGSGC achieves excellent performance. On Epinions, 94% test accuracy of training on the original signed graph, while reducing their graph size by 99.95% - 99.99%, and there exist 2.24% - 6.26% accuracy improvements for link sign prediction compared to the state-of-the-arts. Copyright © 2025, Association for the Advancement of Artificial Int

关键词： Network theory (graphs)

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Identification of Human Activity from Video Streaming Smartphone data Using Intensified VGG16

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International Journal of Engineering, Transactions B: Applications 2025年第6期38卷 1340-1352页

作者： Yadav, R.K. Daniel, A. Semwal, V.B. Department of Computer Science and Engineering Amity University Gwalior India Data Science and Engineering Manipal University Jaipur India Department of Computer Science and Engineering Maulana Azad National Institute Technology Bhopal India

Human activity recognition (HAR) techniques pick out and interpret human behaviors and actions by analyzing data gathered from various sensor devices. HAR aims to recognize and automatically categorize human activities using patterns and attributes taken from sensor data. HAR is complex in implementing the algorithm for a self-recorded dataset, including challenges such as age variation, wearing different clothes, environment and surface, the direction of the smartphone camera, and many more. The paper aims to propose a VGG16 deep learning framework including an activation function and different optimizers for classifying human activity from the real-time captured dataset;further, we compare the evaluated results with existing results. The proposed methods achieved 99.88% accuracy with excellent precision, recall, and F_measure values. Comparing the evaluated result with existing outcomes over the WISDM and UCI-HAR datasets. The new things in the article are a self-captured dataset of various aged male, female, and healthy volunteers to perform seven activities. Furthermore, this research uses Tensor Processing Units (TPU) available on Kaggle to improve classification accuracy while reducing error rates and speeding up execution. ©2025 The author(s).

关键词： Smartphones

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A New Transformation Approach for Uplift Modeling with Binary Outcome 24

A New Transformation Approach for Uplift Modeling with Binar...

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2024 9th International Conference on Mathematics and Artificial Intelligence, ICMAI 2024

作者： Li, Kun Zhu, Liangshu Department of Big Data Everbright Technology Co. LTD Beijing Shi Shijingshan Qu China Department of Data Asset Management China Everbright Bank Beijing ShiQu Xicheng China

ISBN: (纸本)9798400717284

Uplift modeling has been used effectively in fields such as marketing and customer retention, to target those customers who are more likely to respond due to the campaign or treatment. Essentially, it is a machine learning technique that predicts the gain from performing some action with respect to not taking it. A popular class of uplift models is the transformation approach that redefines the target variable with the original treatment indicator. These transformation approaches only need to train and predict the difference in outcomes directly. The main drawback of these approaches is that in general it does not use the information in the treatment indicator beyond the construction of the transformed outcome and usually is not efficient. In this paper, we design a novel transformed outcome for the case of the binary target variable and unlock the full value of the samples with zero outcome. From a practical perspective, our new approach is flexible and easy to use. Experimental results on synthetic and real-world datasets obviously show that our new approach outperforms the traditional one. At present, our new approach has already been applied to precision marketing in a China nation-wide financial holdings group. 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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Vision transformer for detecting traffic congestion using image mapping

Advances in Transportation Studies

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Advances in Transportation Studies 2025年 66卷 19-38页

作者： Khalfi, A. Guerroumi, M. Haid, T. Laradji, N. Faculty of Computer Science Artificial Intelligence and Data Science Department University of Science and Technology Houari Boumediene Algiers Algeria Matter Sciences and Computer Science Faculty Department of Computer Science University of Djilali Bounaama Khemis Miliana Algeria

Leveraging advanced computer vision techniques, we present a novel method for analyzing and categorizing traffic videos by utilizing an image mapping technique combined with a UniVit (Unit Vision Transformer) model. First, video data is transformed into an image dataset, where each frame undergoes a mapping process based on vehicle detection using the YOLOv8s algorithm. These mapped images are then input into a vision transformer, which utilizes attention mechanisms to identify and analyze complex patterns across frames. The approach is evaluated on the UCSD (University of California San Diego) dataset, which features diverse weather conditions such as clear, overcast, and rainy scenarios, enabling a rigorous assessment under varied environments. Our method achieves a notable accuracy score of 99.89%, surpassing previous approaches on the UCSD benchmark. This improvement underscores the effectiveness of combining image mapping with advanced vision transformers, suggesting promising applications for intelligent traffic management systems aimed at mitigating traffic issues. © 2025, Aracne Editrice. All rights reserved.

关键词： Photomapping

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SourcePlag - Source Code Plagiarism Detection Based on Abstract Syntax Trees 3

SourcePlag - Source Code Plagiarism Detection Based on Abstr...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Sharma, Nakul Shinde, Siddharth Bhosale, Swarup Patil, Suyog Vishwakarma Institute of Information Technology Faculty of Engineering Department of Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350354348

Code plagiarism poses a significant challenge in programming communities, necessitating effective detection mechanisms. This paper introduces a novel system that employs Abstract Syntax Trees (ASTs) for code representation and comparison. The system utilizes ASTs to capture the structural essence of code, facilitating a comprehensive analysis of code similarity, it offers flexibility for application across multiple programming languages. In addition to leveraging the Levenshtein Distance Algorithm for Python code comparison, the system incorporates node counting for other languages such as Java and C/C++. By integrating AST-based representation and a combination of comparison techniques, the system offers a robust solution for identifying plagiarized code instances accurately across various programming environments. Through a detailed exploration of the system's methodology, this paper underscores its potential to address the pervasive issue of code plagiarism in programming communities. © 2024 IEEE.

关键词： C++ (programming language)

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