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arXiv 2025年

作者： Cheng, Zihan Guo, Jintao Zhang, Jian Qi, Lei Zhou, Luping Shi, Yinghuan Gao, Yang Shanghai Jiao Tong University School of Medicine Shanghai200025 China National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210093 China State Key Laboratory for Novel Software Technology the National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210093 China School of Computer Science and Engineering Key Lab of Computer Network and Information Integration Southeast University Jiangsu Nanjing211189 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia

To segment medical images with distribution shifts, domain generalization (DG) has emerged as a promising setting to train models on source domains that can generalize to unseen target domains. Existing DG methods are mainly based on CNN or ViT architectures. Recently, advanced state space models, represented by Mamba, have shown promising results in various supervised medical image segmentation. The success of Mamba is primarily owing to its ability to capture long-range dependencies while keeping linear complexity with input sequence length, making it a promising alternative to CNNs and ViTs. Inspired by the success, in the paper, we explore the potential of the Mamba architecture to address distribution shifts in DG for medical image segmentation. Specifically, we propose a novel Mamba-based framework, Mamba-Sea, incorporating global-to-local sequence augmentation to improve the model’s generalizability under domain shift issues. Our Mamba-Sea introduces a global augmentation mechanism designed to simulate potential variations in appearance across different sites, aiming to suppress the model’s learning of domain-specific information. At the local level, we propose a sequence-wise augmentation along input sequences, which perturbs the style of tokens within random continuous sub-sequences by modeling and resampling style statistics associated with domain shifts. To our best knowledge, Mamba-Sea is the first work to explore the generalization of Mamba for medical image segmentation, providing an advanced and promising Mamba-based architecture with strong robustness to domain shifts. Remarkably, our proposed method is the first to surpass a Dice coefficient of 90% on the Prostate dataset, which exceeds previous SOTA of 88.61%. The code is available at https://***/orange-czh/Mamba-Sea. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Dual-View Interaction-Aware Lane Change Prediction for Autonomous Driving 39

Dual-View Interaction-Aware Lane Change Prediction for Auton...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Yuhuan Zhang, Zhen Bai, Rufan Liu, Han Wang, Wei Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University China Department of Computer and Information Science University of Macau China Department of Computer Science and Engineering Southeast University China School of Software Dalian University of Technology China School of Medical Technology Beijing Institute of Technology China

ISBN: (纸本)157735897X

As artificial intelligence techniques evolve, we are approaching a critical moment for the widespread deployment of autonomous vehicles. Subsequently, the emergence of mixed-autonomy traffic environments presents formidable challenges to autonomous vehicles, especially for the accurate prediction of lane change intentions of their surrounding human-driven vehicles, which is crucial for ensuring the safety of autonomous vehicles. Existing lane change prediction models mainly focus on capturing the temporal variations in the movement dynamics of individual vehicles. However, the neglect to consider inter-vehicle interactions hinders their capability in complex lane change scenarios, resulting in suboptimal prediction performance. Moreover, current interaction-aware approaches for autonomous driving fail to explicitly model future interactions between vehicles, leading to unreasonable prediction results that can cause collisions between vehicles. To address the above issues, we propose to incorporate the concept of perceived safety into future interaction modeling and design a dual-view interaction-aware lane change prediction model. We evaluate the proposed model on two real-world datasets and experimental results show that the proposed model achieves average improvements of 11.7-12.4% in classification ability and 75.6-95.7% in forecast ability over the best-performing baselines across the two datasets. The ablation study and investigation into future interaction modeling demonstrate that our model has advantages in interpreting lane change scenarios from a driving safety perspective. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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How Mature is Requirements engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions

arXiv

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arXiv 2024年

作者： Umm-E-Habiba Haug, Markus Bogner, Justus Wagner, Stefan Institute of Software Engineering University of Stuttgart Stuttgart70569 Germany Department of Software Engineering University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands TUM School of Communication Information and Technology Technical University of Munich Heilbronn Germany

Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new quality requirements due to emerging ethical implications. It is currently unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Therefore, our goal is to provide a comprehensive overview of RE4AI to researchers and practitioners. What has been achieved so far, i.e., what practices are available, and what research gaps and challenges still need to be addressed? To achieve this, we conducted a systematic mapping study combining query string search and extensive snowballing. The extracted data was aggregated, and results were synthesized using thematic analysis. Our selection process led to the inclusion of 126 primary studies. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. Furthermore, we identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges, along with a few others. Additionally, we proposed seven potential research directions to address these challenges. Practitioners can use our results to identify and select suitable RE methods for working on their AI-based systems, while researchers can build on the identified gaps and research directions to push the field forward. ∗ © 2024, CC BY.

关键词： Requirements engineering

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DLUE: Benchmarking Document Language Understanding

arXiv

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arXiv 2023年

作者： Xu, Ruoxi Lin, Hongyu Guan, Xinyan Han, Xianpei Sun, Yingfei Sun, Le Chinese Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

Understanding documents is central to many real-world tasks but remains a challenging topic. Unfortunately, there is no well-established consensus on how to comprehensively evaluate document understanding abilities, which significantly hinders the fair comparison and measuring the progress of the field. To benchmark document understanding researches, this paper summarizes four representative abilities, i.e., document classification, document structural analysis, document information extraction, and document transcription. Under the new evaluation framework, we propose Document Language Understanding Evaluation – DLUE, a new task suite which covers a wide-range of tasks in various forms, domains and document genres. We also systematically evaluate six well-established transformer models on DLUE, and find that due to the lengthy content, complicated underlying structure and dispersed knowledge, document understanding is still far from being solved, and currently there is no neural architecture that dominates all tasks, raising requirements for a universal document understanding architecture. © 2023, CC BY.

关键词： information retrieval systems

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PROGRESSIVE DISTRIBUTION ALIGNMENT BASED ON LABEL CORRECTION FOR UNSUPERVISED DOMAIN ADAPTATION

PROGRESSIVE DISTRIBUTION ALIGNMENT BASED ON LABEL CORRECTION...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Li, Yong Li, Desheng Lu, Yuwu Gao, Can Wang, Wenjing Lu, Jianglin College of Computer Science and Software Engineering Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)9781665438643

Unsupervised domain adaptation (UDA) aims to transfer knowledge between different domains. Most of the existing UDA methods try to align the conditional distribution between the source and target domains by utilizing the information of pseudo labels induced from the target domain. To tackle the negative transfer caused by inaccurate pseudo labels, we propose a novel UDA method named progressive distribution alignment based on label correction (PDALC). Specifically, PDALC uses the class discriminative information to perform subspace learning to obtain the domain invariance subspace. Furthermore, a new mechanism of pseudo label correction is introduced to measure the reliability of pseudo labels and utmostly correct the inaccurate pseudo labels. By combining subspace learning with label correction, the performance of PDALC can be continuously improved, which in turn reduces the generation of inaccurate pseudo labels. The experimental results show that the proposed method outperforms the state-of-the-art UDA methods. © 2021 IEEE

关键词： computer vision

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Locating what you need: towards adapting diffusion models to OOD concepts in-the-wild 24

Locating what you need: towards adapting diffusion models to...

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Proceedings of the 38th International Conference on Neural information Processing Systems

作者： Jianan Yang Chenchao Gao Zhiqing Xiao Junbo Zhao Sai Wu Gang Chen Haobo Wang College of Computer Science and Technology Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security International School of Information Science and Engineering Dalian University of Technology and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security School of Software Technology Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security

ISBN: (纸本)9798331314385

The recent large-scale text-to-image generative models have attained unprecedented performance, while people established adaptor modules like LORA and DreamBooth to extend this performance to even more unseen concept tokens. However, we empirically find that this workflow often fails to accurately depict the out-of-distribution concepts. This failure is highly related to the low quality of training data. To resolve this, we present a framework called Controllable Adaptor Towards Out-of-Distribution Concepts (CATOD). Our framework follows the active learning paradigm which includes high-quality data accumulation and adaptor training, enabling a finer-grained enhancement of generative results. The aesthetics score and concept-matching score are two major factors that impact the quality of synthetic results. One key component of CATOD is the weighted scoring system that automatically balances between these two scores and we also offer comprehensive theoretical analysis for this point. Then, it determines how to select data and schedule the adaptor training based on this scoring system. The extensive results show that CATOD significantly outperforms the prior approaches with an 11.10 boost on the CLIP score and a 33.08% decrease on the CMMD metric.

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Implementation of IoT and WSN Technologies for Health Monitoring 1st

Implementation of IoT and WSN Technologies for Health Monito...

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Mazhar, Tehseen Iqbal, Muhammad Iqbal, Muhammad Ghadi, Yazeed Yasin Rehman, Ateeq Ur Abbasi, Waseem Department of Computer Science Virtual University of Pakistan Lahore54000 Pakistan Institute of Computing and Information Technology Gomal University Dera Ismail Khan29220 Pakistan Department of Computer Science and Software Engineering Al Ain Universit Abu Dhabi15322 United Arab Emirates Department of Electrical Engineering Government College University Lahore54000 Pakistan Department of Computer Science and IT Superior University Sargodha Pakistan

ISBN: (纸本)9783031776168

IoT-based applications make innovative use of millions of sensors. Research has risen in the medical and technological fields in the previous ten years. The Internet of Things impacts the development of medical research. Improved medical care is possible because the Internet of Things can link sensors, medical equipment, and remote healthcare professionals. Patient treatment has been enhanced in terms of effectiveness, cost, accessibility, and safety. Due to COVID-19, receiving medical treatment in the convenience of one’s home is more important than ever. To enhance healthcare, internet of things devices is utilized to monitor patients. His article examines how the Internet of Things has improved, enabling technology, healthcare services, and healthcare-related challenges. IoT system concerns are also taken into consideration. This effort aims to help future researchers understand the topic better by giving a thorough overview of all of HIoT’s many uses. The applications, technology, and challenges of the healthcare industry are all examined in this article. As a result, the IoT has the potential to improve healthcare’s effectiveness and ability to treat patients. The idea put out in this article is that the Internet of Things may be made widely used, lucrative, and easily available everywhere. The integration provides a faster calculation. The last section includes a number of practical recommendations and suggestions for implementing the IoT healthcare system during COVID-19 and future severe pandemics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： COVID-19

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On the Convergence of Gossip Learning in the Presence of Node Inaccessibility

On the Convergence of Gossip Learning in the Presence of Nod...

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IEEE International Conference on Communications (ICC)

作者： Tian Liu Yue Cui Xueyang Hu Yecheng Xu Bo Liu Zhejiang Laboratory Hangzhou China Department of Computer Science and Computer Information Systems Auburn University at Montgomery Montgomery USA Department of Computer Science and Software Engineering Auburn University Auburn USA Institute of Rural Development Zhejiang Academy of Agricultural Sciences Hangzhou China DBAPPSecurity Co. Ltd. Hangzhou China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Gossip learning (GL), as a decentralized alternative to federated learning (FL), is more suitable for resourceconstrained wireless networks, such as Flying Ad-Hoc Networks (FANETs) that are formed by unmanned aerial vehicles (UAVs). GL can significantly enhance the efficiency and extend the battery life of UAV networks. Despite the advantages, the performance of GL is strongly affected by data distribution, communication speed, and network connectivity. However, how these factors influence the GL convergence is still unclear. Existing work studied the convergence of GL based on a virtual quantity for the sake of convenience, which failed to reflect the real state of the network when some nodes are inaccessible. In this paper, we formulate and investigate the impact of inaccessible nodes to GL under a dynamic network topology. We first decompose the weight divergence by whether the node is accessible or not. Then, we investigate the GL convergence under the dynamic of node accessibility and theoretically provide how the number of inaccessible nodes, data non-i.i.d.-ness, and duration of inaccessibility affect the convergence. Extensive experiments are carried out in practical settings to comprehensively verify the correctness of our theoretical findings.

关键词： Training Accuracy Network topology Federated learning Wireless networks Autonomous aerial vehicles Ad hoc networks

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Design Amharic Text Sentiment Analysis Model Using Machine Learning Techniques. In Case of Restaurant Reviews

Design Amharic Text Sentiment Analysis Model Using Machine L...

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information and Communication Technology for Development for Africa (ICT4DA), International Conference on

作者： Birku Gedif Abebe Alemu Yibeltal Assefa Shegaw Nibret Information Science College Of Informatics University of Gondar Computer Science College Of Informatics University of Gondar Software Engineering Kombolcha Institute of Technology Wollo University ICT4D Research Center Bahir Dar Institute of Technology Bahir Dar University

Sentiment analysis is a type of natural language processing for tracking the attitude of the public about a particular product, service or topic. It is also highly challenging as natural language processing research topic, and covers many novel sub-problems. Now business organizations and academics are putting forward their efforts to find the best system for sentiment analysis. The focus of this study was an Amharic unstructured restaurant review on the web. The objective of the paper was to design Amharic text sentiment analysis model using supervised machine learning techniques and evaluate the performance of classifiers. This paper explored the supervised machine learning classification approaches (naive Bayes, support vector machine and k-nearest neighbor) with different feature selection schemes to obtain a sentiment analysis model for domain specific restaurant review dataset at sentence level. The proposed model has the following components: Data preparation, preprocessing such as tokenization, normalization, filter stop words, feature extraction and selection to prepare feature vector, polarity classification. Performance analysis carried out on classifiers, based on n-grams proposed. From the results of the experimental studies, all algorithms are known to be highly effective classifiers, and are able to achieve good accuracy in this experiment. The experiments show that Term frequency (TF) and the TF -IDF scheme gives maximum accuracy 80.43 % and 79.49 % respectively for SVM in bigram features. Term frequency and term occurrence also give maximum accuracy 78.37 % and 78.00 % respectively for Naive Bayes classifier at bigram features. TF-IDF also give maximum accuracy 78.00% for KNN at 4-gram. The challenge was opinion holders sometimes give objective text to express their opinion, but the classifier did not identify those facts from opinions. These kinds of complexities of natural languages make sentiment mining systems more challenging and to resolving t

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Machine Learning in Higher Education using RF and SVM for Pedagogical Enhancement

Machine Learning in Higher Education using RF and SVM for Pe...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Fateh Bahadur Kunwar Satyajee Srivastava R Dhivya Monika Gulati Taruna Anand D Antony Arul Raj Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Udaipur India Department of Computer Science and Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India Department of Information Technology M.Kumarasamy College of Engineering Karur India MMIM Maharishi Markandeshwar (Deemed to be University) Ambala India Department of Humanities and Social Sciences Graphic Era (Deemed to be) University Dehradun India Department of Software Systems PSG College of Arts & Science Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions.

关键词： Support vector machines Radio frequency Training Accuracy Education Symbols Feature extraction Tokenization Integrated circuit modeling Principal component analysis

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