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1st MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance, LISA 2024, held in Conjunction with Medical Image Computing and computer Assisted Intervention Conference, MICCAI 2024

作者： Zhu, Yueyue Jiang, Haotian Cai, Rongqing Chen, Geng National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9783031830105

Magnetic Resonance Imaging (MRI) can be utilized to study the structure of pediatric brains non-invasively. In practice, low-field MRI scanners are widely adopted for pediatric brain imaging. However, the corresponding acquired MRI data usually suffers from severe artifacts, such as noise and motion. Therefore, an effective Quality Assessment (QA) method is essential. To this end, we design a Multi-Label MambaOut (MLMambaOut) model for the low-field pediatric brain MRI QA challenge. Specifically, we view this challenge as a multi-label classification task, utilizing four stages of gated convolution neural network blocks and ML-Decoder to finish the classification with class balance loss. Furthermore, we explore the performance of Mamba and some advanced models for this challenge. We performed extensive experiments on the challenge data, which is low-field and corrupted with seven kinds of artifacts. The results show that our MLMambaOut achieves superior classification results compared with other methods. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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Improving Continual Relation Extraction through Prototypical Contrastive Learning 29

Improving Continual Relation Extraction through Prototypical...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Hu, Chengwei Yang, Deqing Jin, Haoliang Chen, Zhen Xiao, Yanghua School of Data Science Fudan University Shanghai China School of Computer Science Fudan University Shanghai China

Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks. In order to alleviate this critical problem for enhanced CRE performance, we propose a novel Continual Relation Extraction framework with Contrastive Learning, namely CRECL, which is built with a classification network and a prototypical contrastive network to achieve the incremental-class learning of CRE. Specifically, in the contrastive network a given instance is contrasted with the prototype of each candidate relations stored in the memory module. Such contrastive learning scheme ensures the data distributions of all tasks more distinguishable, so as to alleviate the catastrophic forgetting further. Our experiment results not only demonstrate our CRECL’s advantage over the state-of-the-art baselines on two public datasets, but also verify the effectiveness of CRECL’s contrastive learning on improving CRE performance. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Extraction

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Epidermis Bruise Analysis Using Deep Learning 6

Epidermis Bruise Analysis Using Deep Learning

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Sharma, Vansheta Rani, Rupa Pathak, Priya Chakraborty, Sudeshna Department of Computer Science & Engineering- Data Science ABES Institute of Technology U.P. Ghaziabad India Department of Computer Science & Engineering Ajay Kumar Garg Engineering College U.P. Ghaziabad India Depatment of Computer Science & Engineering Banasthali Vidyapith Rajasthan India Department School of Computer Application and Technology Galgotias University UP India

ISBN: (纸本)9798350374629

Among the most vital organs that protect the body of a human from the external environment is the skin. Early skin illness identification is essential for reducing mortality because it prevents skin cancer, and any other skin disease from developing. But because it resembles skin patches, insect bites etc in appearance, early detection that might lengthen life expectancy is a fantastic experiment. We would be working with different skin diseases namely ‘Acne’, ‘Eczema or Atopic Dermatitis’, ‘melanoma’. A working model that uses image analysis and machine learning to classify and identify skin conditions. A brand-new, cutting edge automated system that can swiftly and accurately identify skin lesions is needed to deal with biomedical pictures. There is growing curiosity concerning the application of deep learning to the diagnosis and management of different diseases. For the early detection of skin disease, we therefore suggested a deep learning model based on CNN and the merger of the U-Net and ResNet models is presented as a fusion model that has been used to perform encoding decoding of the disorders using skin scans. This model integrates image processing, image enhancement, machine learning and segmentation techniques. The model yielded an overall accuracy of 95.6%. Incorporate additional skin conditions in the future to strengthen our model's suitability for medical applications. ©2024 IEEE.

关键词： Image enhancement

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Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction 39

Disconnected Emerging Knowledge Graph Oriented Inductive Lin...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Zhang, Yufeng Wang, Weiqing Yin, Hongzhi Zhao, Pengpeng Chen, Wei Zhao, Lei Soochow University School of Computer Science and Technology China Monash University Department of Data Science and Ai Australia The University of Queensland School of Information Technology and Electrical Engineering Australia

ISBN: (纸本)9798350322279

Inductive link prediction (ILP) is to predict links for unseen entities in emerging knowledge graphs (KGs), considering the evolving nature of KGs. A more challenging scenario is that emerging KGs consist of only unseen entities without any edge connected to original KGs, called as disconnected emerging KGs (DEKGs). Existing studies for DEKGs only focus on predicting enclosing links, i.e., predicting links inside the emerging KG. The bridging links, which carry the evolutionary information from the original KG to DEKG, have not been investigated by previous work so far. To fill in the gap, we propose a novel model entitled DEKG-ILP (Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction) that consists of the following two components. (1) The module CLRM (Contrastive Learning-based Relation-specific Feature Modeling) is developed to extract global relation-based semantic features that are shared between original KGs and DEKGs with a novel sampling strategy. (2) The module GSM (GNN-based Subgraph Modeling) is proposed to extract the local subgraph topological information around each link in KGs. The extensive experiments conducted on several benchmark datasets demonstrate that DEKG-ILP has obvious performance improvements compared with state-of-the-art methods for both enclosing and bridging link prediction. © 2023 IEEE.

关键词： Knowledge graph

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Towards Safer Roads: A Comprehensive Review of Object Detection Techniques for Autonomous Vehicles

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SN computer science 2025年第5期6卷 1-20页

作者： Parisapogu, Samson Anosh Babu Narla, Nitya Juryala, Aarthi Ramavath, Siddhu Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amaravati Campus Andhra Pradesh Amaravati 522 503 India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India

Nowadays, people spend a lot of time in travel, and road safety has become an important area of research. As a result, self-driving cars have been gaining popularity in recent years because they have a lot of benefits in terms of reducing traffic congestion and improving road safety, thereby reducing accidents. They also have an environmental benefit as they reduce travel times and hence reduce the usage of fuel. Though a lot of research has already been done in this field, there are always new problems being created and new solutions being found. Apart from self-driving cars, automated navigation also has a huge number of applications. Therefore, it is important to stay up to date about the latest developments in this field. This research is a comprehensive survey that explores and evaluates the different object-detecting algorithms that have been used throughout the years, their advantages, disadvantages, and trade-offs. It also analyses the datasets and benchmarks that have been used for evaluating the models. It aims to identify the gaps in research in this growing field so that it can act as a roadmap for researchers to focus their study on. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Object detection Road safety Self-driving YOLO

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Unleashing the Power of Knowledge Graph for Recommendation via Invariant Learning 24

Unleashing the Power of Knowledge Graph for Recommendation v...

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33rd ACM Web Conference, WWW 2024

作者： Wang, Shuyao Sui, Yongduo Wang, Chao Xiong, Hui School of Data Science University of Science and Technology of China China Hong Kong The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou Hkust Fok Ying Tung Research Institute Hong Kong

ISBN: (纸本)9798400701719

Knowledge graph (KG) demonstrates substantial potential for enhancing the performance of recommender systems. Due to its rich semantic content and associations among interactive entities, it can effectively alleviate inherent limitations in collaborative filtering (CF), such as data sparsity or cold-start issues. However, most existing knowledge-aware recommendation models indiscriminately aggregate all information in KG, without considering information specifically relevant to the recommendation task. Such indiscriminate aggregation could introduce additional noisy knowledge into representation learning, which can distort the understanding of users' genuine preferences, thereby sacrificing the recommendation quality. In this paper, we introduce the principle of invariance to the knowledge-aware recommendation, culminating in our Knowledge Graph Invariant Learning (KGIL) framework. It aims to discern and harness the task-relevant knowledge connections within KG to enhance the recommendation models. Specifically, we employ multiple environment generators to simulate diverse noisy KG-environments. Then we devise a novel attention learning mechanism for KG and user-item interaction graph, aiming to learn environment-invariant subgraphs. Leveraging an adversarial optimization strategy, we enhance the diversity of the environments, meanwhile, promote invariant representation learning across environments. We conduct extensive experiments on three datasets and compare KGIL with state-of-the-art methods. The experimental results further demonstrate the superiority of our approach. © 2024 ACM.

关键词： Knowledge graph

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Long-Term and Short-Term Perception in Knowledge Tracing 1

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11th CCF Big data Conference, Bigdata 2023

作者： Chen, Zihang Zhu, Mengxiao Wang, Fei Shen, Shuanghong Huang, Zhenya Liu, Qi Anhui Province Key Laboratory of Science Education and Communication Institute of Advanced Technology and School of Humanities and Social Sciences University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science and School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9789819989799

ISBN: (纸本)9789819989782

Knowledge Tracing (KT) is a fundamental task in contemporary intelligent educational systems, which tracks the knowledge states of the learners based on their response sequences. KT is crucial for the effectiveness of computer-assisted intelligent educational systems, such as intelligent tutoring systems and educational resource recommendation systems. In recent years, KT models benefited from the deep learning approaches and improved dramatically compared with the traditional probabilistic approaches. However, deep learning based KT models also have significant limitations. For example, Recurrent Neural Network (RNN)-based KT models can not capture dependencies in long-term sequences effectively, and attention mechanism-based KT models rely on the positional encoding to perceive sequential information, which may disrupt the semantics of the original embeddings. Moreover, the data sparsity question remains a big challenge in existing KT models. This study is based on the observation that KT shows a stronger sequential dependence in the long term than in the short term. In this paper, we propose a novel KT model called "Long-term and Short-term perception in knowledge tracing (LSKT)", which uses multilayer perceptrons and attention mechanism to capture the long-term and short-term dependencies, and employs 2PL-IRT based embedding to alleviate the data sparsity question. Extensive experiments on multiple datasets demonstrate the effectiveness of our proposed LSKT model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Deep Learning Educational data Mining Intelligent Education Knowledge Tracing

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Finding Top-k Important Edges on Bipartite Graphs: Ego-betweenness Centrality-based Approaches 39

Finding Top-k Important Edges on Bipartite Graphs: Ego-betwe...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Weng, Tongfeng Zhou, Xu Fang, Yixiang Tan, Kian-Lee Li, Kenli Hunan University College of Computer Science and Electronic Engineering China The Chinese University of HongKong School of Data Science Shenzhen China National University of Singapore School of Computing Singapore

ISBN: (纸本)9798350322279

Bipartite graph is an important data structure that widely exists in disease prevention and control, community detection, and other real-life applications. In a bipartite graph, edges not only connect entries of different types but also are bridges of different communities in the above applications. However, research to date has not yet focused on edge importance in bipartite graphs. Inspired by this, we study a new problem of top-k edge search in bipartite graphs with the goal of finding k most important edges for a given bipartite graph;these edges are crucial bridges among communities. In particular, we introduce the measure of ego-betweenness for evaluating the importance of edges. To handle this problem effectively, a lazy bound-based algorithm is first proposed by integrating an upper bound pruning strategy. After that, to further get better efficiency, a greedy bound-based heuristic algorithm is explored on the basis of a tighter upper bound which contributes to reducing redundant computation for calculating ego-betweenness. Last but not least, two parallel techniques with different levels of granularity, called P -src and P -task, are respectively introduced to further improve the search efficiency. The experimental results on both real-world and synthetic graphs demonstrate the efficiency and scalability of the proposed algorithms. © 2023 IEEE.

关键词： Heuristic algorithms

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis 6

Mapping the Scientific Landscape of Integrated Scheduling To...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

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MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning 39

MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Zheng, Shangfei Wang, Weiqing Qu, Jianfeng Yin, Hongzhi Chen, Wei Zhao, Lei Soochow University School of Computer Science and Technology China Monash University Department of Data Science and Ai Australia The University of Queensland School of Information Technology and Electrical Engineering Australia

ISBN: (纸本)9798350322279

Multi-modal knowledge graphs (MKGs) include not only the relation triplets, but also related multi-modal auxiliary data (i.e., texts and images), which enhance the diversity of knowledge. However, the natural incompleteness has significantly hindered the applications of MKGs. To tackle the problem, existing studies employ the embedding-based reasoning models to infer the missing knowledge after fusing the multi-modal features. However, the reasoning performance of these methods is limited due to the following problems: (1) ineffective fusion of multi-modal auxiliary features;(2) lack of complex reasoning ability as well as inability to conduct the multi-hop reasoning which is able to infer more missing knowledge. To overcome these problems, we propose a novel model entitled MMKGR (Multi-hop Multi-modal Knowledge Graph Reasoning). Specifically, the model contains the following two components: (1) a unified gate-attention network which is designed to generate effective multi-modal complementary features through sufficient attention interaction and noise reduction;(2) a complementary feature-aware reinforcement learning method which is proposed to predict missing elements by performing the multi-hop reasoning process, based on the features obtained in component (1). The experimental results demonstrate that MMKGR outperforms the state-of-the-art approaches in the MKG reasoning task. © 2023 IEEE.

关键词： Knowledge graph

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