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Spatio-Temporal Domain-Aware Network for Skeleton-Based Action Representation Learning 27th

Spatio-Temporal Domain-Aware Network for Skeleton-Based Act...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Hu, Jiannan Wu, Cong Xu, Tianyang Wu, Xiao-Jun Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

ISBN: (纸本)9783031781094

Considering its capability to extract implicit patterns from unlabeled data, contrastive learning has been widely employed in unsupervised skeleton-based action recognition. Spatio-temporal modeling is a key component in understanding skeleton sequence. However, existing methods often adopt rudimentary mechanisms or even completely overlook this aspect, leading to suboptimal performance in downstream tasks. In this paper, we propose a Spatio-Temporal Domain-Aware Network (STDA-Net). Firstly, the features extracted from the backbone extractor are further decoded into a triple-stream representation, corresponding to the spatial, temporal and global domains, respectively. Following that, an innovative approach named Triple Attention Transformer Module (TATM) is proposed to achieve customized spatio-temporal reasoning. TATM consists of three independent attention modules and a shared feedforward layer, thus achieving reasoning in different domains in a more efficient manner. Finally, domain-aware projectors are used to obtain richer spatio-temporal representations, providing a basis for the subsequent construction of inter-domain and intra-domain contrasts. Comprehensive experiments on NTU-RGB+D 60 &120 and PKU-MMD datasets demonstrate the superior performance of STDA-Net. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Transformers in the Service of Description Logic-Based Contexts 24th

Transformers in the Service of Description Logic-Based Co...

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24th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2024

作者： Poulis, Angelos Tsalapati, Eleni Koubarakis, Manolis Department of Computer Science Boston University Boston United States Athens Technology Center Chalandri Greece AI Team Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece Archimedes/Athena RC Marousi Greece

ISBN: (纸本)9783031777912

Recent advancements in transformer-based models have initiated research interests in investigating their ability to learn to perform reasoning tasks. However, most of the contexts used for this purpose are in practice very simple: generated from short (fragments of) first-order logic sentences with only a few logical operators and quantifiers. In this work, we construct the natural language dataset, DELTAD, using the description logic language ALCQ. DELTAD contains 384K examples, and increases in two dimensions: i) reasoning depth, and ii) linguistic complexity. In this way, we systematically investigate the reasoning ability of a supervised fine-tuned DeBERTa-based model and of two large language models (GPT-3.5, GPT-4) with few-shot prompting. Our results demonstrate that the DeBERTa-based model can master the reasoning task and that the performance of GPTs can improve significantly even when a small number of samples is provided (9 shots). We open-source our code and datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distribution transformers

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Designing a Large Language Model Based Open Data Assistant for Effective Use 19th

Designing a Large Language Model Based Open Data Assistant f...

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19th International Conference on Design science Research in Information Systems and Technology (DESRIST) - Design science Research for a Resilient Future

作者： Schelhorn, Till Carlo Gnewuch, Ulrich Maedche, Alexander Karlsruhe Inst Technol KIT Inst Informat Syst & Mkt IISM Karlsruhe Germany Univ Passau Passau Germany

ISBN: (纸本)9783031611742;9783031611759

Open data is widely recognized for its potential positive impact on society and economy. However, many open data sets remain underutilized because users, such as civil servants and citizens, lack the necessary technical and analytical skills. Additionally, existing open data portals often fall short of providing user-friendly access to data. Although conversational agents equipped with Large Language Models have emerged as a promising solution to address these challenges, it is unclear how to design Large Language Model based open data assistants that allow users to formulate their information needs in natural language and ultimately use open data effectively. To address this gap, we undertake a Design science Research project guided by the theory of effective use. In this first cycle of the project, we present meta-requirements and propose initial design principles on how to design a Large Language Model based open data assistant for effective use. Subsequently, we instantiate our principles in a prototype and evaluate it in a focus group with experts from a medium-sized German city. Our results contribute design knowledge in the form of design principles for open data assistants and inform future design cycles of our Design science Research project.

关键词： Open Data Conversational Agents Large Language Models Theory of Effective Use Design science Research

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ICPR 2024 Competition on Multi-line Mathematical Expressions Recognition 27th

ICPR 2024 Competition on Multi-line Mathematical Expression...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zong, Tianrui Yang, Lin Luo, Wei Yu, Wenhao Cai, Borui Zhang, He Liu, Fenghong Yan, Qinqin Gao, Liangcai Kexin Technology Beijing China Data Intelligence National Science Library of the Chinese Academy of Sciences Beijing China Wangxuan Institute of Computer Technology Peking University Beijing China

ISBN: (纸本)9783031801389

Nowadays, mathematical expressions play a significant role in conveying a wide range of information, and accurately recognizing multi-line mathematical expressions is essential for numerous applications. This task is notably challenging due to the intricate spatial relationships between symbols, which complicates the recognition process. Despite its significance, the recognition of multi-line mathematical expressions has received relatively limited attention in both academic literature and competitive evaluations. To advance research and development in this domain, we organized the ICPR 2024 Competition on Multi-line Mathematical Expression Recognition. In this competition, we introduced the AMME-20K dataset, which includes 60% multi-line expressions and more than 4,600 expressions with three or more lines which is over three times the number in the recently published dataset for multi-line expressions recognition task. This competition was held from April 26, 2024 to July 21, 2024 with 9 valid submissions. This report provides a detailed description of the proposed dataset, the task, the evaluation metrics, and the recognition results, highlighting key advancements and insights gained from the competition. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dataset Multi-line Mathematical Expressions Recognition Transformer

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SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning 27th

SecureCut: Federated Gradient Boosting Decision Trees with ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Bowen Zhang, Jian Li, Jie Wu, Chentao Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Yancheng Blockchain Research Institute Yancheng China Blockchain Advanced Research Center Shanghai China Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3 Shanghai China

ISBN: (纸本)9783031781681

In response to legislation, companies are now mandated to honor the right to be forgotten by erasing user data. Consequently, it has become imperative to enable data removal in Vertical Federated Learning (VFL), where multiple parties provide private features for model training. In VFL, data removal, i.e., machine unlearning, requires removing specific features across all samples, ensuring privacy in federated learning. To address this challenge, we propose SecureCut, a novel Gradient Boosting Decision Tree (GBDT) framework that effectively enables both instance unlearning and feature unlearning without the need for retraining from scratch. Leveraging a robust GBDT structure, we enable effective data deletion while reducing degradation of model performance. Extensive experimental results on popular datasets demonstrate that our method achieves superior model utility and forgetfulness compared to the state-of-the-art methods. To the best of our knowledge, this is the first work that investigates machine unlearning in VFL scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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A Novel Privacy-Preserving Federated Learning Model Based on Secure Multi-party Computation 1

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10th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2023

作者： Tran, Anh Tu Luong, The Dung Pham, Xuan Sang Academy of Cryptography Techniques Hanoi Viet Nam Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (数字)9783031467813

ISBN: (纸本)9783031467806

Although supporting training deep learning models distributed without disclosing the raw privacy data, federated learning (FL) is still vulnerable to inference attacks. This paper proposes ComEnc-FL, a privacy-enhancing federated learning system that combats these vulnerabilities. ComEnc-FL uses secure multi-party computation and parameter encoding to reduce communication and computational expenses. ComEnc-FL surpasses typical secure multi-party computation systems in training time and data transfer bandwidth. ComEnc-FL matches the base FL framework and outperforms differential privacy-safe frameworks. We also show that parameter compression reduces encryption time, improving model performance over the FL. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Data transfer

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Analyzing Students' Perspective for Using computer-Mediated Communication Tools for Group Collaboration in Higher Education 10th

Analyzing Students' Perspective for Using Computer-Mediated ...

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10th International Conference on Learning and Collaboration Technologies (LCT) Held as Part of the 25th International Conference on Human-computer Interaction (HCII)

作者： Owusu, Eric Kulkarni, Adita Washington, Brittani S. SUNY Coll Brockport Dept Comp Sci 350 New Campus Dr Brockport NY 14420 USA Univ N Carolina Dept Business Informat Syst & Operat Management 9201 Univ City Blvd Charlotte NC 28223 USA

ISBN: (纸本)9783031344107;9783031344114

Advancement in technology has changed the way humans communicate with one another significantly. Post the COVID-19 pandemic, computer-mediated communication (CMC) has become an essential part of an instructor's course deliverable process in technology-oriented courses in higher education. It has affected the way instructors communicate with their students, and the way students communicate among themselves. Throughout their higher education curriculum, students work on at least one group project. Collaboration skills obtained while working on such group projects are important as they help the students in their post-graduation career. This work analyzes the use of CMC tools for group collaboration in higher education from the students' perspective. There are five measures of performance (MOP) indicators - payload, time, range, features and speed, used in this analysis. The observation is that student responses in all the indicators are skewed towards agreement, satisfaction, and effectiveness of the use of CMC tools.

关键词： computer-mediated Communication Collaborative Measure of performance

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EDB-Net: An Edge-Guided Dual-Branch Neural Network for Skin Cancer Classification 27th

EDB-Net: An Edge-Guided Dual-Branch Neural Network for Skin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Ray, Amartya Gayen, Soumyajit Kaplun, Dmitrii Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9783031781032

Skin cancer poses a serious global health challenge, where timely and precise diagnosis is essential to improve patient outcomes. Recently, neural networks have proven to be highly effective tools for automated skin cancer classification, significantly advancing the field of dermatology. This paper introduces a novel approach to generate edge maps from dermoscopic images using a holistically nested edge detector model. These edge maps enhance the detection of shape and symmetry irregularities, which are key indicators of malignancy, and improve the focus on relevant regions of interest. We then propose an edge-guided dual-branch neural network, called EDB-Net, for the classification task. Branch 1 handles edge maps, while Branch 2 processes original dermoscopic images. To highlight significant regions and focus on specific lesion areas, we incorporate a novel channel-spatial synergistic attention block within Branch 2. Additionally, we introduce a unique strategy to modulate the generated attention maps using edge features extracted from the edge maps in Branch 1, creating edge-guided features that refine the overall feature representation. In the final stage, both edge-guided and attention-aided features are combined, producing more distinct and contextually relevant outputs, thereby significantly enhancing classification performance. Our model achieves accuracies of 0.927 and 0.848 on the challenging HAM10000 and ISIC 2016 datasets, respectively, without employing any data augmentation. The source code of the proposed model is available at: https://***/Cmatermedicalimageanalysis/EDB_Net. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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HyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks 7th

HyperBrain: Anomaly Detection for Temporal Hypergraph Brain...

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7th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Sadeghian, Sadaf Li, Xiaoxiao Seltzer, Margo Department of Computer Science University of British Columbia Vancouver Canada Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada Vector Institute Toronto Canada

ISBN: (纸本)9783031787607

Identifying unusual brain activity is a crucial task in neuroscience research, as it aids in the early detection of brain disorders. It is common to represent brain networks as graphs, and researchers have developed various graph-based machine learning methods for analyzing them. However, the majority of existing graph learning tools for the brain face a combination of the following three key limitations. First, they focus only on pairwise correlations between regions of the brain, limiting their ability to capture synchronized activity among larger groups of regions. Second, they model the brain network as a static network, overlooking the temporal changes in the brain. Third, most are designed only for classifying brain networks as healthy or disordered, lacking the ability to identify abnormal brain activity patterns linked to biomarkers associated with disorders. To address these issues, we present HyperBrain, an unsupervised anomaly detection framework for temporal hypergraph brain networks. HyperBrain models fMRI time series data as temporal hypergraphs capturing dynamic higher-order interactions. It then uses a novel customized temporal walk (BrainWalk) and neural encodings to detect abnormal co-activations among brain regions. We evaluate the performance of HyperBrain in both synthetic and real-world settings for Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder (ADHD). HyperBrain outperforms all other baselines on detecting abnormal co-activations in brain networks. Furthermore, results obtained from HyperBrain are consistent with clinical research on these brain disorders. Our findings suggest that learning temporal and higher-order connections in the brain provides a promising approach to uncover intricate connectivity patterns in brain networks, offering improved diagnosis. Our code is available at: https://***/ubc-systopia/HyperBrain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Anomaly detection

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Efficient Identity-Based Dynamic Cloud Storage Data Integrity Auditing with Incremental Updates for Handheld Mobile Devices 2nd

Efficient Identity-Based Dynamic Cloud Storage Data Integrit...

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2nd International Conference on Data Security and Privacy Protection, DSPP 2024

作者： Huang, Yichi Zhang, Futai Yang, Wenjie Yang, Shaojun Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China

ISBN: (纸本)9789819785452

With rapid advancements in handheld mobile devices (HMDs), striking a balance between storage and data security is crucial. Cloud storage has attracted great attention of users for its convenience and availability. However, data integrity becomes challenging once data is uploaded to the cloud, as physical control is lost. In this paper, we propose an efficient identity-based dynamic cloud storage data integrity auditing protocol for HMDs. Our protocol simplifies certificate management and avoids the use of Map to Point (MtP) operation during private key extraction, resulting in a 50% improvement in private key verification efficiency compared to other protocols. We optimize tag generation computation, enhancing efficiency for computation-constrained users. Furthermore, our protocol supports incremental updates for dynamic data operations. We provide detailed system and security models, demonstrating the protocol’s security in ensuring data integrity and auditing soundness. Performance comparisons reveal the computation cost advantages of our protocol, making it ideal for application in cloud-based HMDs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data integrity

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