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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning"

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MHHCR: Multi-behavior Heterogeneous Hypergraph Contrastive Recommendation 25th

MHHCR: Multi-behavior Heterogeneous Hypergraph Contrastive R...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Li, Yiheng Lu, Weihai Shanghai University of International Business and Economics Shanghai China Peking University Beijing China

ISBN: (纸本)9789819605699

Multi-behavior graph recommendation systems can simultaneously consider various user behaviors on the platform, such as clicks, purchases, comments, etc., thus more comprehensively capturing user behavior patterns and preferences and handling heterogeneous relationships. Despite achieving commendable results, recommender systems based on hypergraphs still face challenges. Firstly, the effectiveness of Collaborative Filtering(CF) is affected when encountering real-world data with skewed distributions. Secondly, large-scale auxiliary behaviors contain a lot of noise. In order to address these issues, we propose a novel recommendation framework Multi-behavior Heterogeneous Hypergraph Contrastive Recommendation (MHHCR), including hypergraph global dependency learning and contrastive learning tasks to solve above challenges, respectively. Extensive experiments demonstrate that the effectiveness of MHHCR on two real-world datasets, achieving SOTA performance over various state-of-the-art baselines. Source code is available at https://***/Anticoder1/MHHCR-rec. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Confluence of Machine learning and Internet of things for E-Healthcare Security 8th

Confluence of Machine Learning and Internet of Things for E-...

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8th international conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Pattnayak, Parthasarathi Das, Tulip Mohanty, Arpeeta Patnaik, Sanghamitra School of Computer Applications KIIT Deemed to be University Odisha Bhubaneswar India Faculty of Emerging Technologies SRI SRI University Odisha Cuttack India School of Computer Applications KIIT Deemed to be University Odisha Bhubaneswar India School of Law KIIT Deemed to be University Odisha Bhubaneswar India

ISBN: (纸本)9789819784219

In the current era, the e-communication technology is developing swiftly and has made a remarkable transformation in e-healthcare sector by facilitating efficient and secure transmission of patient’s data. Internet of things (IoT) is developing drastically in the e-healthcare sector and the intelligent devices are also improving with many new innovative enhancements. the confluence of IoT and ML is manifested in many sectors but its implementation in e-healthcare sector provides real-time surveillance and monitoring of patient’s data and health status through prediction, anticipation and the capability of taking decisions intelligently. Nowadays, cryptographic and biometric systems, anomaly detection and machine learning (ML) approaches for authentication are being widely used for ensuring the security and functioning of healthcare systems. the present study aims to know how the convergence of IoT e-healthcare management system (EHMS) and ML techniques helps to design automated systems for proper monitoring and intelligent decision making. It also focuses to present an innovative architecture to safeguard the real-time healthcare from outside threats with the consumption of less possible resources of low-powered devices. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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A Phishing Website Detection System Based on Hybrid Feature engineering with SHAP Explainable Artificial Intelligence Technique

A Phishing Website Detection System Based on Hybrid Feature ...

引用

PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web data Analytics, AIWDA 2024 form the 25th international conference on Web Information Systems engineering, WISE 2024

作者： Alsuqayh, Norah Mirza, Abdulrahman Alhogail, Areej College of Computer and Information Sciences King Saud University Riyadh11451 Saudi Arabia

ISBN: (纸本)9789819614820

Today, phishing website attacks have emerged as a prevalent threat affecting Internet users, governments, and businesses. the key challenge is that phishers continuously deploy new techniques to create zero-day phishing attacks. Recently, researchers have suggested anti-phishing techniques based on classifiers such as machine learning and deep learning methods. this research aims to develop a framework for predicting zero-day phishing websites through introducing new hybrid feature engineering methods that help adapt to evolving threats by extracting meaningful attributes or characteristics of phishing websites, including content-based and URL-based features. By combining diverse feature types, a detection system can capture a comprehensive set of characteristics associated with phishing behavior, improving the accuracy of the detection process. In addition, we also apply SHapley Additive exPlanations (SHAP) technique to interpret the behavior of the hybrid feature engineering by quantifying the impact of individual features on model predictions, providing a clear understanding of which features contribute most significantly to phishing detection. Moreover, the proposed approach aims to capture the most informative and discriminative features, reducing the likelihood of overlapping features. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Phishing

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Discovering Causal Relationships in Noisy Web data for Sentiment Classification Using Attention Mechanisms

Discovering Causal Relationships in Noisy Web Data for Sen...

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作者： Mihoubi, Miloud Zerkouk, Meriem Chikhaoui, Belkacem Artificial Intelligence Institute University of Téluq 5800 rue Saint-Denis bureau 1105 MontrealQCH2S 3L5 Canada

ISBN: (纸本)9789819614820

In the era of big data, sentiment analysis on social media platforms presents unique challenges due to the noisy and unstructured nature of the data. this study introduces a novel deep learning approach for discovering causal relationships within such noisy web data using an attention-enhanced architecture. the proposed model leverages an embedding layer, bidirectional LSTM, attention mechanisms, global average pooling, and dropout techniques, followed by a dense layer with sigmoid activation for binary classification. We validated our model on the Sentiment140 dataset, achieving a test accuracy of 82.36% and an F1-score of 0.8246, outperforming traditional machine learning models such as Logistic Regression, Support Vector Machines (SVM), and Random Forests. An extensive ablation study was conducted to assess the contributions of key model components, confirming the critical role of attention mechanisms and bidirectional LSTM in improving performance and interpretability. Beyond its superior performance in sentiment classification, our model provides insights into the causal dynamics driving sentiment shifts on social media. this work significantly contributes to the fields of web data mining and causal inference by offering a robust framework capable of handling the complexities of noisy, high-dimensional data commonly encountered in online environments.. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Context-Aware Selection of Machine learning as a Service (MLaaS) in IoT Environments

Context-Aware Selection of Machine Learning as a Service ...

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作者： Patel, Keya Mistry, Sajib Kanneganti, Deepak Krishna, Aneesh School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia

ISBN: (纸本)9789819614820

the integration of Machine learning as a Service (MLaaS) into the Internet of things (IoT) environments presents considerable opportunities for enhancing decision-making and automation. We propose a novel framework for context-aware selection of MLaaS in IoT settings, aimed at optimising the interaction between IoT users’ activities and machine learning services. Our framework considers various contextual dimensions, such as user preferences, locations, IoT device capabilities, and application requirements, to develop a dynamic selection process. By employing context-aware algorithms, our approach seeks to enhance the efficiency, accuracy, and responsiveness of IoT systems. We propose a context change analysis algorithm based on support vector machines (SVM). We develop a contextual bandits algorithm along with skyline queries to achieve optimal mapping between abstract MLaaS services and concrete MLaaS services for quality of service (QoS) attributes. Experiments conducted with real-world and simulated datasets demonstrate the effectiveness of our proposed methods. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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PR-Rank: A Parameter Regression Approach for learning-to-Rank Model Adaptation Without Target Domain data 25th

PR-Rank: A Parameter Regression Approach for Learning-to-Ra...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Ito, Takumi Maruta, Atsuki Kato, Makoto P. Fujita, Sumio University of Tsukuba Tsukuba Japan LY Corporation Chiyoda Japan

ISBN: (纸本)9789819605729

this paper addresses a problem of constructing a learning-to-Rank (LtR) model tailored to a target domain without using any domain-specific queries and relevance judgements. Our proposed method, PR-Rank, incorporates domain features, which are represented in a real-valued vector and can be estimated by domain experts, for adapting LtR models. the key component in our method is a parameter regression model that learns to regress the optimal parameters of the LtR model from the domain features. this eliminates the need for access to users’ queries and relevance judgements in a target domain, which is often unavailable in new and emerging services. In our experiments, we compared the performance of the proposed method against a domain-agnostic method, using publicly available LtR datasets including OHSUMED, MQ2007/2008, TREC Web track, and MSLR. the results showed that our method could outperform the baseline model trained on a large amount of data without considering domain differences. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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CLIMB: Imbalanced data Modelling Using Contrastive learning with Limited Labels 25th

CLIMB: Imbalanced Data Modelling Using Contrastive Learning ...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Alsuhaibani, Abdullah Razzak, Imran Jameel, Shoaib Wang, Xianzhi Xu, Guandong University of Technology Sydney School of Computer Science Sydney Australia The University of New South Wales Sydney Australia University of Southampton Southampton United Kingdom Islamic University of Madinah Medina Saudi Arabia The Education University of Hong Kong HK Hong Kong

ISBN: (纸本)9789819605729

Machine learning classifiers typically rely on the assumption of balanced training datasets, with sufficient examples per class to facilitate effective model learning. However, this assumption often fails to hold. Consider a common scenario where the positive class has only a few labelled instances compared to thousands in the negative class. this class imbalance, coupled with limited labelled data, poses a significant challenge for machine learning algorithms, especially in the ever-growing data landscape. this challenge is further amplified when dealing with short text datasets, as these inherently provide less information for computational models to leverage. While techniques like data sampling and fine-tuning pre-trained language models exist to address these limitations, our analysis reveals their inconsistencies in achieving reliable performance. We propose a novel model that leverages contrastive learning within a two-stage approach to overcome these challenges. Our proposed framework involves unsupervised Fine-Tuning of a language model to learn representation on short text followed by fine-tuning on a few labels integrated with GPT-generated text using a novel contrastive learning algorithm designed to effectively model short texts and handle class imbalance simultaneously. Our experimental results demonstrate that the proposed method significantly outperforms established baseline models. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Enhancing Federated learning Robustness in Non-IID data Environments via MMD-Based Distribution Alignment 25th

Enhancing Federated Learning Robustness in Non-IID Data Env...

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25th international conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Ma, Xiao Shen, Hong Lyu, Wenqi Ke, Wei Faculty of Applied Sciences Macao Polytechnic University China School of Engineering and Technology Central Queensland University Rockhampton Australia

ISBN: (纸本)9789819642069

Federated learning(FL), due to its distributed nature, is highly susceptible to malicious attacks. Although various Byzantine-robust FL methods exist, they often fail to maintain robustness in practical scenarios due to the non-independent and identically distributed (Non-IID) nature of client data. Moreover, existing FL methods often suffer from weight divergence caused by heterogeneous data distributions across clients. To address these issues, we propose a novel federated learning framework that aligns local data distributions across different clients to enhance robustness for Non-IID data in adversarial environments. It contains a feature transformation layer that incorporates Maximum Mean Discrepancy (MMD) as a regularization term to avoid weight divergence through aligning local and global data distributions without sharing raw data. Our approach dynamically updates the statistical information of both local and global data, including the mean and variance, ensuring that local models are closely aligned with the global model throughout training. Experimental results on MNIST and CIFAR-10 datasets demonstrate that our proposed framework significantly improves robustness both in the absence of attacks and against untargeted attacks such as sign-flipping and additive noise. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Improving the Robustness of Rumor Detection Models with Metadata-Augmented Evasive Rumor datasets 25th

Improving the Robustness of Rumor Detection Models with M...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Huynh, Larry Gansemer, Andrew Kim, Hyoungshick Hong, Jin B. The University of Western Australia Perth Australia Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9789819605750

Rumors on social media can cause serious harm. Advances in NLP enable deceptive rumors resembling real posts, necessitating more robust detection. One approach collects and augments a dataset with adversarial rumors meant to evade models. Understanding evasive rumors and adding them to a dataset improves model robustness. We demonstrate effective data augmentation that significantly improves detection models. State-of-the-art accuracy drops by up to 29.5% against evasive rumors, while our augmentation raises it by up to 14.62%. Results highlight data augmentation’s importance for robust detection models countering evasion. Our evaluation shows the value of augmentation for developing models robust against adversarial attacks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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CSA4Rec: Collaborative Signals Augmentation Model Based on GCN for Recommendation 25th

CSA4Rec: Collaborative Signals Augmentation Model Based on ...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Liu, Haibo Yu, Lianjie Si, Yali Liu, Jinglian School of Cyber Security and Computer Hebei University Baoding Baoding China School of Computer Science and Engineering Changshu Institute of Technology Suzhou China

ISBN: (纸本)9789819605699

Graph Convolution Network (GCN) is a potent deep learning methodology and GCN-based graph data augmentation methods excel in recommender systems. the augmented data, as the higher-order collaborative signals, is generated by pre-training the GCN model on raw data, before making the final prediction. Despite their success, three problems have always existed. First, during the pre-training phase, over-smooth and data sparsity have a serious impact on the quality of augmented graph data. Second, the influence degree of higher-order collaborative signals are difficult to control, resulting in unstable embeddings. Last, the output of the pre-trained model is only used to augment the data and is not utilized in subsequent training phases, leading to performance penalty. In this study, we propose an efficient Collaborative Signals Augmentation model based on GCN for Recommendation (CSA4Rec). In the pre-training phase, graph contrastive learning is utilized to generate distinct embeddings to address over-smooth and data sparsity. To manipulate the higher-order collaborative signals effectively, the signals are generated from raw data in the modeling phase and aggregated independently by GCN in the training phase. the Residual-like connection is employed to leverage the output of the pre-trained model to improve the convergence speed in the next training phase. Extensive experiments demonstrate the effectiveness and rationality of CSA4Rec. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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