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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Shi, Qinglei Duan, Wenhan Chen, Wanyi Yang, Haotian Lu, Haochen Wu, Kecan Zhu, Junxi Yuan, Juefei Ke, Qiyan Zhang, Andu Wan, Xiang Wang, Changmiao Ruan, Li Wang, Renzhi Chinese University of Hong Kong Shandong University School of Software Jinan China Sino-French School of Engineering Beihang University Beijing China School of Data Science Chinese University of Hong Kong Shenzhen China School of Computer Science Beihang University Beijing China School of Medicine Zhengzhou University Zhengzhou China School of Medicine Chinese University of Hong Kong Shenzhen Shenzhen China Hebei Medical University Fourth Afliated Hospital University Hebei China Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China Medical Big Data Lab Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science and Engineering Beihang University Beijing China School of Medicine Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9798350386226

Transformer-based models have demonstrated substantial potential in medical image segmentation tasks due to their exceptional ability to capture long-range dependencies. To further enhance segmentation performance, various effective methods have been proposed, including pretraining methods (weakly supervised or self-supervised pretraining schemes), contrastive learning schemes, and knowledge distillation methods. However, segmenting esophageal cancer (EC) from CT images remains a significant challenge, partly due to the complex anatomy of EC, such as variable shapes, extensive extents, and often blurred boundaries with adjacent anatomical structures. In this study, we propose a prior-guided pretraining (PGP) regimen based on bounding boxes, which enhances the model's ability to discern textural differences between EC and the surrounding tissues. Using Swin UNITR as the backbone, our proposed pretraining scheme demonstrates superior performance in EC segmentation compared to other schemes. To further improve the segmentation accuracy of EC, we also addressed the class imbalance and long-tail problems inherent in EC segmentation, thereby further enhancing segmentation performance. © 2024 IEEE.

关键词： Self-supervised learning

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The periodic product recommendation system based on deep reinforcement learning and the multi-objective framework 12

The periodic product recommendation system based on deep rei...

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12th International Conference on Awareness science and Technology, iCAST 2023

作者： Dat, Dang Tien Thang, Tran Ngoc Minh, Nguyen Anh Chen, Rung Ching Ngoc Anh, Nguyen Thi School of Applied Mathematics and Informatics Hanoi University of Science and Technology Viet Nam Big Data Lab CMC Institute of Science and Technology Viet Nam Department of Information Management Chaoyang University of Technology Taiwan

ISBN: (纸本)9798350324693

The primary objective of the recommendation system is to suggest suitable products to users. The need for a personalized recommendation system has become essential with the continuous growth in the number of users and the increasing diversity of products. In this paper, we proposed a novel approach that combines a reinforcement learning algorithm with user clustering to address the recommendation challenge for periodic products. Additionally, we extend our framework to accommodate multi-objective scenarios. Our contributions encompass the development of meaningful representation vectors for actions and states, along with the definition of reward vector aligning with users’ long-term preferences and optimizing profits. We apply our proposed method to the problem of recommending mobile packages in Vietnam. Notably, our approach yields favorable recommendation results and exhibits superior performance compared to alternative methods. © 2023 IEEE.

关键词： Reinforcement learning

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Multimodal Biological Knowledge Graph Completion via Triple Co-Attention Mechanism 39

Multimodal Biological Knowledge Graph Completion via Triple ...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Xu, Derong Zhou, Jingbo Xu, Tong Xia, Yuan Liu, Ji Chen, Enhong Dou, Dejing University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China Baidu Research Business Intelligence Lab Baidu Intelligent Healthcare Unit Baidu Research Big Data Lab BCG X

ISBN: (纸本)9798350322279

Biological Knowledge Graphs (BKGs) can help to model complex biological systems in a structural way to support various tasks. Nevertheless, the incompleteness problem may limit the performance of existing BKGs, which still deserves new methods to reveal the missing relations. Though great efforts have been made to knowledge graph completion, existing methods are not easy to be adapted to the multimodal biological information such as molecular structures and textual descriptions. To this end, we propose a novel co-attention-based multimodal embedding framework, named CamE, for the multimodal BKG completion task. Specifically, we design a Triple Co-Attention (TCA) operator to capture and highlight the same semantic features among different modalities. Based on TCA, we further propose two components to handle multimodal fusion and multimodal entity-relation interaction, respectively. One is the multimodal TCA fusion module to achieve a multimodal joint representation for each entity in the BKG. It aims to project different modal information into a common space by capturing the same semantic features and overcoming the modality gap. The other is the relation-aware interactive TCA module to learn interactive representation by modelling the deep interaction between multimodal entities and relations. Extensive experiments on two real-world multimodal BKG datasets demonstrate that our method significantly outperforms several state-of-the-art baselines, including 10.3% and 16.2% improvement w.r.t MRR and Hits@1 metrics over its best competitors on public DRKG-MM dataset. © 2023 IEEE.

关键词： Knowledge graph

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Towards Effective and Efficient Error Handling Code Fuzzing Based on Software Fault Injection 31

Towards Effective and Efficient Error Handling Code Fuzzing ...

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Chen, Kang Wen, Ming Jia, Haoxiang Wu, Rongxin Jin, Hai Wuhan430074 China Jin YinHu Laboratory Wuhan430074 China School of Informatics Xiamen University Xiamen361005 China School of Computer Science and Technology HUST Wuhan430074 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security China Cluster and Grid Computing Lab China

ISBN: (纸本)9798350330663

Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bug-prone, while sufficiently testing them is challenging as such code often cannot be triggered under normal conditions. Motivated by this, recent studies have proposed to leverage software fault injection (SFI) based fuzzing to discover potential bugs in complicated error handling code. Despite the promising results achieved, their effectiveness and efficiency are still compromised in practice due to the huge search space of error sites, inadequate fuzzing guidance, and the overhead induced by context-sensitive SFI. To achieve effective and efficient testing of error handling code, this study presents AFL-FI, which first utilizes a similarity-based method to identify suspicious error sites, and then incorporates the idea of error site coverage to guide the fuzzing process. Finally, the design of lightweight context-sensitive SFI enables AFL-FI to execute test cases efficiently. We evaluate AFL-FI on eight large-scale open-source projects, and the results show that it can outperform existing state-of-the-art fuzzing tools significantly in terms of branch code coverage. More importantly, AFL-FI has discovered 13 previously unknown bugs, and all of them have been confirmed while 12 of them have been fixed. Besides, our evaluation also demonstrates that all the key designs of AFL-F I are effective that contribute significantly to its overall performance. © 2024 IEEE.

关键词： Errors

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Explainable Deep Learning with Embedded Feature Selection for Electricity Demand Forecasting

Explainable Deep Learning with Embedded Feature Selection fo...

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International Conference on Smart Systems and Technologies (SST)

作者： M. J. Jiménez-Navarro A. R. Troncoso-García A. Troncoso F. Martínez-Álvarez M. Martínez-Ballesteros Department of Computer Languages and Systems University of Seville Seville Spain Data Science and Big Data Lab Pablo de Olavide University Seville Spain

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

Electricity demand forecasting is an important part of the energy industry strategy. Accurate predictions are crucial for maintaining a stable energy supply, planning production, managing distribution, preventing grid overloads, integrating renewable energy sources, and reducing costs and environmental impact. Machine learning and, in particular, deep learning are promising techniques to improve the prediction accuracy of electric demand, but face challenges related to a lack of interpretability due to the "black box" nature of some models. Feature selection methods address these issues by identifying relevant features and simplifying the learning process, This paper aims to explain the most critical lags that impact electric demand forecasting in Spain using the temporal selection layer technique within deep learning models for time series forecasting. This technique transforms a neural network into a model with embedded feature selection, aiming to enhance efficacy and interpretability while reducing computational costs. The results were compared with other methods that incorporate an embedded feature selection mechanism to select the best model. Furthermore, an explainable technique is used to assess the feature importance in the best model over the last year to understand how input features influence electric demand forecasting and provide insights into their contributions and interactions. The results show that our approach improves both the efficacy and interpretability in the context of electric demand forecasting.

关键词： Deep learning Accuracy Electricity Computational modeling Time series analysis Neural networks Demand forecasting Transforms Predictive models Feature extraction

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FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph Neural Network 27

FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph N...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Du, Boyu Zhou, Jingya Liu, Ling She, Xiaolong School of Computer Science and Technology Soochow University China Engineering Lab.of Big Data and Intelligence of Jiangsu Province Soochow University China State Key Lab.for Novel Software Technology Nanjing University China School of Computer Science Georgia Institute of Technology United States

ISBN: (纸本)9781643685489

Graph Neural Networks (GNNs) have become an important graph feature learning paradigm that is extensively applied to graph inference ***, GNNs still have limitations in some aspects such as representation capability, interpretability, *** this end, this paper introduces a novel hybrid Fuzzy-Logic Graph Neural Network (FL-GNN) that synergistically combines Fuzzy Neural Network (FNN) and GNN to effectively capture and aggregate local information flows within the ***-GNN exhibits three distinctive ***, we incorporate a specialized structure fuzzy rule to enhance FL-GNN's graph inference capability, surpassing representative GNN ***, we augment the interpretability of FL-GNN by integrating analytical exploration methods from two perspectives: Fuzzy Inference System and Message Passing Algorithm (MPA).Lastly, we refine the structure of FL-GNN based on MPA to optimize its calculation complexity and consequently improve learning *** results show that FL-GNN outperforms existing representative graph neural networks for graph inference tasks. © 2024 The Authors.

关键词： Graph neural networks

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Efficient Modeling Attack on Multiplexer PUFs via Kronecker Matrix Multiplication

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Wang, Hongfei Wan, Caixue Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

The Physical Unclonable Function (PUF) is valued for its lightweight nature and unique functionality, making it a common choice for securing hardware products requiring authentication and key generation mechanisms. In response to the susceptibility of individual PUFs to modeling attacks, advanced PUF variants have been developed to improve security measures. One notable type in this regard is the multiplexer-based composition of arbiter PUFs, known as MPUF, which aims to meet high reliability and security standards simultaneously. Current research on attacking MPUF encounters challenges such as substantial demands for training CRPs and low success rates. In this work, we propose a novel numerical modeling attack strategy for MPUFs. Using Kronecker products from mathematical perspective, this method precisely describe the MPUF model without using complex network architectures, boosting attack accuracy and overall efficiency. Experiment comparison with state-of-the-art works demonstrates that our method achieves better performance in terms of attack accuracy, robustness, and efficiency. Our method is able to successfully attack a (512, 8)-MPUF in 32.71 minutes with 97.14% accuracy, outperforming all existing attack methods on MPUFs. More, we validate our method through experiments with hardware implementations on FPGAs. The advantages of our method also include the adaptability to be employed to attack other MPUF variations like cMPUF and rMPUF, and the capability to be integrated with an existing attack method for launching efficient attack on MPUFs leveraging reliability information. © 2025 IEEE.

关键词： modeling attack multiplexer-based PUF Physical unclonable function

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BDLR:lncRNA identification using ensemble learning

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BIOCELL 2022年第4期46卷 951-960页

作者： LEJUN GONG SHEHAI ZHOU JINGMEI CHEN YONGMIN LI LI ZHANG ZHIHONG GAO Jiangsu Key Lab of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China

Long non-coding RNAs(lncRNAs)play an important role in many life activities such as epigenetic material regulation,cell cycle regulation,dosage compensation and cell differentiation regulation,and are associated with many human *** are many limitations in identifying and annotating lncRNAs using traditional biological experimental *** the development of high-throughput sequencing technology,it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning *** on the Bagging method and Decision Tree algorithm in ensemble learning,this paper proposes a method of lncRNAs gene sequence identification called *** identification results of this classification method are compared with the identification results of several models including Byes,Support Vector Machine,Logical Regression,Decision Tree and Random *** experimental results show that the lncRNAs identification method named BDLR proposed in this paper has an accuracy of 86.61%in the human test set and 90.34%in the mouse for lncRNAs,which is more than the identification results of the other ***,the proposed method offers a reference for researchers to identify lncRNAs using the ensemble learning.

关键词： lncRNAs High-throughput sequencing Ensemble learning Bagging Decision Tree

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Effective and Efficient big OLAP data Cube Compression in Mobile Cloud Environments: The IQTS Algorithm

Effective and Efficient Big OLAP Data Cube Compression in Mo...

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2023 IEEE International Conference on big data, bigdata 2023

作者： Cuzzocrea, Alfredo Hajian, Mojtaba University of Calabria IDEA Lab Rende Italy University of Paris City Dept. of Computer Science Paris France Big Data Management and Analytics at University of Paris City Paris France

ISBN: (纸本)9798350324457

This paper introduces the Indexed Quad-Tree Summary (IQTS) algorithm, along with its concepts, models, and 'philosophy'. IQTS allows us to compress multidimensional OLAP views derived from big OLAP data cubes that populate Mobile Clouds. In addition to this, IQTS effectively and efficiently supports approximate query answering over compressed multidimensional OLAP views. These functionalities turn to be enabling functionalities for big data applications in a wide collection of modern scenarios, such as healthcare analytics. In light of these considerations, this paper provides motivations, anatomy and procedures of IQTS, enriched by several case studies that contribute to pinpoint the benefits coming from our proposed algorithm. © 2023 IEEE.

关键词： Approximate Query Answering big data big OLAP Cubes Intelligent big data Applications Mobile Clouds

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Trustworthy AI: Securing Sensitive data in Large Language Models

arXiv

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arXiv 2024年

作者： Feretzakis, Georgios Verykios, Vassilios S. Big Data Analytics and Anonymization Lab School of Science and Technology Hellenic Open University Patras Greece

Large Language Models (LLMs) have transformed natural language processing (NLP) by enabling robust text generation and understanding. However, their deployment in sensitive domains like healthcare, finance, and legal services raises critical concerns about privacy and data security. This paper proposes a comprehensive framework for embedding trust mechanisms into LLMs to dynamically control the disclosure of sensitive information. The framework integrates three core components: User Trust Profiling, Information Sensitivity Detection, and Adaptive Output Control. By leveraging techniques such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), Named Entity Recognition (NER), contextual analysis, and privacy-preserving methods like differential privacy, the system ensures that sensitive information is disclosed appropriately based on the user's trust level. By focusing on balancing data utility and privacy, the proposed solution offers a novel approach to securely deploying LLMs in high-risk environments. Future work will focus on testing this framework across various domains to evaluate its effectiveness in managing sensitive data while maintaining system efficiency. © 2024, CC BY.

关键词： Access control models

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