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A blockchain-empowered authentication scheme for worm detection in wireless sensor network

Digital Communications and Networks 2024年第2期10卷 265-272页

作者： Yuling Chen Xiong Yang Tao Li Yi Ren Yangyang Long State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Computing Science University of East AngliaNorwichNR57TJUK

Wireless Sensor Network(WSN)is a distributed sensor network composed a large number of nodes with low cost,low performance and *** special structure of WSN brings both convenience and *** example,a malicious participant can launch attacks by capturing a physical ***,node authentication that can resist malicious attacks is very important to network ***,blockchain technology has shown the potential to enhance the security of the Internet of Things(IoT).In this paper,we propose a Blockchain-empowered Authentication Scheme(BAS)for *** our scheme,all nodes are managed by utilizing the identity information stored on the ***,the simulation experiment about worm detection is executed on BAS,and the security is evaluated from detection and infection *** experiment results indicate that the proposed scheme can effectively inhibit the spread and infection of worms in the network.

关键词： Wireless Sensor Network(WSN) Node authentication Blockchain Tangle Worm detection

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Bayesian Inverse Transfer in Evolutionary Multiobjective Optimization

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-27页

作者： Liu, Jiao Gupta, Abhishek Ong, Yew-Soon College of Computing and Data Science Nanyang Technological University Singapore Singapore School of Mechanical Sciences Indian Institute of Technology Goa Goa India

Transfer optimization enables data-efficient optimization of a target task by leveraging experiential priors from related source tasks. This is especially useful in multiobjective optimization settings where a set of tradeoff solutions is sought under tight evaluation budgets. In this article, we introduce a novel concept of inverse transfer in multiobjective optimization. Inverse transfer stands out by employing Bayesian inverse Gaussian process models to map performance vectors in the objective space to population search distributions in task-specific decision space, facilitating knowledge transfer through objective space unification. Building upon this idea, we introduce the first Inverse Transfer Evolutionary Multiobjective Optimizer (invTrEMO). A key highlight of invTrEMO is its ability to harness the common objective functions prevalent in many application areas, even when decision spaces do not precisely align between tasks. This allows invTrEMO to uniquely and effectively utilize information from heterogeneous source tasks as well. Furthermore, invTrEMO yields high-precision inverse models as a significant byproduct, enabling the generation of tailored solutions on-demand based on user preferences. Empirical studies on multi-and many-objective benchmark problems, as well as a practical case study, showcase the faster convergence rate and modeling accuracy of the invTrEMO relative to state-of-The-Art evolutionary and Bayesian optimization algorithms. The source code of the invTrEMO is made available at https://***/LiuJ-2023/invTrEMO. © 2024 Copyright held by the owner/author(s).

关键词： Budget control

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Attention boosted Individualized Regression 38

Attention boosted Individualized Regression

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Guang Cao, Yuan Feng, Long Department of Data Science College of Computing City University of Hong Kong Hong Kong Department of Statistics and Actuarial Science School of Computing and Data Science The University of Hong Kong Hong Kong

Different from classical one-model-fits-all strategy, individualized models allow parameters to vary across samples and are gaining popularity in various fields, particularly in personalized medicine. Motivated by medical imaging analysis, this paper introduces a novel individualized modeling framework for matrix-valued data that does not require additional information on sample similarity for the individualized coefficients. Under our framework, the model individualization stems from an optimal internal relation map within the samples themselves. We refer to the proposed method as Attention boosted Individualized Regression, due to its close connections with the self-attention mechanism. Therefore, our approach provides a new interpretation for attention from the perspective of individualized modeling. Comprehensive numerical experiments and real brain MRI analysis using an ADNI dataset demonstrated the superior performance of our model. © 2024 Neural information processing systems foundation. All rights reserved.

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ZeroMark: Towards dataset Ownership Verification without Disclosing Watermarks 38

ZeroMark: Towards Dataset Ownership Verification without Dis...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Guo, Junfeng Li, Yiming Chen, Ruibo Wu, Yihan Liu, Chenxi Huang, Heng Department of Computer Science Institute of Health Computing University of Maryland College Park United States College of Computing and Data Science Nanyang Technology University Singapore

High-quality public datasets significantly prompt the prosperity of deep neural networks (DNNs). Currently, dataset ownership verification (DOV), which consists of dataset watermarking and ownership verification, is the only feasible solution to protect their copyright by preventing unauthorized use. In this paper, we revisit existing DOV methods and find that they all mainly focused on the first stage by designing different types of dataset watermarks and directly exploiting watermarked samples as the verification samples for ownership verification. As such, their success relies on an underlying assumption that verification is a onetime and privacy-preserving process, which does not necessarily hold in practice. To alleviate this problem, we propose ZeroMark to conduct ownership verification without disclosing dataset-specified watermarks. Our method is inspired by our empirical and theoretical findings of the intrinsic property of DNNs trained on the watermarked dataset. Specifically, ZeroMark first generates the closest boundary version of given benign samples and calculates their boundary gradients under the label-only black-box setting. After that, it examines whether the given suspicious method has been trained on the protected dataset by performing a hypothesis test, based on the cosine similarity measured on the boundary gradients and the watermark pattern. Extensive experiments on benchmark datasets verify the effectiveness of our ZeroMark and its resistance to potential adaptive attacks. The codes for reproducing our main experiments are publicly available at GitHub. © 2024 Neural information processing systems foundation. All rights reserved.

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Traffic Sign Detection Using YOLOv8 2

Traffic Sign Detection Using YOLOv8

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2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Kumar, Rahul Gupta, Aniket Rajeswari, D. School of Computing College of Engineering and Technology Department of Data Science and Business Systems India

ISBN: (纸本)9798350327533

Traffic signals and other signs like parking., stop signs., etc. have become very crucial in autonomous and s elf-driving cars as it helps the smart system to comply with the basic traffic rules along with that it helps navigate routes based on the signs thus enabling a more secure driving experience for the drivers. There have been a lot of new algorithms that have emerged in the past recent years regarding this. In this paper., this research has used the new YOLOv8 object detection system to help us detect traffic signs as it is much fas ter and more precis e than its previous iterations. To improve the algorithm., this paper has used a dataset comprising photos of traffic signs taken at different angles and different light intensities. This system can predict the traffic signs with 93% accuracy. © 2024 IEEE.

关键词： Autonomous vehicle Deep Learning Traffic sign detection yolov8

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Calibrating the Principal Point of Vehicle-Mounted Fisheye Cameras Using Point-Oriented Representation

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IEEE Sensors Journal 2025年第11期25卷 19602-19612页

作者： Zhou, Juncheng Lin, Chunyu Yang, Shangrong Liao, Kang Zhao, Yao Beijing Jiaotong University Beijing Visual Intelligence + X International Joint Laboratory of the Ministry of Education Institute of Information Science China Nanyang Technological University College of Computing and Data Science Singapore

Estimating the principal point for vehicle-mounted fisheye cameras is essential for accurate vision tasks. However, traditional calibration methods require human intervention and specific patterns, lacking flexibility and adaptability to different camera models during camera change. In this study, we propose an automatic, flexible, end-to-end self-calibration framework that predicts the principal point from a single fisheye image. First, considering the dimensional differences between the high-dimensional image features and the two-dimensional principal point coordinates, directly regressing the parameters is unsuitable for our task. To address this, we propose a point-oriented representation map to represent the principal point locations, leveraging the distortion characteristics of the fisheye camera. This approach aligns more effectively with the dense prediction capabilities of neural networks. To enhance the accuracy of the generated principal point representation map, we introduce a Contourlet-Semantic Extract Network, which is specially designed to extract the fisheye image’s semantic and edge features. Using these features, we create an initial principal point map, refined with edge-based offsets and resampled for precision. This process captures finer edge details in autonomous driving scenarios (e.g., lane markings), enabling the network to more accurately understand the distortion distribution within the image. For training and evaluation, we have compiled a fisheye image dataset from driving scenes featuring 20 distinct principal point locations. Additionally, we introduce a circular cropping data augmentation technique that generates fisheye images with different principal points. Extensive experiments demonstrate the effectiveness and superiority of the proposed method. The code and dataset will be released at https://***/BJTU-zjc/FCPPE-Net. © 2001-2012 IEEE.

关键词： Image enhancement

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Enhancing Protein Language Model With Feature Integration for Anticancer Peptide Prediction

Enhancing Protein Language Model With Feature Integration fo...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Binte Sayuti, Tiara Natasha Cheng, Shen Ajith, Santhisenan Bin Abdul Samad, Abdul Hadi Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Health Informatics Lab Singapore College of Computing and Data Science Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350351552

In the fight against cancer, anticancer peptides (ACP) hold promising therapeutic potential due to their selective cytotoxicity and lower side effects compared to traditional treatments. However, identifying novel ACP is challenged by high costs and labor-intensive processes. Protein language models (PLMs), such as ESM-2 and ProtBERT, have revolutionized peptide prediction by leveraging vast datasets to capture complex biological patterns through pre-training. However, they often struggle to accurately model specific biochemical interactions. To address this limitation, we integrated four sequence-based features: amino acid composition (AAC), dipeptide composition (DPC), composition of k-spaced amino acid group pairs (CKS), and k-mer sparse matrix (k-mer) through a cross-attention mechanism. These features infuse biochemical insights that PLM alone may overlook, enabling a more detailed prediction of anti-cancer properties. This integration enhances biochemical insights, improving prediction accuracy by 15.8% for ProtBERT and 2.9% for ESM-2, with ESM-2 achieving the highest accuracy at 77.8%. SHapley Additive exPlanations (SHAP) analysis confirms the importance of these features, demonstrating that incorporating amino acid features into PLMs enhances ACP prediction. © 2024 IEEE.

关键词： Prediction models

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SMMGCL: A novel multi-level graph contrastive learning framework for integrating spatial multi-omics data

SMMGCL: A novel multi-level graph contrastive learning frame...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Bo Liu, Wei Luo, Jiawei Chen, Xiangtao Kwoh, Chee Keong Hunan University College of Computer Science and Electronic Engineering China Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798350386226

Recent advances in spatial omics technologies have allowed various omics data to be obtained from a single tissue section. To fully explore the relationships among these different types of omics data, it is urgent to develop more effective methods for spatial multi-omics data integration. In this work, we propose a novel Multi-level Graph Contrastive Learning framework, named SMMGCL, to simultaneously mine complementary information at both spot and graph levels for integrating Spatial Multi-omics data. Specifically, to adaptively fuse multi-omics modalities, we first design a multi-modality autoencoder that integrates spatial locations with spot omic expressions to extract modality-specific embeddings. These embeddings are then fused into a consensus representation using an attention mechanism to capture spot-level cross-omics representations. Next, to explore the complex inter-omic structural information, we connect corresponding spots across different omics adjacency graphs into a heterogeneous graph. We then employ a graph convolutional network (GCN) to extract spatial correlations across the omics, learning a graph-level cross-omics global representation. Finally, SMMGCL aligns feature similarity matrixes between spot-level and graph-level representations with their pseudo-label similarity matrix, ensuring multi-level clustering consistency and leading to more accurate spatial multi-omics integration. Experimental results on simulated and real datasets from across tissues show that SMMGCL consistently outperforms other state-of-the-art methods in spatial multi-omics integration performance. The code for SMMGCL is available for download from the GitHub repository at https://***/cs-wangbo/SMMGCL. © 2024 IEEE.

关键词： Contrastive Learning

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Efficient allocation of independent gridlet on small, medium, and large grid

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Personal and Ubiquitous computing 2023年第3期27卷 1029-1037页

作者： Rajeswari, D. Ramamoorthy, S. Srinivasan, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

Gridlet allocation in a computational grid environment is a major research issue to obtain not only the efficient gridlet allocation technique but also the time needed to obtain the efficient allocation technique. Grid computing networks have various nodes to process user jobs. To achieve the high performance of computational grid, task scheduling is an important issue. The users who are using services of grid systems are more cautious about time to complete their job. Hence, this work concentrates on gridlet allocation method used for time reduction by a genetic algorithm with the MapReduce programming model for independent tasks in computational grid. In computational grid environment, multi-objective problem formulation minimization of makespan and flowtime is considered. In this proposed technique, fitness function formulation for makespan and flowtime has been formulated mathematically. The genetic algorithm with the MapReduce programming model is implemented using MapReduce written in Java and then combined with GridSim. The experimental outcome with regard to time needed, flowtime, and makespan clearly reveals that the genetic algorithm with the MapReduce model effectively optimizes time, makespan, and flowtime in computational grid environment. A comparative study of performance efficiency among genetic algorithm with the MapReduce and sequential genetic algorithm (SGA) and parallel genetic algorithm (PGA) depicts the usefulness of the model. The execution time achieved by GA with the MapReduce model in small grid is 10.48 s, medium grid is 18.76 s, and large grid is 33.73 s. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： MapReduce

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Optimised EfficientNet for multi-class skin cancer diagnosis: integrating chaotic grey wolf algorithms

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Current science 2025年第7期128卷 728-736页

作者： Rama, P. Angayarkanni, V. Theresa, W. Gracy Pandiaraj, A. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603 203 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai600 123 India

The detection of skin cancer holds paramount importance worldwide due to its impact on global health. While deep convolutional neural networks (DCNNs) have shown potential in this domain, current approaches often struggle with fine-grained variability in skin lesion features, imbalanced datasets and inadequate augmentation. The objectives of our study encompass building a model that not only enhances accuracy but also reduces training time and cost, improves dimensionality reduction during classification and segmentation, and is evaluated on larger datasets for robustness. We enhance the learning process using an improved nature-inspired optimisation algorithm tailored specifically for skin cancer classification tasks. This algorithm optimises model parameters to maximise classification accuracy while minimising computational overhead. Our proposed architecture leverages a gold standard dermatological image dataset meticulously curated and validated by experts. This dataset spans eight distinct classes, including actinic keratosis, basal cell carcinoma, melanoma, nevus, and others, ensuring a diverse representation of skin conditions. The balanced class distribution in the training and test sets enables fair evaluation and robustness assessment of our deep learning models. The developed algorithm showcases novelty in its approach, contributing to advancements in early skin cancer detection technologies. This study introduces an optimised deep learning model designed for the early detection of skin cancer, achieving a notable accuracy of 97.48%. Our study aims to contribute significantly to the medical field by providing a reliable and efficient tool for dermatologists and healthcare practitioners to assist in diagnosing skin conditions accurately and promptly, ultimately improving patient outcomes and healthcare efficiency. © (2025), (Indian Academy of sciences). All rights reserved.

关键词： Convolutional neural networks

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