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6th International Conference on Emerging Technologies in computing, iCETiC 2023

作者： Teng, Wil Liam Salam, Iftekhar Yau, Wei-Chuen Teh, Jia Yew School of Computing and Data Science Xiamen University Malaysia Sepang43900 Malaysia

ISBN: (纸本)9783031502149

Lightweight cryptography aims to design secure and efficient cryptographic algorithms for resource-constrained devices. Traditional cryptographic algorithms may not be readily usable in resource-constrained environments. To standardise cryptographic solutions tailored for such resource-constraint environments, the National Institute of Standards and Technology (NIST) launched the Lightweight Cryptography (LWC) project. Grain-128AEAD is a stream cipher-based finalist in the NIST LWC project. In this work, we examine the security of the initial version of Grain-128AEAD against cube attacks. We present distinguishing attacks on a reduced-round version of the cipher, assuming that the keystream can be observed immediately after the reduced-round initialisation of the pre-output generator. We obtained various cubes of sizes 25 to 45 for reduced-round Grain-128AEAD. The best cube reported in this work can distinguish the output of a 165-round initialisation of Grain-128AEAD with a cube size of 35. The complexity of the distinguishing attack is O(235). The results are confirmed experimentally. We conclude that even with fewer rounds of initialisation for the first version of Grain-128AEAD, the cipher still has a good security margin against cube attacks. © 2024, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Geometry

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A Comprehensive Approach to Mitigate Return-Oriented Programming Attacks: Combining Operating System Protection Mechanisms and Hardware-Assisted Techniques 8

A Comprehensive Approach to Mitigate Return-Oriented Program...

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8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023

作者： Zhang, Xingnan Huang, Jingjia Feng, Yue Xiamen University Computing & Data Science Sepang Malaysia

ISBN: (纸本)9798350310931

This paper proposes a comprehensive approach to mitigate ROP (Return-Oriented Programming) attacks by combining internal operating system protection mechanisms and hardware-assisted techniques. Through extensive literature review, we identify the effectiveness of ASLR (Address Space Layout Randomization) and LBR (Last Branch Record) in preventing ROP attacks. We present a process involving buffer overflow detection, hardware-assisted ROP attack detection, and the use of Turing detection technology to monitor control flow behavior. We envision a specialized tool that views and analyzes the last branch record, compares control flow with a baseline, and outputs differences in natural language. This tool offers a graphical interface, facilitating the prevention and detection of ROP attacks. The proposed approach and tool provide practical solutions for enhancing software security. © 2023 IEEE.

关键词： Random processes

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Large Graphs with Small Burning Numbers

Large Graphs with Small Burning Numbers

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International Conference on Computational Engineering, ICCE 2023

作者： Rajasingh, Angelin Jemima Parthiban, N. Department of Data Science and Business System School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

In this paper, we have determined the burning number of graphs with universal vertices and graphs that are generated using the graph operation namely ‘join of graphs’. © The Author(s), under exclusive license t... 详细信息

ISBN: (纸本)9783031777639

关键词： Burning number Join of graphs Universal vertex

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A data representation method using distance correlation

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Frontiers of Computer science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO Keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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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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Towards High-Performance Graph Processing: From a Hardware/Software Co-Design Perspective

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Journal of Computer science & Technology 2024年第2期39卷 245-266页

作者：廖小飞赵文举金海姚鹏程黄禹王庆刚赵进郑龙张宇邵志远 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph *** domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software *** article presents a graph processing ecosystem from hardware to *** start by introducing a series of hardware accelerators as the foundation of this ***,the codesigned parallel graph systems and their distributed techniques are presented to support graph ***,we introduce our efforts on novel graph applications and hardware *** results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

关键词： graph processing hardware accelerator software system high performance ecosystem

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A Xgboost Optimized Ensemble Model for Remaining useful Life Prediction of Aircraft Turbofan Engines 6

A Xgboost Optimized Ensemble Model for Remaining useful Life...

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6th IEEE International Conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Sharma, Vaasudev Dagar, Riyansh Sharanya, S. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (纸本)9798350306613

Proactive maintenance for the optimized lifecycle of equipment is now effortless via condition monitoring and fault diagnosis. Recognition and avoidance of a state of failure help to achieve solidity and reliability. Artificial Intelligence and Machine Learning (ML) forecast such situations and aid in the prevention of economic and safety emergencies. This work reviews such ML algorithms and ML models that help diagnose faults in Turbofan engines via measurement of the Remaining Useful Life parameter. This proposed an ensemble machine-learning model optimized by Extreme Gradient Boosting. The approach is then evaluated via different metrics and their performance is measured to current innovative methods. The system is useful for all processes that require high reliability quality and efficiency and reduces unplanned downtime, maintenance costs and asset quality. © 2024 IEEE.

关键词： CMAPSS Ensemble Model Machine Learning Predictive Maintenance Remaining useful life (RUL) Turbofan Engine

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Interactive E-Commerce: Addressing Cart Abandonment through Gamified User Engagement

Interactive E-Commerce: Addressing Cart Abandonment through ...

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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Sohanda, Mayank Kumar Gupta, Mihir Kalpana, A.V. Srm Institute Of Science And Technology School Of Computing Department Of Data Science And Business Systems Kattankulathur India

ISBN: (纸本)9798350352689

This paper addresses the pervasive issue of cart abandonment in online shopping, a phenomenon that significantly impacts e-commerce revenue streams. Through an investigation into the underlying causes, we identify the lack of social engagement as a critical factor contributing to this behavior. Our primary objective is to bridge the gap between item selection and purchase completion by enhancing the user experience and boosting conversion rates. To tackle this challenge, we propose a novel approach by integrating gamification strategies into the online shopping experience. Our methods include implementing real-time user activity indicators, such as visual representations of active shoppers and interactive leaderboards, to foster a sense of community and competition among users. Additionally, we introduce social shopping features, such as cart sharing and real-time feedback systems, to promote user collaboration and interaction. Preliminary data analysis reveals a significant increase in conversion rates following the implementation of these gamification strategies. Users are more inclined to complete purchases within a community-centric and competitive environment. Moreover, the introduction of social elements has enriched the overall shopping experience, as evidenced by user feedback and heightened engagement on the platform. In conclusion, our study demonstrates the effectiveness of integrating gamification elements into online shopping platforms to combat cart abandonment. By creating a more engaging and communal shopping environment, users are not only motivated to complete purchases, but also to enjoy a more fulfilling shopping experience. Our findings highlight the potential of gamification as a powerful tool for enhancing e-commerce platforms and offer valuable insights for future advancements in online retail strategies. © 2024 IEEE.

关键词： Sales

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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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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based Computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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