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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Lopez, Kevin Rezaei, Amin California State University Long Beach Computer Engineering & Computer Science Department Long BeachCA United States

ISBN: (纸本)9783982674100

The outsourcing of semiconductor manufacturing raises security risks, such as piracy and overproduction of hardware intellectual property. To overcome this challenge, logic locking has emerged to lock a given circuit using additional key bits. While single-key logic locking approaches have demonstrated serious vulnerability to a wide range of attacks, multi-key solutions, if carefully designed, can provide a reliable defense against not only oracle-guided logic attacks, but also removal and dataflow attacks. In this paper, using time base keys, we propose, implement and evaluate a family of secure multi-key logic locking algorithms called Cute-Lock that can be applied both in RTL-level behavioral and netlist-level structural representations of sequential circuits. Our extensive experimental results under a diverse range of attacks confirm that, compared to vulnerable state-of-the-art methods, employing the Cute-Lock family drives attacking attempts to a dead end without additional overhead. © 2025 EDAA.

关键词： Keyways

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Recently emerging trends in big data analytic methods for modeling and combating climate change effects

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Energy Informatics 2024年第1期7卷 6页

作者： Ikegwu, Anayo Chukwu Nweke, Henry Friday Mkpojiogu, Emmanuel Anikwe, Chioma Virginia Igwe, Sylvester Agbo Alo, Uzoma Rita Software Engineering Department Veritas University Abuja Nigeria Computer Science Department David Umahi Federal University of Health Sciences Ebonyi State Uburu Nigeria Computer Science Department Alex Ekwueme Federal University Ebonyi Ndufu-Alike Nigeria Computer Science Department Ebonyi State University Ebonyi State Abakaliki Nigeria

Big climate change data have become a pressing issue that organizations face with methods to analyze data generated from various data types. Moreover, storage, processing, and analysis of data generated from climate change activities are becoming very massive, and are challenging for the current algorithms to handle. Therefore, big data analytics methods are designed for significantly large amounts of data required to enhance seasonal change monitoring and understand and ascertain the health risks of climate change. In addition, analysis of climate change data would improve the allocation, and utilisation of natural resources. This paper provides an extensive discussion of big data analytic methods for climate data analysis and investigates how climate change and sustainability issues can be analyzed through these approaches. We further present the big data analytic methods, strengths, and weaknesses, and the essence of analyzing big climate change using these methods. The common datasets, implementation frameworks for climate change modeling, and future research directions were also presented to enhance the clarity of these compelling climate change analysis challenges. This big data analytics method is well-timed to solve the inherent issues of data analysis and easy realization of sustainable development goals. © The Author(s) 2024.

关键词： Data Analytics

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Out-of-Distribution Detection Algorithms for Robust Insect Classification

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Plant Phenomics 2024年第2期6卷 433-444页

作者： Mojdeh Saadati Aditya Balu Shivani Chiranjeevi Talukder Zaki Jubery Asheesh K.Singh Soumik Sarkar Arti Singh Baskar Ganapathysubramanian Department of Computer Science Iowa State UniversityAmesIAUSA Department of Mechanical Engineering Iowa State UniversityAmesIAUSA Department of Agronomy Iowa State UniversityAmesIAUSA

Plants encounter a variety of beneficial and harmful insects during their growth *** identification(i.e.,detecting insects'presence)and classification(i.e.,determining the type or class)of these insect species is critical for implementing prompt and suitable mitigation *** timely actions carry substantial economic and environmental *** learning-based approaches have produced models with good insect classification *** aim to implement identification and classification models in agriculture,facing challenges when input images markedly deviate from the training distribution(e.g.,images like vehicles,humans,or a blurred image or insect class that is not yet trained on).

关键词： distribution robust detection algorithms classification insect

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Feature Collusion Attack on PMU Data-driven Event Classification

Feature Collusion Attack on PMU Data-driven Event Classifica...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Ghasemkhani, Amir Haridas, Rutuja Sanjeev Mohammadabadi, Seyed Mahmoud Sajjadi Yang, Lei California State University Long Beach Department of Computer Engineering and Computer Science Long Beach United States California State University San Bernardino School of Computer Science and Engineering San Bernardino United States University of Nevada Reno Department of Computer Science and Engineering Reno United States

ISBN: (纸本)9798350313604

Event classification is a critical task to ensure the reliability of the power system. Recent developments in event classification methods leverage data-driven techniques with fine-grained Phasor Measurement Units (PMU) data. However, the existing event classification methods are vulnerable to different types of adversarial attacks that can significantly degrade the event classification performance. In this paper, we evaluate the vulnerability of the classification models against feature collision attacks on PMU data. Feature collusion attack leverages a surrogate model to learn the victim's classification model which in turn makes it a plausible attack strategy for both black-box and white-box settings. Specifically, this attack strategy undermines the accuracy of the classification models by crafting poisonous samples that share common features with benign samples which in turn changes the decision boundaries of the classification models. The experimental results on real-world PMU data in a black-box setting show that generating and adding poisonous samples into the model training dataset can significantly degrade the accuracy of current event classification methods. © 2024 IEEE.

关键词： Phasor measurement units

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3-Multi ranked encryption with enhanced security in cloud computing

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Digital Communications and Networks 2023年第2期9卷 313-326页

作者： YeEun Kim Junggab Son Reza M.Parizi Gautam Srivastava Heekuck Oh Major in Bio Artificial Intelligence Dept.of Computer Science and EngineeringHanyang UniversityAnsanRepublic of Korea Department of Computer Science Kennesaw State UniversityMariettaUSA Department of Software Engineering Kennesaw State UniversityMariettaUSA Department of Mathematics and Computer Science Brandon UniversityBrandonCanada Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Dept.of Computer Science and Engineering Hanyang UniversityAnsanRepublic of Korea

Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when *** refer to a model that satisfies all of the conditions a 3-multi ranked search ***,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation *** is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real *** this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security *** proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same ***,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different *** evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.

关键词： Searchable encryption Multi-keyword search Multiple data owners/users Ranked search Enhanced security

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Road Surface Analysis through Machine Learning Techniques

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IEIE Transactions on Smart Processing and Computing 2024年第4期13卷 344-353页

作者： Singh, Prabhat Sharma, Shilpi Kamal, Ahmed E. Kumar, Sunil Department of Computer Science and Engineering Amity School of Engineering & Technology Uttar Pradesh Noida India Department of Electrical and Computer Engineering Iowa State University Ames United States Computer Science and Engineering Amity School of Engineering & Technology Uttar Pradesh Noida India

Roads are an important part of transporting goods and products from one place to another. In developing countries, the main challenge is to maintain road conditions regularly. Roads can deteriorate from time to time. Monitoring the conditions of the roads, which may degrade with time, is very difficult, resulting in a delay in transportation and damage to the vehicles moving on the roads. Poor road conditions cause road accidents. A model is being proposed to monitor the conditions of the road surface by smartphone sensors. Accelerometer, gyroscope, and GPS sensors are deployed in the mobile phones, which will help to collect data on the road conditions. After collecting the data about the road conditions, various machine learning approaches, such as supervised, multi-layered, and multiclass, are applied to data filtration. Road conditions are divided into three categories to achieve this methodology: potholes, deep traverse cracks, and smooth roads. This categorization helped in analyzing the road surface condition through smartphone sensors over all three axes instead of taking it over a single axis. Neural networks helped analyze data or road conditions more accurately than Decision Tree and SVM. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： Decision trees

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High Performance MPI over the Slingshot Interconnect

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Journal of computer science & Technology 2023年第1期38卷 128-145页

作者： Kawthar Shafie Khorassani Chen-Chun Chen Bharath Ramesh Aamir Shafi Hari Subramoni Dhabaleswar K.Panda Department of Computer Science and Engineering The Ohio State UniversityColumbusOH 43210U.S.A.

The Slingshot interconnect designed by HPE/Cray is becoming more relevant in high-performance computing with its deployment on the upcoming exascale *** particular,it is the interconnect empowering the first exascale and highest-ranked supercomputer in the world,*** offers various features such as adaptive routing,congestion control,and isolated *** deployment of newer interconnects sparks interest related to performance,scalability,and any potential bottlenecks as they are critical elements contributing to the scalability across nodes on these *** this paper,we delve into the challenges the Slingshot interconnect poses with current state-of-the-art MPI(message passing interface)*** particular,we look at the scalability performance when using Slingshot across *** present a comprehensive evaluation using various MPI and communication libraries including Cray MPICH,Open-MPI+UCX,RCCL,and MVAPICH2 on CPUs and GPUs on the Spock system,an early access cluster deployed with Slingshot-10,AMD MI100 GPUs and AMD Epyc Rome CPUs to emulate the Frontier *** also evaluate preliminary CPU-based support of MPI libraries on the Slingshot-11 interconnect.

关键词： AMD GPU interconnect technology MPI(message passing interface) Slingshot

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computer vision in smart agriculture and precision farming:Techniques and applications

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Artificial Intelligence in Agriculture 2024年第3期13卷 64-83页

作者： Sumaira Ghazal Arslan Munir Waqar S.Qureshi Department of Computer Science Kansas State University66506 ManhattanKSUSA Department of Electrical Engineering and Computer Science Florida Atlantic University33431 Boca RatonFLUSA School of Computer Science University of GalwayH91 TK33 GalwayIreland

The transformation of age-old farming practices through the integration of digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision and artificial intelligence(AI)*** transformation not only promises increased productivity and economic growth,but also has the potential to address important global issues such as food security and *** survey paper aims to provide a holistic understanding of the integration of vision-based intelligent systems in various aspects of precision *** providing a detailed discussion on key areas of digital life cycle of crops,this survey contributes to a deeper understanding of the complexities associated with the implementation of vision-guided intelligent systems in challenging agricultural *** focus of this survey is to explore widely used imaging and image analysis techniques being utilized for precision farming *** paper first discusses various salient crop metrics used in digital *** this paper illustrates the usage of imaging and computer vision techniques in various phases of digital life cycle of crops in precision agriculture,such as image acquisition,image stitching and photogrammetry,image analysis,decision making,treatment,and *** establishing a thorough understanding of related terms and techniques involved in the implementation of vision-based intelligent systems for precision agriculture,the survey concludes by outlining the challenges associated with implementing generalized computer vision models for real-time deployment of fully autonomous farms.

关键词： Digital agriculture computer vision Smart agriculture Image analysis Vision-guided intelligent systems

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A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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computer Modeling in engineering & sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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Enhancing K-user Interference Alignment for Discrete Constellations via Learning

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IEEE Journal on Selected Areas in Communications 2025年第7期43卷 2405-2416页

作者： Mishra, Rajesh Jafar, Syed Vishwanath, Sriram Kim, Hyeji Samsung Research America Inc United States University of California at Irvine Department of Electrical Engineering and Computer Science United States University of Texas at Austin Department of Electrical and Computer Engineering AustinTX United States

In this paper, we consider a K-user interference channel where interference among the users is neither too strong nor too weak, a scenario that is relatively underexplored in the literature. We propose a novel deep learning-based approach to design the encoder and decoder functions that aim to maximize the sumrate of the interference channel for discrete constellations. We first consider the MaxSINR algorithm, a state-of-the-art linear scheme for Gaussian inputs, as the baseline and then propose a modified version of the algorithm for discrete inputs. We then propose a neural network-based approach that learns a non-linear constellation mapping with the objective of maximizing the sumrate. We provide numerical results to show that the constellations learned by the neural network-based approach provide enhanced alignments, not just in beamforming directions but also in terms of the effective constellation at the receiver, thereby leading to improved sum-rate performance. © 1983-2012 IEEE.

关键词： Multiple access interference

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