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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Alqaaf, Muhammad Technology, Ahmad Kamal Nasution Graduate School Of science And Supriyanti, Retno Asaduzzaman, Abu Ono, Naoaki Altaf-Ul-Amin, Md. Kanaya, Shigehiko Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan Jenderal Soedirman University Department of Electrical Engineering Banyumas Indonesia Wichita State University Department of Electrical and Computer Engineering Wichita United States

ISBN: (纸本)9798350391213

Breast cancer (BC) is a complex disease with multiple histological subtypes that exhibits distinct biological and clinical characteristics. Accurate identification of these subtypes is crucial for the implementation of personalized treatment strategies and the improvement of patient outcomes. This study aimed to leverage the potential of neural networks and mammography data to identify specific histological subtypes of BC, with a focus on distinguishing between in situ and invasive carcinoma. We used the Digital Database for Screening Mammography (DDSM) and its curated subset, CBIS-DDSM, which provides mammography images of normal and malignant cases with verified pathological information. A convolutional neural network (CNN) architecture was designed to extract relevant features from mammography images. The model was trained using data augmentation techniques to enhance diversity and mitigate overfitting, resulting in training and validation accuracies ranging from 0.98 to 1.0. The features extracted by the trained model were then used for clustering analysis using Kmeans on PCA (KM-PCA), which identified nine well-separated clusters with minimal overlap. The clustering results were validated using a subset of mammography images with confirmed invasive carcinoma types from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Two validation approaches were incorporated to strengthen the hypothesis and further validate the results. Overall, this study demonstrates the potential of combining neural networks and mammography data to accurately identify BC histological subtypes, which could pave the way for personalized and effective treatment strategies. © 2024 IEEE.

关键词： Lung cancer

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Poster: ReMouse Dataset: Measuring Similarity of Human-Generated Trajectories as an Important Step in Dealing with Session-Replay Bots 22

Poster: ReMouse Dataset: Measuring Similarity of Human-Gener...

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28th ACM SIGSAC Conference on computer and Communications Security, CCS 2022

作者： Sadeghpour, Shadi Vlajic, Natalija Electrical Engineering and Computer Science York University TorontoON Canada

ISBN: (纸本)9781450394505

Session-replay bots are believed to be the latest and most advanced generation of web-bots, that are also difficult challenging to defend against. Combating session-replay bots is particularly problematic in online domains that get repeatedly visited by the same genuine human user(s), and possibly in the same/similar way-such as news, banking or gaming sites. Namely, in such domains, it is difficult to determine whether two look-alike sessions are produced by the same human user or these sessions are just bot-generated session replays. In this paper we introduce and provide to the public a novel real-world mouse dynamics dataset named ReMouse. ReMouse dataset is collected in a guided environment and, unlike other publicly available mouse dynamics dataset, it contains repeat-sessions generated by the same human user(s). As such, ReMouse dataset is first of its kind and is of particular relevance for studies on the development of effective defenses against session-replay bots. Our own statistical analysis of ReMouse dataset shows that not only two different human users are highly unlikely to generate same/similar looking sessions when performing the same/similar online task, but even the (repeat) sessions generated by the same human user are likely to be sufficiently distinguishable from one another. © 2022 Owner/Author.

关键词： Dynamics

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Optimal Receive Filter Design for Misaligned Over-the-Air Computation

Optimal Receive Filter Design for Misaligned Over-the-Air Co...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Hellström, Henrik Razavikia, Saeed Fodor, Viktoria Fischione, Carlo School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Stockholm Sweden Stanford University Electrical Engineering Department CA United States

ISBN: (纸本)9798350370218

Over-the-air computation (AirComp) is a promising wireless communication method for aggregating data from many devices in dense wireless networks. The fundamental idea of AirComp is to exploit signal superposition to compute functions of multiple simultaneously transmitted signals. However, the time-and phase-alignment of these superimposed signals have a significant effect on the quality of function computation. In this study, we analyze the AirComp problem for a system with unknown random time delays and phase shifts. We show that the classical matched filter does not produce optimal results, and generates bias in the function estimates. To counteract this, we propose a new filter design and show that, under a bound on the maximum time delay, it is possible to achieve unbiased function computation. Additionally, we propose a Tikhonov regularization problem that produces an optimal filter given a tradeoff between the bias and noise-induced variance of the function estimates. When the time delays are long compared to the length of the transmitted pulses, our filter vastly outperforms the matched filter both in terms of bias and mean-squared error (MSE). For shorter time delays, our proposal yields similar MSE as the matched filter, while reducing the bias. © 2023 IEEE.

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Enhancing Track Management Systems with Vehicle-To-Vehicle Enabled Sensor Fusion 100

Enhancing Track Management Systems with Vehicle-To-Vehicle E...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Billington, Thomas Gwash, Ansh Kothari, Aadi Izquierdo, Lucas Talty, Timothy Virginia Polytechnic Institute and State University Department of Computer Science BlacksburgVA United States Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering BlacksburgVA United States

ISBN: (纸本)9798331517786

In the rapidly advancing landscape of connected and automated vehicles (CAV), the integration of Vehicle-to-Everything (V2X) communication in traditional fusion systems presents a promising avenue for enhancing vehicle perception. Addressing current limitations with vehicle sensing, this paper proposes a novel Vehicle-to-Vehicle (V2V) enabled track management system that leverages the synergy between V2V signals and detections from radar and camera sensors. The core innovation lies in the creation of independent priority track lists, consisting of fused detections validated through V2V communication. This approach enables more flexible and resilient thresholds for track management, particularly in scenarios with numerous occlusions where the tracked objects move outside the field of view of the perception sensors. The proposed system considers the implications of falsification of V2X signals which is combated through an initial vehicle identification process using detection from perception sensors. Presented are the fusion algorithm, simulated environments, and validation mechanisms. Experimental results demonstrate the improved accuracy and robustness of the proposed system in common driving scenarios, highlighting its potential to advance the reliability and efficiency of autonomous vehicles. © 2024 IEEE.

关键词： Magnetic levitation vehicles

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Implications of Solution Patterns on Adversarial Robustness

Implications of Solution Patterns on Adversarial Robustness

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liang, Hengyue Liang, Buyun Sun, Ju Cui, Ying Mitchell, Tim University of Minnesota Department of Electrical & Computer Engineering United States University of Minnesota Department of Computer Science & Engineering United States University of Minnesota Department of Industrial & Systems Engineering United States City University of New York Queens College Department of Computer Science United States

ISBN: (纸本)9798350302493

Empirical robustness evaluation (RE) of deep learning models against adversarial perturbations involves solving non-trivial constrained optimization problems. Recent work has shown that these RE problems can be reliably solved by a general-purpose constrained-optimization solver, PyGRANSO with Constraint-Folding (PWCF). In this paper, we take advantage of PWCF and other existing numerical RE algorithms to explore distinct solution patterns in solving RE problems with various combinations of losses, perturbation models, and optimization algorithms. We then provide extensive discussions on the implications of these patterns on current robustness evaluation and adversarial training. A comprehensive version of this work can be found in [19]. © 2023 IEEE.

关键词： Constrained optimization

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CMOS-Based Ripple Correlation Control MPPT DC-DC Boost Converter for Perovskite Solar CellEnergy Harvester 4

CMOS-Based Ripple Correlation Control MPPT DC-DC Boost Conve...

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4th IEEE International Conference in Power engineering Applications, ICPEA 2024

作者： Adebunmi, Kayode Oluwaseyi Marzuki, Arjuna Ng, Annie Ukaegbu, Ikechi Augustine Nazarbayev University School of Engineering and Digital Sciences Department of Electrical and Computer Engineering Astana Kazakhstan Wawasan Open University School of Science and Technology Penang Malaysia

ISBN: (纸本)9798350344578

The power management integrated circuits in photovoltaic (PV) energy harvesting applications require efficient maximum power point tracking (MPPT) algorithm to maximize power output from the PV in the face of continuously fluctuating atmospheric circumstances. A CMOS-based Ripple Correlation Control (RCC) algorithm for optimizing power extraction from the Perovskite Solar cell (PSC) was designed in this work. The RCC uses the intrinsic ripple characteristics of the DC-DC boost converter to perturb the voltage and current of the PV panel to achieve MPP. The design uses a multiplier, a 2-stage op-amp, two differentiator circuits, an integrator, an XOR gate, and a deadtime circuit. The PWM stage of the MPPT consists of a comparator, and a Schmitt trigger circuit was incorporated into the PWM section of the design to minimize the system's chip size and power consumption. The effectiveness of the RCC output signal was able to drive the boost converter to produces an output voltage of 5.67V from an input voltage of 1.5V with a power efficiency of 98%. The system was designed using UMC180nm CMOS technology. © 2024 IEEE.

关键词： Timing circuits

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V2I-Aided zk-SNARK for Travel Records Verification of Electric Vehicles 7

V2I-Aided zk-SNARK for Travel Records Verification of Electr...

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7th IEEE International Symposium on Telecommunication Technologies, ISTT 2024

作者： Ding, Cao Ho, Ivan Wang-Hei Chau, Sid Chi-Kin The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Hong Kong College of Engineering and Computer Science The Australian National University Australia

ISBN: (纸本)9798350392074

The rapid increase in the number of electric vehicles (EVs) has resulted in huge fuel tax losses for governments every year. Many countries have levied taxes based on the annual or monthly travel record (TR) submitted by the EV. On the one hand, TR contains important private information, such as the time, locations, and trajectories of EV owners. On the other hand, EV owners may forge TR to reduce taxes. Therefore, the verification protocol of TR requires extremely high security and effectiveness. To solve this outstanding issue, this paper proposes a V2I-SNARK protocol that combines vehicle-to-infrastructure communications (V2I) and zk-SNARK for TR verification of EVs. V2I-SNARK is divided into two stages, the trusted setup stage and the TR verification stage. In the former stage, a trusted authority (TA) will generate the proof key and verification key for verification and store them on the verification server (Verifier). In the latter stage, EV will use the proof key to generate a randomized proof, and the verifier will use the verification key to verify the proof. Regarding the performance of the V2I-SNARK protocol, we first provide security proofs for completeness, soundness, and zero-knowledge properties. Furthermore, we compare the verification efficiency, energy consumption, computational complexity, and other performance of V2I-SNARK with the benchmark protocols. The results show that the proposed V2I-SNARK protocol outperforms other protocols in terms of verification efficiency and energy consumption. © 2024 IEEE.

关键词： Benchmarking

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computer Vision based Panoptic Driving Perception under Various Weather Conditions

Computer Vision based Panoptic Driving Perception under Vari...

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2023 International Conference on Industry sciences and computer science Innovation, iSCSi 2023

作者： Sridevi, M. Harish, M. Department of Computer Science & Engineering National Institute of Technology Tiruchirappalli620015 India Department of Electrical and Electronics Engineering National Institute of Technology Tiruchirappalli620015 India

Learning models that have been known to multi-task are gaining too much attention in various fields. One such area of work where Multi-tasking models are extracting hopeful outcomes is Autonomous Driving. Panoptic Driving Perception has become an inevitable aspect of autonomous driving systems which demands a very good accuracy and speed. Over a decade, various multi-tasking models have been proposed to simultaneously carry out traffic object detection, drivable area segmentation, and lane detection. Among all of them, YOLOP, YOLOPv, and HybridNets are the 3 models that have accomplished the above tasks using the BDD100K Dataset with high excellent accuracy and efficiency, thus achieving the new State-Of-The-Art (SOTA) performance. This paper focuses mainly on comparing the performance of the above-mentioned models in various weather conditions based on accuracy, robustness, and computational speed. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Object detection

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An upgraded variant of backtracking search algorithm and its application to COVID-19 CT images segmentation

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Multimedia Tools and Applications 2025年 1-57页

作者： Debnath, Sanjib Nama, Sukanta Chakraborty, Sanjoy Saha, Apu Kumar Mirjalili, Seyedali Department of Computer Science & Engineering National Institute of Technology Agartala Tripura Agartala799046 India Department of Science & Humanities Gomati District Polytechnic Fulkumari Tripura Udaipur799013 India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Center for Artificial Intelligence Research and Optimization Torrens University Australia Brisbane Australia Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Ostrava Czech Republic University Research and Innovation Center Obuda University Budapest Hungary Department of Computer Science & Engineering The ICFAI University Tripura Tripura 799210 India

To improve the performance of the original backtracking search algorithm (BSA), this work combines BSA with a centralized population initialization and an elitism-based local escape operator and proposes a new, improved variant of BSA called CiBSA. BSA's performance is improved by centralized population initialization regarding search capability, convergence speed, and capacity to jump out of the local optimum. The suggested algorithm's elitism-based local escape operator method prevents BSA searching slowness and enhances convergence rates and local search efficiency. The performance of the proposed algorithm has been tested on a set of 54 test suites. An extensive comparison with other recent meta-heuristics algorithms (MA) demonstrates that both methodologies substantially assist BSA in boosting the quality of its solutions and speeding up the convergence rate. The proposed CiBSA is then applied to a real-world problem: image segmentation using Kapur's entropy function. The proposed CiBSA is successfully applied to the segmentation of COVID-19 CT images using Kapur's entropy for grey-scale images. Considering the performance matrices Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Intersection over Union (IoU), the suggested CiBSA outperformed conventional MA algorithms. In COVID-19 CT image segmentation trials, CiBSA has superior segmentation capability and adaptability at varied threshold levels than other approaches. As a result, CiBSA has a lot of potential for enhancing COVID-19 diagnosis accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

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Review of Power Grid Optimization Scheduling Methods Based On Deep Reinforcement Learning 7

Review of Power Grid Optimization Scheduling Methods Based O...

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7th IEEE International electrical and Energy Conference, CIEEC 2024

作者： Zherui, Yang Zhen, Zhang Fangzhou, Li Nan, Dai Yiying, Yan Ke, Wang Dayu College Hohai University China College of Energy and Electrical Engineering Hohai University China College of Computer Science and Software Engineering Hohai University China

ISBN: (纸本)9798350359558

In recent years, with the introduction of energy conservation and emission reduction targets, the introduction of renewable energy has become an important initiative. However, due to the many factors that affect the new energy units, the traditional scheduling method becomes inappropriate. If a more intelligent scheduling method is not developed, the lack of flexible energy scheduling capacity will further lead to problems such as insufficient wind power consumption level, inefficient power grid decision-making, high operating costs, and unstable energy supply. In order to solve the above problems and improve the environmental protection, efficiency, economy and safety of the power grid, this paper summarizes the application of artificial intelligence in the field of power grid scheduling, analyzes SAC, A3C and TD3 algorithms in detail, analyzes their mechanism through the algorithm structure, and summarizes the advantages and disadvantages of each algorithm and its application scenarios. In addition, this paper also analyzes the application of artificial intelligence in unit combination, economic scheduling optimization and optimal power flow optimization. Different from other review papers, this paper deeply analyzes several problems that may arise in the application of the algorithm, and specifically analyzes the future optimization direction and corresponding optimization methods, so as to improve the level of experience combination, fault handling, comprehensive consideration, interpretability and other aspects of the future agent. © 2024 IEEE.

关键词： Reinforcement learning

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