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20th IEEE International Conference on Networking, Sensing and Control, ICNSC 2023

作者： Shen, Chen Liu, Gaiyun Zhong, Chunfu Barkaoui, Kamel Xidian University School of Electro-Mechanical Engineering Xi'an China Conservatoire National des Arts et Métiers Cedric Lab and Computer Science Department Paris France

ISBN: (纸本)9798350369502

The effectiveness and stealthiness analysis of sensor attacks, utilizing the reachability graph of labeled Petri nets (LPNs), typically encounters the challenge of the state explosion problem. To address this issue, this work provides a simplified LPN method via simplified structure. Attack structure is derived from the concept of concurrent composition. The attack structure based on the simplified structure subnet describes all possible attacks, and by removing the state with the risk of exposure, the supremal stealthy attack substructure can be derived. If the supremal stealthy attack substructure contains a critical marking, the attack is stealthy. By using simplification rules, the state count in the attack structure can be effectively reduced while still guaranteeing the attack's effectiveness and stealthiness. © 2023 IEEE.

关键词： Petri nets

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Temporal Gradient Inversion Attacks with Robust Optimization

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Li, Bowen Gu, Hanlin Chen, Ruoxin Li, Jie Wu, Chentao Ruan, Na Si, Xueming Fan, Lixin Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China WeBank WeBank AI Lab China

Federated Learning (FL) has emerged as a promising approach for collaborative model training without sharing private data. However, privacy concerns regarding information exchanged during FL have received significant research attention. Gradient Inversion Attacks (GIAs) have been proposed to reconstruct the private data retained by local clients from the exchanged gradients. While recovering private data, the data dimensions and the model complexity increase, which thwart data reconstruction by GIAs. Existing methods adopt prior knowledge about private data to overcome those challenges. In this paper, we first observe that GIAs with gradients from a single iteration fail to reconstruct private data due to insufficient dimensions of leaked gradients, complex model architectures, and invalid gradient information. We investigate a Temporal Gradient Inversion Attack with a Robust Optimization framework, called TGIAs-RO, which recovers private data without any prior knowledge by leveraging multiple temporal gradients. To eliminate the negative impacts of outliers, e.g., invalid gradients for collaborative optimization, robust statistics are proposed. Theoretical guarantees on the recovery performance and robustness of TGIAs-RO against invalid gradients are also provided. Extensive empirical results on MNIST, CIFAR10, ImageNet and Reuters 21578 datasets show that the proposed TGIAs-RO with 10 temporal gradients improves reconstruction performance compared to state-of-the-art methods, even for large batch sizes (up to 128), complex models like ResNet18, and large datasets like ImageNet (224×224 pixels). Furthermore, the proposed attack method inspires further exploration of privacy-preserving methods in the context of FL. © 2004-2012 IEEE.

关键词： Differential privacy

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R-AT: Regularized Adversarial Training for Natural Language Understanding

R-AT: Regularized Adversarial Training for Natural Language ...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Ni, Shiwen Li, Jiawen Kao, Hung-Yu Intelligent Knowledge Management Lab Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

Currently, adversarial training has become a popular and powerful regularization method in the natural language domain. In this paper, we Regularized Adversarial Training (R-AT) via dropout, which forces the output probability distributions of different sub-models generated by dropout to be consistent under the same adversarial samples. Specifically, we generate adversarial samples by perturbing the word embeddings. For each adversarial sample fed to the model, R-AT minimizes both the adversarial risk and the bidirectional KL-divergence between the adversarial output distributions of two sub-models sampled by dropout. Through extensive experiments on 13 public natural language understanding datasets, we found that RAT has improvements for many models (e.g., rnn-based, cnn-based, and transformer-based models). For the GLUE benchmark, when RAT is only applied to the fine-tuning stage, it is able to improve the overall test score of the BERT-base model from 78.3 to 79.6 and the RoBERTa-large model from 88.1 to 88.6. Theoretical analysis reveals that R-AT has potential gradient regularization during the training process. Furthermore, R-AT can reduce the inconsistency between training and testing of models with dropout. © 2022 Association for Computational Linguistics.

关键词： Rats

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A Composite Retinal Fundus and OCT Dataset to Grade Macular and Glaucomatous Disorders 2

A Composite Retinal Fundus and OCT Dataset to Grade Macular ...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Hassan, Taimur Raja, Hina Hassan, Bilal Akram, Muhammad Usman Dias, Jorge Werghi, Naoufel Khalifa University C2PS and Kucars Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates College of EME Nust Biomisa Lab Department of Computer and Software Engineering Rawalpindi Pakistan Khalifa University Kucars Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665498197

Retinopathy represents a group of retinal diseases that causes severe visual impairments and even blindness. Many researchers have publicly released datasets containing fundus or optical coherence tomography (OCT) scans to screen retinal diseases like macular edema (ME) and age-related macular degeneration (AMD). These datasets also contain clinical markings to analyze the retinal layers and retinal lesions within normal and abnormal pathologies. However, to the best of our knowledge, no dataset provides the clinically graded fundus and OCT images reflecting the geographic AMD, neovascular AMD, acute central serous retinopathy (CSR), chronic CSR, centrally involved DME (ci-DME), and glaucomatous pathologies. Furthermore, the majority of the publicly available OCT datasets are acquired through Spectralis Machines, which limits the thorough evaluation of autonomous frameworks to screen retinal pathologies irrespective of the scanner specifications. To overcome these challenges, we present a novel dataset containing composite fundus and OCT scans of each patient, along with detailed annotations for extracting the retinal layers and retinal lesions. Also, contrary to its competitors, the proposed dataset is acquired through Topcon 3D OCT 2000 machine that can be utilized for training (or evaluating) any autonomous frameworks to give the lesion-aware screening and severity grading of the above-mentioned retinal diseases as per the clinical standards. Moreover, in this paper, we are also releasing the retinal annotation software alongside the proposed dataset. This software can help clinicians in quickly marking both fundus and OCT scans, which can be saved later on in any image format. Overall, the proposed dataset contains 9,268 OCT scans and 180 fundus scans from 105 subjects depicting healthy, ci-DME, geographic AMD, neovascular AMD, acute CSR, chronic CSR, and glaucomic pathologies. © 2022 IEEE.

关键词： Optical tomography

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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A Dynamic Resource-Aware Routing Protocol in Resource-Constrained Opportunistic Networks

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computers, Materials & Continua 2022年第2期70卷 4147-4167页

作者： Aref Hassan Kurd Ali Halikul Lenando Slim Chaoui Mohamad Alrfaay Medhat A.Tawfeek Department of Computer Systems and Communication Technologies Faculty of Computer Science and Information TechnologyUniversiti Malaysia Sarawak(UNIMAS)Kota Samarahan94300Malaysia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka2014KSA Unit-Lab of Sciences of Electronics Technologies of Information and TelecommunicationsSfax UniversitySfaxTunisia Department of Basic Sciences First Common Year DeanshipJouf UniversitySakaka2014KSA Department of Computer Sciences College of Computer and Information SciencesJouf UniversitySakaka2014KSA Department of Computer Sciences Faculty of Computers and InformationMenoufia UniversityEgypt

Recently,Opportunistic Networks(OppNets)are considered to be one of the most attractive developments of Mobile Ad Hoc Networks that have arisen thanks to the development of intelligent *** are characterized by a rough and dynamic topology as well as unpredictable contacts and contact *** is forwarded and stored in intermediate nodes until the next opportunity ***,achieving a high delivery ratio in OppNets is a challenging *** is imperative that any routing protocol use network resources,as far as they are available,in order to achieve higher network *** this article,we introduce the Resource-Aware Routing(ReAR)protocol which dynamically controls the buffer usage with the aim of balancing the load in resource-constrained,stateless and non-social *** ReAR protocol invokes our recently introduced mutual informationbased weighting approach to estimate the impact of the buffer size on the network performance and ultimately to regulate the buffer consumption in real *** proposed routing protocol is proofed conceptually and simulated using the Opportunistic Network Environment *** show that the ReAR protocol outperforms a set of well-known routing protocols such as EBR,Epidemic MaxProp,energy-aware Spray and Wait and energy-aware PRoPHETin terms of message delivery ratio and overhead ratio.

关键词： Opportunistic networks mobile ad hoc networks routing protocols resource-constrained networks load balancing buffer management

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User-Centric Crowdsourcing Approach to Improve Urban Accessibility Data Collection

User-Centric Crowdsourcing Approach to Improve Urban Accessi...

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2024 IEEE MIT Undergraduate Research Technology Conference, URTC 2024

作者： Ortiz, Tyler Tang, Vicky Sutton, Karla The City College of New York Cuny New York Grove School of Engineering Computer Science New York United States School of Constructed Environments Parsons School of Design New York New York United States Lab School for Collaborative Studies New York Department of Computer Science New York United States

ISBN: (纸本)9798331531003

While crowdsourcing is an effective method for collecting large-scale datasets, it often faces challenges related to data quality and quantity. This study employs crowdsourcing to collect comprehensive urban accessibility data, focusing on storefront accessibility information in a large city. This task presents significant challenges in terms of data completeness and quality. To address these issues, we propose a user-centric, request-driven crowdsourcing approach that gathers accessibility data based on requests from individuals who are blind or have low vision (BLV). This approach strengthens the user-volunteer feedback loop, increasing volunteer engagement and enhancing user experience. It allows BLV individuals to effectively utilize the collected storefront accessibility data for independent travel. A preliminary user study confirms the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Crowdsourcing

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Temporal Consistency for LLM Reasoning Process Error Identification

arXiv

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arXiv 2025年

作者： Guo, Jiacheng Wu, Yue Qiu, Jiahao Huang, Kaixuan Juan, Xinzhe Yang, Ling Wang, Mengdi Department of Electrical & Computer Engineering Princeton University United States AI Lab Princeton University United States Department of Computer Science & Engineering University of Michigan United States

Verification is crucial for effective mathematical reasoning. We present a new temporal consistency method where verifiers iteratively refine their judgments based on the previous assessment. Unlike one-round verification or multi-model debate approaches, our method leverages consistency in a sequence of self-reflection actions to improve verification accuracy. Empirical evaluations across diverse mathematical process error identification benchmarks (Mathcheck, ProcessBench, and PRM800K) show consistent performance improvements over baseline methods. When applied to the recent DeepSeek R1 distilled models, our method demonstrates strong performance, enabling 7B/8B distilled models to outperform all 70B/72B models and GPT-4o on ProcessBench. Notably, the distilled 14B model with our method achieves performance comparable to Deepseek-R1. Our codes are available at https://***/jcguo123/Temporal-Consistency © 2025, CC BY.

关键词： Iterative methods

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Analog Magnetic Sensor-Robotic System for Steel Structure Inspection

Analog Magnetic Sensor-Robotic System for Steel Structure In...

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Pham, Anh Quyen La, Duc La, Hung Manh AIR-VIET Corporation Viet Nam Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States Lab Department of Computer Science and Engineering RenoNV89557 United States

ISBN: (纸本)9798350312072

More and more steel bridge collapse accidents occur worldwide due to broken steel structures, causing significant l oss o f l ife a nd p roperty t o m ankind. T his has spurred much research into robots that can climb steel surfaces and carry smart sensors with the desire to assist inspectors in inspecting steel defects. However, current non-destructive evaluation (NDE) sensors such as eddy-current and giant magnetoresistance (GMR) are not able to be integrated with the robotic platforms due to their large size, mass, and limited ability to interface. More importantly, these NDE sensors may fail to detect hidden and underlying cracks. Therefore, in this paper, we present a novel and compact analog magnetic sensor system to detect different types of cracks in steel. Validations are carried out on a steel test plate 600mm long, 140mm wide, and 6mm thick, and precision machined by CNCs to produce various man-made cracks including penetrating cracks, surface cracks, internal/hidden cracks, and underlying cracks. These cracks have a width varying from 0.6mm to 1.2mm with different depth levels. Combined with applying the Kalman filter t o t he n oise r eduction s ystem, t he r esults o btained are accurate, and the response speed is fast. The sensor is small enough and has firmware/software with an interface to make it possible to be integrated with a small drone or a climbing robot to perform an in-depth inspection of the steel bridge. A demonstration of the system can be seen in this video: https://***/OrdpLdnFlYk © 2024 IEEE.

关键词： Eddy current testing

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PowerGen: Resources Utilization and Power Consumption Data Generation Framework for Energy Prediction in Edge and Cloud Computing 15

PowerGen: Resources Utilization and Power Consumption Data G...

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15th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Ismail, Leila Materwala, Huned Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios. © 2024 Elsevier B.V.. All rights reserved.

关键词： Internet of things

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