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arXiv 2024年

作者： Wen, Qi Zhang, Yiyang Ye, Yutong Zhou, Yingbo Zhang, Nan Lian, Xiang Chen, Mingsong MoE Engineering Research Center on HW/SW Co-design and Application East China Normal University Shanghai China Department of Computer Science Kent State University KentOH44242 United States

Due to the power of learning representations from unlabeled graphs, graph contrastive learning (GCL) has shown excellent performance in community detection tasks. Existing GCL-based methods on the community detection usually focused on learning attribute representations of individual nodes, which, however, ignores structural semantics of communities (e.g., nodes in the same community should be structurally cohesive). Therefore, in this paper, we consider the community detection under the community structure semantics and propose an effective framework for graph contrastive learning under structure semantics (GCLS2) to detect communities. To seamlessly integrate interior dense and exterior sparse characteristics of communities with our contrastive learning strategy, we employ classic community structures to extract high-level structural views and design a structure semantic expression module to augment the original structural feature representation. Moreover, we formulate the structure contrastive loss to optimize the feature representation of nodes, which can better capture the topology of communities. To adapt to large-scale networks, we design a high-level graph partitioning (HGP) algorithm that minimizes the community detection loss for GCLS2 online training. It is worth noting that we prove a lower bound on the training of GCLS2 from the perspective of the information theory, explaining why GCLS2 can learn a more accurate representation of the structure. Extensive experiments have been conducted on various real-world graph datasets and confirmed that GCLS2 outperforms nine state-of-the-art methods, in terms of the accuracy, modularity, and efficiency of detecting communities. © 2024, CC BY.

关键词： Contrastive Learning

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IBB Traffic Graph Data: Benchmarking and Road Traffic Prediction Model

IBB Traffic Graph Data: Benchmarking and Road Traffic Predic...

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IEEE International Workshop on computer-Aided Modeling, Analysis, and design of Communication Links and Networks, CAMAD

作者： Eren Olug Kiymet Kaya Resul Tugay Sule Gunduz Oguducu Department of Artificial Intelligence and Data Engineering Istanbul Technical University Istanbul Turkey ITU AI Research and Application Center Istanbul Turkey BTS Group Istanbul Turkey Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Computer Engineering Gazi University Ankara Turkey

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

Road traffic congestion prediction is a crucial component of intelligent transportation systems, since it enables proactive traffic management, enhances suburban experience, reduces environmental impact, and improves overall safety and efficiency. Although there are several public datasets, especially for metropolitan areas, these datasets may not be applicable to practical scenarios due to insufficiency in the scale of data (i.e. number of sensors and road links) and several external factors like different characteristics of the target area such as urban, highways and the data collection location. To address this, this paper introduces a novel IBB Traffic graph dataset as an alternative benchmark dataset to mitigate these limitations and enrich the literature with new geographical characteristics. IBB Traffic graph dataset covers the sensor data collected at 2451 distinct locations. Moreover, we propose a novel Road Traffic Prediction Model that strengthens temporal links through feature engineering, node embedding with GLEE to represent interrelated relationships within the traffic network, and traffic prediction with ExtraTrees. The results indicate that the proposed model consistently outperforms the baseline models, demonstrating an average accuracy improvement of 4%.

关键词： Representation learning Computational modeling Roads Urban areas Predictive models Sensor phenomena and characterization Benchmark testing Road traffic Data models Safety

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IBB Traffic Graph Data: Benchmarking and Road Traffic Prediction Model

arXiv

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arXiv 2024年

作者： Olug, Eren Kaya, Kiymet Tugay, Resul Oguducu, Sule Gunduz Istanbul Technical University Department of Artificial Intelligence and Data Engineering Istanbul Turkey Istanbul Technical University Department of Computer Engineering Istanbul Turkey ITU AI Research and Application Center Istanbul Turkey BTS Group Istanbul Turkey Gazi University Department of Computer Engineering Ankara Turkey

关键词： Graph embeddings

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Enhancement of CdS resistance to photocorrosion and photocatalytic removal of uranyl by complexation with N-deficient g-C3N4under aerobic conditions

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Chemosphere 2023年第1期335卷

作者： Zhang, Yu Chen, Qixu Xiao, Qianxiang Shi, Lang Zhao, Zheng Wang, Hongqing Hunan Key Laboratory for the Design and Application of Actinide Complexes School of Chemistry and Chemical Engineering University of South China Hunan Hengyang421001 China Engineering Research Center for Rare Earth GRINM Group Corporation Limited Beijing100088 China

The effect of oxygen on the reduction of uranyl and photocorrosion of CdS remains a pressing issue when CdS is used as a photocatalyst for the removal of uranyl in uranium-containing wastewater. In this study, composites (CdS/PCN) were prepared by designing N-deficient g-C3N4 composite with CdS for efficient photocatalytic reduction of uranyl under aerobic condition. Meanwhile, a series of characterizations of the CdS/PCN composites were carried out by XRD, FT-IR, XPS, EDS and UV–vis. Surprisingly, the CdS/PCN not only showed very high photocatalytic reduction activity for uranyl under aerobic condition, but also the photocorrosion of CdS by oxygen and h+ was inhibited. With a starting uranium (VI) concentration of 20 ppm, the uranium (VI) removal efficiency could reach 97.33% (dark: 30 min, light: 10 min). Interestingly, the removal efficiency was better in air condition than in pure nitrogen or 30% oxygen atmosphere, i.e. a proper amount of oxygen has accelerated the reduction reaction, while excess oxygen weakened the reduction. Finally, a new mechanism of reduction of uranyl by CdS/PCN photocatalyst was given under aerobic condit ions. This work presents a novel strategy for reduction of U(VI) by photocatalysis and the inhibition of photocorrosion of photocatalysts under aerobic conditions. © 2023 Elsevier Ltd

关键词： Cadmium sulfide

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Prediction of Smoking Addiction Among Youths Using Elastic Net and KNN: A Machine Learning Approach 9th

Prediction of Smoking Addiction Among Youths Using Elastic N...

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2021

作者： Pratik, Shreerudra Nayak, Debasish Swapnesh Kumar Prasath, Rajendra Swarnkar, Tripti Department of Mathematics Utkal University Bhubaneswar751004 India Department of Computer Science and Engineering Institute of Technical Education and Research Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar751030 India Computer Science and Engineering Group Indian Institute of Information Technology Sri City Chittoor517646 India Department of Computer Application Institute of Technical Education and Research Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar751030 India

ISBN: (纸本)9783031215162

In the current generation, it has been studied that smoking addiction among the youths is increased exponentially. Since there is a lot of awareness among the people about tobacco use but the youths are exposed to a broader spectrum of the different types of nicotine products like E-cigarettes, or hookah or water pipes, or conventional cigarettes, or dissolvable tobacco, and many more products. Our study aims are to identify some of the demographic factors like age, gender, sex, etc. to predict smoking addiction among the youths of our society. To predict e-cigarette addiction among youths, we considered the wings of Artificial Intelligence (AI) like Machine Learning (ML), and Deep Learning (DL) for better understanding and finding the relationship among the various features. During the data preprocessing we used the elastic net regression technique for feature selection and K-Nearest neighbor for making the accurate predictions. The Hybrid Prediction model was built by the Elastic net regression technique and KNN. It is observed that Elastic net finds a better selection of significant features. The outcome of the suggested pipeline provides high performance on the selection of significant features for the prediction model and provides better accuracy. © 2022, Springer Nature Switzerland AG.

关键词： Tobacco

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A Cascaded design of Best Features Selection for Fruit Diseases Recognition

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computers, Materials & Continua 2022年第1期70卷 1491-1507页

作者： Faiz Ali Shah Muhammad Attique Khan Muhammad Sharif Usman Tariq Aimal Khan Seifedine Kadry Orawit Thinnukool Department of Computer Science COMSATS University IslamabadWah Campus47080Pakistan Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia National University of Science and Technology Islamabad IslamabadPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s *** manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an ***,it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf *** to the literature,many automated methods have been developed for the recognition of fruit diseases at the early ***,these techniques still face some challenges,such as the similar symptoms of different fruit diseases and the selection of irrelevant *** processing and deep learning techniques have been extremely successful in the last decade,but there is still room for improvement due to these ***,we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation(ACO)based *** proposed method consists of four fundamental steps:data augmentation to solve the imbalanced dataset,fine-tuned pretrained deep learning models(NasNetMobile andMobileNet-V2),the fusion of extracted deep features using matrix length,and finally,a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis(NCA).The best-selected features were eventually passed to many classifiers for final *** experimental process involved an augmented dataset and achieved an average accuracy of 99.7%.Comparison with existing techniques showed that the proposed method was effective.

关键词： Fruits diseases data augmentation deep learning features fusion feature selection

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Bounded-DWA: An Efficient Local Planner for Ackermann-driven Vehicles on Sandy Terrain

Bounded-DWA: An Efficient Local Planner for Ackermann-driven...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Ke Gong Zhiyuan Xu Xinyu Zhang Shanghai Key Laboratory of Trustworthy Computing Engineering Research Center of Software/Hardware Co-Design Technology and Application (MoE) and School of Software Engineering East China Normal University Shanghai Midea Group China

We present a new dynamic window approach (DWA) for mobile vehicles equipped with Ackermann steering geometry that adheres to Ackermann kinematic constraints. By integrating these constraints with the sampling window in DWA, we can further reduce and bound the sampling range and enhance the efficiency of the DWA when a mobile vehicle moves on sandy terrain. Furthermore, we improve the evaluation function to optimize the selected trajectory. Our algorithm is successfully validated in ROS and Gazebo through comparison with other existing local planner such as the original DWA and TEB algorithms. We also successfully deploy our Bounded-DWA in the application of coverage path planning, where tree-planting robots traverse on sandy terrain.

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t-PSI: Efficient Multi-party Private Set Intersection with Threshold 19

t-PSI: Efficient Multi-party Private Set Intersection with T...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Meng, Dan Fu, Zhihui Kong, Chao Qi, Yue Cao, Guitao Oppo Research Institute Shenzhen China Anhui Polytechnic University School of Computer and Information Wuhu China East China Normal University Shanghai Key Laboratory of Trustworthy Computing Shanghai China MoE Engineering Research Center of SH/HW Co-design Technology and Application Ecnu Shanghai China

ISBN: (纸本)9798350346558

Multi-party privacy set intersection (MPSI) enables multiple parties to compute the intersection of their datasets without leaking data privacy. Among existing MPSI protocols, e.g., KMPRT-based protocols, oblivious evaluation of programmable pseudo-random function (OPPRF) or oblivious pseudo-random function (OPRF) is typically used to generate pseudo random numbers, requiring frequent online interactions. To mitigate such communication overheads, we propose the threshold Privacy Set Intersection (t-PSI) protocol for multi-party, leveraging the Shamir Secret Sharing (SSS) protocol and the masking construction mechanism. We propose two types of masking construction mechanisms: Polynomial and Garbled Bloom-filter. To the best of our knowledge, this is the first Secret Sharing-based MPSI protocol. With the significant reduction of inter-party communication, the t-PSI protocol is highly scalable and can handle datasets ranging from 26 to 220. Further, with built-in fault tolerance, t-PSI works even if some parties went offline during protocol execution. As shown in the experiments, for five parties with datasets of 220 items each, the proposed t-PSI takes only 63 seconds, outperforming the state-of-the-art in [1]. © 2022 IEEE.

关键词： Fault tolerance

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Semi/Fully-Automated Segmentation of Gastric-Polyp Using Aquila-Optimization-Algorithm Enhanced Images

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computers, Materials & Continua 2022年第2期70卷 4087-4105页

作者： Venkatesan Rajinikanth Shabnam Mohamed Aslam Seifedine Kadry Orawit Thinnukool Department of Electronics and Instrumentation Engineering St.Joseph’s College of Engineering Chennai600119India Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Faculty of Applied Computing and Technology Noroff University CollegeKristiansand4608Norway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

The incident rate of the Gastrointestinal-Disease(GD)in humans is gradually rising due to a variety of reasons and the Endoscopic/Colonoscopic-Image(EI/CI)supported evaluation of the GD is an approved *** and evaluation of the suspicious section of the EI/CI is essential to diagnose the disease and its *** proposed research aims to implement a joint thresholding and segmentation framework to extract the Gastric-Polyp(GP)with better *** proposed GP detection system consist;(i)Enhancement of GP region using Aquila-Optimization-Algorithm supported tri-level thresholding with entropy(Fuzzy/Shannon/Kapur)and between-class-variance(Otsu)technique,(ii)Automated(Watershed/Markov-Random-Field)and semi-automated(Chan-Vese/Level-Set/Active-Contour)segmentation of GPfragment,and(iii)Performance evaluation and validation of the proposed *** experimental investigation was performed using four benchmark EI dataset(CVC-ClinicDB,ETIS-Larib,EndoCV2020 and Kvasir).The similarity measures,such as Jaccard,Dice,accuracy,precision,sensitivity and specificity are computed to confirm the clinical significance of the proposed *** outcome of this research confirms that the fuzzyentropy thresholding combined with Chan-Vese helps to achieve a better similarity measures compared to the alternative schemes considered in this research.

关键词： Endoscopic/colonoscopic-image gastric polyp aquila-optimizationalgorithm fuzzy entropy chan-vese segmentation

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When Adversarial Example Attacks Meet Vertical Federated Learning 19

When Adversarial Example Attacks Meet Vertical Federated Lea...

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ISBN: (纸本)9798350346558

Federated learning (FL) is proposed to enable efficient machine learning while protecting the privacy of user data. In non-federated scenarios, machine learning models are vulnerable to adversarial attacks. The attacker can construct elaborate adversarial examples, which can greatly reduce the performance of the victim model. It is worth exploring how FL models differ from traditional models in face of complex adversarial attacks. We are also curious about the robustness of different FL modeling methods against adversarial attacks, providing suggestions for selecting suitable FL models in practical applications. Consequently, we perform adversarial example attacks on vertical federated learning and design experiments to study how vertical federated models perform under adversarial example attacks. The simulation results show that federated models are more robust than non-federated models. Besides, in vertical federation learning, decision trees and XGBoost are much more robust than logistic regression and neural networks. © 2022 IEEE.

关键词： Decision trees

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