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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Li, Xujie Shao, Fei Li, Haotian College of Computer Science and Software Engineering Hohai University Nanjing China College of Information Science and Engineering Hohai University Nanjing China

ISBN: (纸本)9798350390643

Currently, the basis for critical nodes definition and identification lies in the representation learning of the network and the extraction of local and global features of the nodes. The effectiveness of the algorithm is closely related to network modeling and network structural characteristics. However, with the increasing complexity of real-world systems, utilizing the continuity and integrity of traditional bipartite graph representation faces significant challenges. Different from traditional graph modeling, the hyperedges of a hypergraph can connect multiple nodes simultaneously, thus forming richer structural features and interaction information between nodes for the analysis and judgment of critical nodes. To more accurately identify critical nodes in complex networks, we propose an algorithm that establishes a hypergraph representation for traditional graph data to extract higher-order features of nodes for neural network training, in order to identify critical nodes in complex networks. The effectiveness of this method is proven by comparing it with various critical node identification algorithms under different infection rates. © 2024 IEEE.

关键词： Complex networks

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A Novel Semi-supervised IoT Time Series Anomaly Detection Model Using Graph Structure Learning 19th

A Novel Semi-supervised IoT Time Series Anomaly Detection Mo...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Song, Weijian Chen, Peng Chen, Juan Xia, Yunni Li, Xi Xi, Qinghui He, Hongxia School of Computer and Software Engineering XiHua University Chengdu China School of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9783031545276

Internet of Things (IoT) is an evolving paradigm for building smart cross-industry. The data gathered from IoT devices may have anomalies or other errors for various reasons, such as malicious activities or sensor failures. Anomaly detection is thus in high need for guaranteeing trustworthy execution of IoT applications. Existing IoT anomaly detection methods are usually built upon unsupervised methods and thus can be inadequate when facing complex IoT data regularity. In this article, we propose a semi-supervised approach for detecting IoT time series anomalies based on Graph Structure Learning (GSL) using multi-layer perceptron Graph Convolutional Networks (GCN) and the Mean Teachers (MT) mechanism. The proposed model is capable of leveraging a small amount of labeled data (1% to 10%) to achieve high detection accuracy. We adopt Mean Teachers to utilize unlabeled data for enhancing the model’s detection performance. Moreover, we design a novel graph structure learning layer to adaptively capture the IoT data features among different nodes. Experimental results clearly suggest that the proposed model outperforms its competitors on two public IoT datasets, achieving 82.85% in terms of F1 score and 22.8% increase. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Time series

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Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

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computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan Akram Imran Khan Javed Rashid Mubbashar Saddique Muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of Computer Science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan Machine Learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

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Motif-oriented influence maximization for viral marketing in large-scale social networks 38

Motif-oriented influence maximization for viral marketing in...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Mingyang Cao, Weiji Liao, Hao Mao, Rui Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

The influence maximization (IM) problem aims to identify a budgeted set of nodes with the highest potential to influence the largest number of users in a cascade model, a key challenge in viral marketing. Traditional IM approaches consider each user/node independently as a potential target customer. However, in many scenarios, the target customers comprise motifs, where activating only one or a few users within a motif is insufficient for effective viral marketing, which, nevertheless, receives little attention. For instance, if a motif of three friends planning to dine together, targeting all three simultaneously is crucial for a restaurant advertisement to succeed. In this paper, we address the motif-oriented influence maximization problem under the linear threshold model. We prove that the motif-oriented IM problem is NP-hard and that the influence function is neither supermodular nor submodular, in contrast to the classical IM setting. To simplify the problem, we establish the submodular upper and lower bounds for the influence function. By leveraging the submodular property, we propose a natural greedy strategy that simultaneously maximizes both bounds. Our algorithm has an approximation ratio of τ·(1−1/e−Ε) and a near-linear time complexity of O((k+l)(m+η) log η/Ε2). Experimental results on diverse datasets confirm the effectiveness of our approach in motif maximization. © 2024 Neural information processing systems foundation. All rights reserved.

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Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

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computers, Materials & Continua 2023年第1期74卷 363-378页

作者： Khalid Hamid Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Due to a tremendous increase in mobile traffic,mobile operators have started to restructure their networks to offload their *** directions will lead to fundamental changes in the design of future Fifthgeneration(5G)cellular *** the formal reason,the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph *** number that can be uniquely calculated by a graph is known as a graph *** the last two decades,innumerable numerical graph invariants have been portrayed and used for correlation *** any case,no efficient assessment has been embraced to choose,how much these invariants are connected with a network *** paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical *** K-banhatti sombor invariants(KBSO)and Contrharmonic-quadratic invariants(CQIs)are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or *** the hexagonal networks are used in mobile base stations in layered,forms called *** review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced *** deduced outcomes can be utilized for the modeling of mobile cellular networks,multiprocessors interconnections,microchips,chemical compound synthesis and memory interconnection *** results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network(MBSN)for the high load of traffic and minimal traffic also.

关键词： Hexagonal networks k-banhatti sombor invariants maple network graph artificial intelligence contraharmonic-quadratic invariants honeycomb

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Quick Hidden Layer Size Tuning in ELM for Classification Problems

Mendel

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Mendel 2024年第1期30卷 1-14页

作者： Albtoush, Audi Fernandez-Delgado, Manuel Maarouf, Haitham Al Nawaiseh, Asmaa Jameel Faculty of Computer Science and Information Technology Jerash University Jordan Santiago De Compostela University Spain Software Engineering Department Mut’ah university Jordan

The extreme learning machine is a fast neural network with outstanding performance. However, the selection of an appropriate number of hidden nodes is time-consuming, because training must be run for several values, and this is undesirable for a real-time response. We propose to use moving average, exponential moving average, and divide-and-conquer strategies to reduce the number of training’s required to select this size. Compared with the original, constrained, mixed, sum, and random sum extreme learning machines, the proposed methods achieve a percentage of time reduction up to 98% with equal or better generalization ability. © 2024, Brno University of Technology. All rights reserved.

关键词： Knowledge acquisition

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Efficient-Learning Grasping and Pushing in Dense Stacking via Mask Function and Pixel Overlap Rate 7

Efficient-Learning Grasping and Pushing in Dense Stacking vi...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Lian, Jie Jiang, Juncheng Qiu, Chaochao Pan, Qinghui Dong, Yongxiang Wang, Zhao Wang, Dong Dalian University of Technology School of Control Science and Engineering Dalian China University of Science and Technology Liaoning Shool of Computer Science and Software Engineering China

ISBN: (纸本)9798350363173

In the field of robotic grasping, grasping tightly stacked objects is a formidable challenge. The presence of non-target objects significantly increases the risk of grasping failure. To solve this problem, the aim is to take action while grasping, reduce clutter, and effectively complete grasping tasks. Based on deep reinforcement learning, the Efficient Learning Grasping(ELG) framework is proposed that integrates pushing actions alongside grasping to efficiently pick up objects from cluttered environments with reduced training time. However, in the process of grasping, the agent is rewarded only when the grasp is successful, so the data collection is extremely inefficient and spends a lot of time on the training of grasping strategy. In our work, two strategies are proposed to improve the training efficiency. First, two mask functions are introduced as prior knowledge to enable the agent to focus on more meaningful regions while ignoring some unnecessary regions, aiming at effective learning. Second, The Pixel Overlap Rate (POR) is introduced to quantify the environment clutter level, and set a new push action reward function based on the POR. The implementation of the above ideas in a simulation environment and transferred the learned models to the real world and verified them in practice. Compared with existing method, our method improves the training efficiency of the initial algorithm while having a higher success rate of grasping. Detailed grasping video at https://***/CqtSvaVl60M. © 2024 IEEE.

关键词： Reinforcement learning

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software development processes in ocean system modeling

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International Journal of Modeling, Simulation, and Scientific Computing 2022年第2期13卷 47-69页

作者： Reiner Jung Sven Gundlach Wilhelm Hasselbring Software Engineering Group Department of Computer Science Kiel UniversityKielGermany

Scientific modeling provides mathematical abstractions of real-world systems and builds software as implementations of these mathematical *** science is a multidisciplinary discipline developing scientific models and simulations as ocean sys-tem models that are an essential research *** software engineering and information systems research,modeling is also an essential *** particular,business process modeling for business process management and systems engineering is the activity of representing processes of an enterprise,so that the current process may be analyzed,improved and *** this paper,we employ process modeling for analyzing sci-entific software development in ocean science to advance the state in engineering of ocean system models and to better understand how ocean system models are developed and maintained in ocean *** interviewed domain experts in semi-structured inter-views,analyzed the results via thematic analysis,and modeled the results via the Busi-ness Process Modeling Notation(BPMN).The processes modeled as a result describe an aspired state of software development in the domain,which are often not(yet)*** enables existing processes in simulation-based system engineering to be improved with the help of these process models.

关键词： Scientific modeling software development processes process modeling the-matic analysis

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Geometry-Aware Self-Supervised Indoor 360° Depth Estimation via Asymmetric Dual-Domain Collaborative Learning

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IEEE Transactions on Multimedia 2025年 27卷 3224-3237页

作者： Wang, Xu He, Ziyan Zhang, Qiudan Yang, You Zhao, Tiesong Jiang, Jianmin Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Huangzhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Fuzhou University College of Physics and Information Engineering Fuzhou350108 China

Being able to estimate monocular depth for spherical panoramas is of fundamental importance in 3D scene perception. However, spherical distortion severely limits the effectiveness of vanilla convolutions. To push the envelope of accuracy, recent approaches attempt to utilize Tangent projection (TP) to estimate the depth of 360° images. Yet, these methods still suffer from discrepancies and inconsistencies among patch-wise tangent images, as well as the lack of accurate ground truth depth maps under a supervised fashion. In this paper, we propose a geometry-aware self-supervised 360° image depth estimation methodology that explores the complementary advantages of TP and Equirectangular projection (ERP) by an asymmetric dual-domain collaborative learning strategy. Especially, we first develop a lightweight asymmetric dual-domain depth estimation network, which enables to aggregate depth-related features from a single TP domain, and then produce depth distributions of the TP and ERP domains via collaborative learning. This effectively mitigates stitching artifacts and preserves fine details in depth inference without overspending model parameters. In addition, a frequent-spatial feature concentration module is devised to simultaneously capture non-local Fourier features and local spatial features, such that facilitating the efficient exploration of monocular depth cues. Moreover, we introduce a geometric structural alignment module to further improve geometric structural consistency among tangent images. Extensive experiments illustrate that our designed approach outperforms existing self-supervised 360° depth estimation methods on three publicly available benchmark datasets. © 1999-2012 IEEE.

关键词： Self-supervised learning

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A Length-Adaptive Non-Dominated Sorting Genetic Algorithm for Bi-Objective High-Dimensional Feature Selection

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1834-1844页

作者： Yanlu Gong Junhai Zhou Quanwang Wu MengChu Zhou Junhao Wen College of Computer Science Chongqing UniversityChongqing 400044China IEEE Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA College of Big Data and Software Engineering Chongqing UniversityChongqing 400044China

As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected *** computing(EC)is promising for FS owing to its powerful search ***,in traditional EC-based methods,feature subsets are represented via a length-fixed individual *** is ineffective for high-dimensional data,because it results in a huge search space and prohibitive training *** work proposes a length-adaptive non-dominated sorting genetic algorithm(LA-NSGA)with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective highdimensional *** LA-NSGA,an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths,and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space ***,a dominance-based local search method is employed for further *** experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms.

关键词： Bi-objective optimization feature selection(FS) genetic algorithm high-dimensional data length-adaptive

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