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Dynamic Community Detection Algorithm based on Allocating and Splitting

Dynamic Community Detection Algorithm based on Allocating an...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Wanchang Jiang Xiaoxi Zhang School of Computer Science Northeast Electric Power University Jilin China

Since the incremental community detection method is affected by the initial network community and the incremental detection process, it is prone to generate the problem of error accumulation. To solve the above problem, a dynamic community detection algorithm is proposed. First, considering the closeness between nodes to detect the community structure of the first snapshot network. After that, active nodes that reflect network changes are identified in adjacent snapshots and allocated to communities. then, during the incremental detection process, considering the influence of adding edges on the accuracy of the community, the edge-added node is defined. In this way, the allocated community structure can be split into multiple local communities and singleton communities constructed by edge-added nodes and other nodes. By merging and optimizing the split communities, the final community structure can be detected. Finally, experiments show that the normalized mutual information and the modularity are improved by an average of 5.23% and 5.00% on synthetic dynamic networks. the modularity is improved by an average of 2.39% on real dynamic networks.

关键词： Image edge detection Heuristic algorithms Merging Dynamic scheduling Detection algorithms Artificial intelligence Mutual information

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A New Focus+Context Visualization Technique for Inspecting Black Oil Reservoir Models 34

A New Focus+Context Visualization Technique for Inspecting B...

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34th SIBGRAPI conference on Graphics, Patterns and Images, SIBGRAPI 2021

作者： Netto, Luiz Felipe Celes, Waldemar Pontifical Catholic University Tecgraf/PUC-Rio Institute Computer Science Department Rio de Janeiro Brazil

ISBN: (纸本)9781665423540

In this paper, we propose a new visualization technique to inspect simulated black oil reservoir models. Numerical reservoir simulation is widely used in the oil & gas industry to predict and plan the exploration of petroleum fields. Of particular interest, it is crucial to understand the physical phenomena around injector and producer wells: how is the distribution of pressure, how oil/gas/water saturation varies over time, and others. Traditional visualization techniques only provide local insights, being hard to understand the three-dimension physical behavior. Another challenge is to handle today's massive models. We propose an efficient and effective focus+context visualization technique employing a cutaway approach, having the wells as objects of interest. We explore cone-shaped and box-shaped view-dependent cutting surfaces. We also allow the user to control the cutting surface aperture freely and to freeze the cut to gain motion parallax depth cues during model exploration. the proposed rendering algorithm runs on GPU, delivering real-time frame rate even for large reservoir models. © 2021 ieee.

关键词： Visualization

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Dark Activity Detection in AIS-Based Maritime networks

Dark Activity Detection in AIS-Based Maritime Networks

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Open Innovations Association (FRUCT), conference of

作者： Bekir Nazmi Görkem Burak Çağlayan Erkam Karaca Candar Karabulut Ömer Korçak Computer Engineering Department Marmara University Istanbul

the Automated Identification System (AIS) is an indispensable tracking system employed in the maritime industry for vessel identification, location tracking, and collision avoidance. While AIS messages provide essential information for maritime traffic management, they also present challenges when vessels aim to conduct operations discreetly or evade observation. this phenomenon, referred to as “dark activity”, involves intentional AIS deactivation by vessel operators seeking to conceal their actions, often related to illicit or illegal maritime activities such as smuggling, piracy or illegal fishing. the detection and monitoring of dark activities pose significant challenges for law enforcement and security agencies. this paper explores innovative approaches to address this issue by harnessing AIS data and incorporating rule-based techniques, as well as machine learning techniques to enhance maritime security efforts. We adopted a local approach where a dark activity of a vessel is detected by nearby ships depending on the previous signals. We implemented a detailed simulation environment based on real and realistic data to run the proposed algorithms. Simulation results show that while rule-based approach is successful in detecting dark activities, it tends to produce false alarms, and ML-based approach provides better overall accuracy.

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A Novel Fuzzy-Based MOPSO Algorithm for Identifying Clusters From Complex networks

A Novel Fuzzy-Based MOPSO Algorithm for Identifying Clusters...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Yue Yang Xiaorui Su Bowei Zhao Yizhou He Lun Hu School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Urumqi China

Many complicated systems can be modeled as complex networks, and a variety of graph clustering algorithms have been proposed to perform accurate clustering analysis for better understanding system behaviors. However, most of them suffer the disadvantage of slow convergence. In this paper, we incorporate multi-objective particle swarm optimization (MOPSO) into a well-established fuzzy clustering algorithm, i.e., FCAN, and propose an improved Fuzzy-based Graph Clustering Algorithm, namely IMFCAN, which retains all the benefits gained with FCAN while achieving significantly fast convergence rate. Specially, IMFCAN enhances the ability of handling the imbalance observed in the distribution of fuzzy membership of nodes by introducing an instance-frequency-weighted regularization (IR) scheme. After that, IMFCAN develops an effective solution to reach a consensus optimization among them by balancing global exploration and local exploitation abilities of particles. Experimental results on four practical datasets demonstrate that IMFCAN performs better than several state-of-the-art clustering algorithm in terms of accuracy and convergence. Hence, IMFCAN is a promising algorithm for addressing the clustering analysis of complex networks.

关键词： Analytical models Social networking (online) Clustering algorithms Complex networks Behavioral sciences Particle swarm optimization Artificial intelligence

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A Turvey-Shapley Value Method for Distribution Network Cost Allocation

A Turvey-Shapley Value Method for Distribution Network Cost ...

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Australasian Universities Power Engineering conference, AUPEC

作者： Donald Azuatalam Archie Chapman Gregor Verbič School of Electrical Engineering and Computer Science The University of Queensland Australia School of Electrical and Computer Engineering The University of Sydney Australia

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

this paper proposes a novel cost-reflective and computationally efficient method for allocating distribution network costs to residential customers. First, the method estimates the growth in peak demand with a 50% probability of exceedance (50POE) and the associated network augmentation costs using a probabilistic long-run marginal cost computation based on the Turvey perturbation method. Second, it allocates these costs to customers on a cost-causal basis using the Shapley value solution concept. To overcome the intractability of the exact Shapley value computation for real-world applications, we implement a fast, scalable and efficient clustering technique based on customers’ peak demand contribution, which drastically reduces the Shapley value computation time. Using customer load traces from an Australian smart grid trial (Solar Home Electricity Data), we demonstrate the efficacy of our method by comparing it with established energy-and peak demand-based cost allocation approaches.

关键词： Power engineering Costs Perturbation methods Tariffs Distribution networks Pricing Probabilistic logic Computational efficiency Smart grids Resource management

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Contrastive Graph Convolutional networks with Degree-specific Augmentation

Contrastive Graph Convolutional Networks with Degree-specifi...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Jiahou Cheng Hui Yan School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

In recent years, Graph Convolutional networks (GCNs) have achieved tremendous success on semi-supervised tasks by effectively gathering local features for nodes. However, we find that the classification performance of nodes with different degrees on GCNs varies greatly: a considerable number of low-degree nodes perform poorly due to a lack of information, while high-degree nodes receive more noise due to their large receptive fields, resulting in suboptimal performance. Graph Convolutional Network (GCN) is widely used as an encoder in graph contrastive learning models, yet a large number of low-degree nodes are generated during crucial graph augmentation, resulting in suboptimal performance. To this end, we propose a novel graph contrastive representation learning method with degree-specific augmentation, which incorporates various priors for topological and semantic aspects of the graph. Specifically, on the topological level, we design a degree-specific augmentation scheme to expand the receptive field of low-degree nodes and reduce the noise received by high-degree nodes. On the node attribute level, we comprehensively consider node degree and feature variance to force the model to recognize the under-lying semantic information. We jointly utilize supervised and unsupervised contrastive losses to improve model performance. Compared with other advanced methods, the experimental results on various real datasets strongly verify the effectiveness of our method.

关键词： Representation learning Semantics Force Robustness Data models Convolutional neural networks Task analysis

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Reusing Comparator networks in Pseudo-Boolean Encodings

Reusing Comparator Networks in Pseudo-Boolean Encodings

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Michał Karpiński Marek Piotrów Institute of Computer Science University of Wroclaw Wroclaw Poland

A Pseudo-Boolean (PB) constraint is a linear in-equality constraint over Boolean literals. One of the popular, efficient ideas used to solve PB-problems (a set of PB-constraints) is to translate them to SAT instances (encodings) via, for example, sorting networks, then to process those instances using state-of-the-art SAT-solvers. In this paper we show an improvement of such technique. By using a variation of a greedy set cover algorithm, when adding constraints to our encoding, we reuse parts of the already encoded PB-instance in order to decrease the size (the number of variables and clauses) of the resulting SAT instance. the experimental evaluation shows that the proposed method increases the number of solved instances.

关键词： NP-hard problem Approximation algorithms Encoding Artificial intelligence Optimization Sorting

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GANs for Class-Imbalanced Data: A Meta-Analysis of Github Projects

GANs for Class-Imbalanced Data: A Meta-Analysis of GitHub Pr...

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Rick Sauber-Cole Taghi M. Khoshgoftaar Justin M. Johnson College of Engineering and Computer Science Florida Atlantic University Boca Raton Florida

Generative Adversarial networks (GANs) have increasingly been the subject of intense research interest for their ability to augment datasets to correct for class imbalance. the collaborative and complex code bases for these GANs are often written in a high-level, general-purpose programming language such as Python and housed on the Github platform. the goal of this work is to summarize the research aims of 18 Github repositories of projects that implement GANs in regimes of class imbalance, in both tabular and non-tabular settings, as well to summarize and analyze the patterns and characteristics of these code bases. With respect to the latter task, we conduct our analysis from a perspective of library reliance and from the structural properties of the code base. the insights discovered herein are meant to serve as a gentle introduction to the various tools available and recommended best practices for the enterprising researcher seeking to apply GANs as data augmenters in imbalanced settings.

关键词： Deep learning computer vision Codes Focusing Generative adversarial networks Data engineering Libraries

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Graph Structure Guided Transformer for Semantic Segmentation

Graph Structure Guided Transformer for Semantic Segmentation

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Luyang Qian Canlong Zhang Zhixin Li Zhiwen Wang School of Computer Science and Engineering Guangxi Normal University Guilin China Guangxi Key Lab of Multi-source Information Mining & Security Guilin China School of Computer Science and Technology Guangxi University of Science and Technology Liuzhou China

Segmentation is an essential operation of image processing, and utilizing long-range context information is the key for pixel-wise prediction tasks such as semantic segmentation. Convolutional Neural networks (CNNs) are good at modeling local relationships through convolutional operations, but they are often inefficient in capturing global relationships between distant regions and require stacking multiple convolutional lay-ers. Utilizing the advantages of transformer in modeling long-range dependency, this paper proposes a novel Graph Structure Guided Transformer (GSGT) to realize semantic segmentation. Different from the previous methods that hard-divide the image in a regular grid manner, our graph projection method maps the two-dimensional feature map into a graph structure according to certain semantic relevance, so as to meet the data structure form required by the transformer. Meanwhile, to fully utilize the graph structure information, we also propose a graph embedding attention module, which utilizes the local topology of the graph structure to complement the global context of transformer. Moreover, GSGT is easy to be incorporated with various CNN backbones and transformer model variants to significantly improve the segmentation accuracy and convergence speed. Experiments on Cityscapes, VOC and ADE20K datasets demonstrate that the proposed method performs well in semantic seamentation task.

关键词： Semantic segmentation Semantics Stacking Network architecture Transformers Data structures Robustness

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Multi-Objective Optimization for Energy Management in Residential Microgrids with local Energy Markets

Multi-Objective Optimization for Energy Management in Reside...

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Australasian Universities Power Engineering conference, AUPEC

作者： Haesum Ali Daniel Burmester Ramesh Rayudu Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

Improper management of increasing distributed rooftop solar photovoltaic (PV) and electric vehicles (EVs) can impact distribution network stability and lead to the degradation of critical assets. local energy markets (LEM) have emerged as a potential solution to optimise energy management, especially in residential communities. this paper proposes a novel LEM-based multi-objective optimisation scheme for energy management in a microgrid (MG) network comprising of a residential community. the objective is to minimize operational costs while mitigating peak grid import and reverse power flow. the optimisation framework integrates mixed-integer linear programming (MILP) based decision-making for power trading and scheduling of flexible devices. this study considers real-time data and utilizes statistical models for numerical simulations. Simulation results demonstrate reductions in peak grid import and reverse power flow, alongside operational cost savings compared to traditional home energy management system (HEMS) and LEM-based approaches. these findings underscore the effectiveness of the proposed approach in enhancing the stability and cost-efficiency of residential MGs considering increasing PV and EV integration.

关键词： Costs Simulation Microgrids Power system stability Stability analysis Real-time systems Power markets Optimization Numerical stability Load flow

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