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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Arvind Hans M. Vigenesh Yuvaraj S Suvarna R. Bhagwat Hamza Zaki Chiging Yamang Faculty Of Business Mangagement & Commerce Usha Martin University Ranchi India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department Of Computer Science Mangalayatan University Aligarh India Department of Bussiness & Management Himalayan University Itanagar India

Top privateers (PGP) is an encryption protocol that shields statistics saved, exchanged, or transmitted over wireless networks. PGP provides a green manner to comfy WiFi networks and communications using symmetric and publickey cryptography. This paper evaluates and examines the utility of PGP in wireless network protection. The paper critiques the layout and implementation of PGP on specific varieties of Wi-Fi networks, which include the partnerships to be had for implementing PGP in pre-present and destiny wireless infrastructures. Similarly to the numerous partnerships, the paper explores PGP's unique protocols and technologies and how they can be integrated inside Wi-Fi networks based on their extraordinary capabilities and functions. The paper additionally discusses the commercial advertising of PGP, its effect on wireless community protection, and the packages of PGP, in addition to a number of the demanding situations and threats related to its use. Moreover, the paper highlights the advantages of implementing PGP in wireless community safety. It gives a diffusion of trying out and implementation strategies that can be used to ensure a successful implementation. The paper concludes with a dialogue about the possible destiny directions of PGP and how it could be used to ease and protect wireless networks.

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Towards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context

arXiv

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arXiv 2023年

作者： Song, Yicheng Gao, Shuyong Xing, Haozhe Cheng, Yiting Wang, Yan Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Keenon Robotics Co. Ltd. Shanghai China Academy for Engineering & Technology Fudan University Shanghai China

Unsupervised salient object detection aims to detect salient objects without using supervision signals eliminating the tedious task of manually labeling salient objects. To improve training efficiency, end-to-end methods for USOD have been proposed as a promising alternative. However, current solutions rely heavily on noisy handcraft labels and fail to mine rich semantic information from deep features. In this paper, we propose a self-supervised end-to-end salient object detection framework via top-down context. Specifically, motivated by contrastive learning, we exploit the self-localization from the deepest feature to construct the location maps which are then leveraged to learn the most instructive segmentation guidance. Further considering the lack of detailed information in deepest features, we exploit the detail-boosting refiner module to enrich the location labels with details. Moreover, we observe that due to lack of supervision, current unsupervised saliency models tend to detect non-salient objects that are salient in some other samples of corresponding scenarios. To address this widespread issue, we design a novel Unsupervised Non-Salient Suppression (UNSS) method developing the ability to ignore non-salient objects. Extensive experiments on benchmark datasets demonstrate that our method achieves leading performance among the recent end-to-end methods and most of the multi-stage solutions. The code is available. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Effects of Variable Viscosity and Internal Heat Generation on Rayleigh–Bénard Convection in Newtonian Dielectric Liquid

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International Journal of Applied and Computational Mathematics 2021年第3期7卷 1-17页

作者： Shivaraj, Bhavya Siddheshwar, P.G. Uma, D. Department of Mathematics Dayananda Sagar Academy of Technology and Management Bengaluru 560082 Karnataka India Department of Mathematics CHRIST (Deemed to be University) Bengaluru 560029 Karnataka India Department of Computer Science and Engineering PES University Bengaluru 560085 Karnataka India

The onset of Rayleigh–Bénard convection of variable-viscosity Newtonian dielectric liquid confined between two parallel plates is subject to free-free isothermal boundary condition. The combined and individual effects of temperature-dependent and electric-field-dependent variable-viscosity along with the internal heat generation are studied using the higher order Galerkin technique. This theoretical study shows that even a mild temperature-dependent variable-viscosity destabilizes the system and the electric-field-dependent variable-viscosity stabilizes the system both in the absence/presence of heat source/sink. © 2021, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Electric field dependent variable viscosity Electric Rayleigh number Internal heat generation Smart liquids Temperature dependent variable viscosity

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Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM

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computers, Materials & Continua 2020年第1期62卷 199-216页

作者： Fangming Bi Xuanyi Fu Wei Chen Weidong Fang Xuzhi Miao Biruk Assefa College of Computer Science and Technology China University of Mining and TechnologyXuzhou221116China College of Computer Science and Technology Mine Digitization Engineering Research Center of the Ministry of EducationChina University of Mining and TechnologyXuzhou221116China College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710054China Key Laboratory of Wireless Sensor Network&Communication Shanghai Institute of Micro-System and Information TechnologyChinese Academy of SciencesShanghai201899China Infromation Communication Technology Department Wollo UniversityDessie EthiopiaP.o Box 1145Ethiopia

Aiming at the defects of the traditional fire detection methods,which are caused by false positives and false negatives in large space buildings,a fire identification detection method based on video images is *** algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image,which can eliminate most non-fire ***,the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively ***,based on the segmented image,the dynamic and static features of the fire flame are further analyzed and extracted in the area of the suspected fire ***,the dynamic features of the extracted fire flame images were fused and classified by improved fruit fly optimization support vector machine,and the recognition results were *** video-based fire detection method proposed in this paper greatly improves the accuracy of fire detection and is suitable for fire detection and identification in large space scenarios.

关键词： Fire detection image segmentation feature extraction fruit fly optimization support vector machine

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Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation

arXiv

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arXiv 2023年

作者： Shen, Haoran Zhang, Yifu Wang, Wenxuan Chen, Chen Liu, Jing Song, Shanshan Li, Jiangyun School of Automation and Electrical Engineering University of Science and Technology Beijing China Center for Research in Computer Vision University of Central Florida United States National Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences China

Recent works have shown that the computational efficiency of 3D medical image (e.g. CT and MRI) segmentation can be impressively improved by dynamic inference based on slice-wise complexity. As a pioneering work, a dynamic architecture network for medical volumetric segmentation (i.e. Med-DANet [44]) has achieved a favorable accuracy and efficiency trade-off by dynamically selecting a suitable 2D candidate model from the pre-defined model bank for different slices. However, the issues of incomplete data analysis, high training costs, and the two-stage pipeline in Med-DANet require further improvement. To this end, this paper further explores a unified formulation of the dynamic inference framework from the perspective of both the data itself and the model structure. For each slice of the input volume, our proposed method dynamically selects an important foreground region for segmentation based on the policy generated by our Decision Network and Crop Position Network. Besides, we propose to insert a stage-wise quantization selector to the employed segmentation model (e.g. U-Net) for dynamic architecture adapting. Extensive experiments on BraTS 2019 and 2020 show that our method achieves comparable or better performance than previous state-of-the-art methods with much less model complexity. Compared with previous methods Med-DANet and TransBTS with dynamic and static architecture respectively, our framework improves the model efficiency by up to nearly 4.1 and 17.3 times with comparable segmentation results on BraTS 2019. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

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Computing Skyline Groups:An Experimental Evaluation

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Tsinghua science and technology 2019年第2期24卷 171-182页

作者： Haoyang Zhu Xiaoyong Li Qiang Liu Hao Zhu Academy of Military Science of the People's Liberation Army Academy of Ocean Science and Engineering National University of Defense Technology College of Computer National University of Defense Technology

Skyline group, also named as combinational skyline or group-based skyline, has attracted more attention recently. The concept of skyline groups is proposed to address the problem in the inadequacy of the traditional skyline to answer queries that need to analyze not only individual points but also groups of points. Skyline group algorithms aim at finding groups of points that are not dominated by any other same-size groups. Although two types of dominance relationship exist between the groups defined in existing works, they have not been compared systematically under the same experimental framework. Thus, practitioners face difficulty in selecting an appropriate definition. Furthermore, the experimental evaluation in most existing works features a weakness,that is, studies only experimented on small data sets or large data sets with small dimensions. For comprehensive comparisons of the two types of definition and existing algorithms, we evaluate each algorithm in terms of time and space on various synthetic and real data sets. We reveal the characteristics of existing algorithms and provide guidelines on selecting algorithms for different situations.

关键词： skyline queries skyline groups performance evaluation

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Using the Heterogeneous Network's Self-Adaptive Topological Structure for Drug-Protein Interaction Prediction 2

Using the Heterogeneous Network's Self-Adaptive Topological ...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Sharma, Vikrant Niranjani, B. Debnath, Baishakhi Mishra, V.A. Yuvaraj, K. Kataria, Ansh Graphic Era Hill University Uttarakhand Dehradun248002 India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Business Administration Chennai127 India School of Management - Ug Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India

ISBN: (纸本)9798350388916

The prediction of drug-protein interactions (DPIs) has been the subject of many robust computer approaches using graph neural networks (GNNs). The burden in laboratories and the costs associated with drug discovery and repurposing may be significantly decreased with its help. The unreported indications of many proteins and medications render their clinical activities unclear, nonetheless. Therefore, using the data that is currently available, it is challenging to construct a trustworthy drug-protein heterogeneous network that can characterize the interactions between medicines and proteins. We provide TAG, a DPI prediction approach that may self-adaptively alter the topological framework of the heterogeneous networks, as a solution to this challenge. Graph Neural Networks (GNNs) have recently gained a lot of interest due to their exceptional compatibility with viral proteins and pharmacological substances. Building such a model that accurately depicts the molecular structures of living molecules is, however, no easy feat. Also, newly created GNNs have been overfitting on Coronavirus datasets as they are still not widely used. Next, we propose a new model called Topology-enhanced Graph Neural Network using Multi-hop Gating Mechanism to fix those problems. Firstly, our model is able to learn more specific properties of proteins and chemicals. Conversely, we provide a novel gating approach for improving atom representation using non-neighbor data. In order to implement the drug-protein heterogeneous network, TAG sets up a representation learning module that is based on graph attention network. The network's topology may be fine-tuned in response to the training loss, and it can learn the connections between nodes using their properties. Lastly, TAG uses the embeddings of medicines and proteins to forecast the likelihood of their interactions. The experimental findings demonstrate that TAG is capable of significantly enhancing the network's topological structure. Across

关键词： Graph neural networks

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Using DL Measure and Distance to Assess Text Mining Document Classification Problems

Using DL Measure and Distance to Assess Text Mining Document...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Vivek Deshpande K. Yuvaraj Akhilesh Kalia Praveena J Shivangi Gupta Uma C Swadimath Department of Computer Engineering Vishwakarma Institute of Technology Pune Maharashtra India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Center Quantum University Roorkee Uttarakhand India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

In this study, we take an asymptotic approach to the issue of document categorization in text mining. Some intriguing aspects may be shown graphically through the formalisation of statistical hypothesis testing as a text mining task. Due to its experimental efficacy in numerous case studies, various heuristics are used to practical analysis in the text mining issue. This method will provide the necessary theoretical explanation for the efficacy of text mining approaches. Documents are sorted using a distance metric in word vector area and deep learning algorithms. The study also examines the effectiveness of distance measures from the fresh perspective of asymptotic analysis.

关键词： Text mining Measurement Deep learning Visualization Documentation Vectors Information and communication technology Classification algorithms Testing

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A Novel Global Clustering Coefficient- Dependent Degree Centrality (Gccdc) Metric for Large Network Analysis Using Real-World Datasets

SSRN

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SSRN 2022年

作者： Fatima, Ubaida Hina, Saman Wasif, Muhammad Mathematics Department NED University of Engineering and Technology Karachi Pakistan Department of Computer Science and IT NED University of Engineering and Technology Karachi Pakistan Dr. Essa Prime Fitness Academy Prime Fitness Karachi Pakistan

Nowadays, it is imperative to identify the combination of profitable products (nodes) within large product networks. Data exploration for such broad-spectrum product networks require sophisticated techniques for their analysis and meaningful inferences. The most commonly used techniques are the centrality metrics due to their efficiency in computation. Centrality metrics include Degree Centrality (DC), Closeness Centrality (CC), Betweenness Centrality (BC), Eigenvector Centrality (EVC), Katz Centrality (KC), and the local clustering coefficient-dependent degree centrality (LCCDC or LD). In this research, a novel approach the global clustering coefficient-dependent degree centrality (GCCDC or GD) method has been formulated for the analysis of links of the profitable products (nodes) in a large product network. GCCDC or GD is formulated by using the global clustering coefficient method which is efficient and accurate for large product networks such as product Amazon network. Furthermore, three correlation coefficients that are Pearson’s, Spearman’s, and Kendall’s have been used for evaluation. The results have shown that GD is preferable over LD to avoid uncertainties in computation of results for real-world datasets. To prove the scalability of the novel method, a dataset from different domain (biological yeast protein-protein interaction (PPI) dataset) was also analyzed using similar metrics and shown improved results. © 2022, The Authors. All rights reserved.

关键词： Large dataset

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GCFormer: Granger Causality based Attention Mechanism for Multivariate Time Series Anomaly Detection

GCFormer: Granger Causality based Attention Mechanism for Mu...

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IEEE International Conference on Data Mining (ICDM)

作者： Shiwang Xing Jianwei Niu Tao Ren State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Zhongguancun Laboratory Beijing China Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences China

Multivariate time series anomaly detection, crucial for ensuring the safety of real-world systems, primarily focuses on extracting characteristics from time series under normal condition, and identifying potential anomalies throughout the evaluation process. Recent studies have achieved fruitful progress through mining the spatio-temporal relationships from multivariate time series, however, these approaches mostly neglect the latency among series which could lead to higher false alarm. Granger causality presents a promising solution to extract these inherent time-lagged relationships. Nonetheless, the intricate and dynamic relationships among numerous time series in real-world systems surpass the ability of linear Granger causality. To address this, we extend the linear Granger causality and propose the Granger Causal Former (GCFormer), a novel approach that leverages attention mechanisms to learn the inherent causal spatio-temporal relationships between historical and current timestamps across multiple time series. Specifically, GCFormer develops a Spatio-Mask (SM) to select the top-k most relevant series and a Temporal-Mask (TM) to concentrate attention on more recent historical timestamps. Moreover, to mitigate overfitting and ensure a smooth training process, GCFormer introduces an adjust top-k method and a TM penalty term. We evaluated GCFormer on four real-world benchmark datasets, demonstrating its superior performance over state-of-the-art approaches. Further analysis and a case study highlight the model’s novelty and interpretability.

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