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Multi-layer network embedding on scc-based network with motif

Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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QCNN—A Conceptual Framework for Duplicate Removal in Big data Using Quality Assured Crowd Sourcing Coupled with Neural Networks 1

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International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023

作者： Shantha Kumari, K. Ashik, Mohamed Gowrishankar, K. Kanmani, P. Data Science and Business Systems SRM Institute of Science and Technology Chengalpattu India College of Computing and Information University of Technology and Applied Sciences Salalah Oman Electrical and Electronics Engineering AMET University Tamil Nadu Chennai India

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

Success of an Enterprise depends on the quality of the data available in its backup. The data need to be consistent, error-free and maintain its integrity irrespective of any threats in any form. Quality data is aka clean data helps in the decision-making process of the Enterprise. However due to multiple entry points available in populating a database, there exists the problem of duplication data, which is one form of dirty data. This duplication data has to be removed without compromising the privacy and integrity of the data. The automated computing algorithms have been limited in its capability for the tasks such as labelling, filtering, translating or transcribing data etc. Hence, crowd sourcing famously referred as human computing are used as supplement to identify and eliminate the duplications available in data. However, it is important to understand that the human crowd is prone in error making and it is essential to choose a qualified crowd that does less error. Because the data labelled by the human crowd is fed to the Neural networks further to identify and eliminate the duplication. This paper provides a conceptual framework that aims to identify the duplicate data through qualified crowds and remove the identified duplication using neural network techniques. The framework explains how to qualify the best crowd to do work, so that clean data is assured with the help of ANNs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Errors

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MLSTM: A Novel Modified Long Short-Term Memory Networks for Melody Synthesis Melody Generation

MLSTM: A Novel Modified Long Short-Term Memory Networks for ...

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International Conference on Advanced Communications and Machine Intelligence, MICA 2023

作者： Ganguly, Tanudeep Sharma, Sushmita Deb, Suman Ezugwu, Absalom El-Shamir Saha, Apu Kumar Department of Computer Science and Engineering National Institute of Technology Agartala Tripura Agartala India Department of Computer Science and Engineering Indian Institute of Information Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University Potchefstroom2520 South Africa

ISBN: (纸本)9789819762217

This research elaborates a melody synthesis model utilizing a Modified Long Short-Term Memory (mLSTM) neural network, trained on Kern dataset, preprocessed into Musical Instrument Digital Interface (MIDI) files. After receiving a seed melody as input 01, the model predicts the note that will come next by looking at the notes that have come before it. The model can learn the structures and patterns seen in classical music by using MIDI files as the training dataset. Categorical cross-entropy loss function is used to train the model over several epochs with early halting. To regulate how random the predictions are, the model makes use of a temperature parameter. The assessment demonstrates that the produced tunes have a variety of traits and are aesthetically pleasing to human ears. To justify the performance of the proposed method some criteria were considered and expert evaluation was done. The evaluation shows that the model has generated the melodies that are pleasing to the ear and have a similar structure to the training dataset. The model has several uses, including prediction and music composition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal data

arXiv

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arXiv 2022年

作者： Ankan, Ankur Textor, Johannes Data Science Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands

Conditional independence (CI) tests underlie many approaches to model testing and structure learning in causal inference. Most existing CI tests for categorical and ordinal data stratify the sample by the conditioning variables, perform simple independence tests in each stratum, and combine the results. Unfortunately, the statistical power of this approach degrades rapidly as the number of conditioning variables increases. Here we propose a simple unified CI test for ordinal and categorical data that maintains reasonable calibration and power in high dimensions. We show that our test outperforms existing baselines in model testing and structure learning for dense directed graphical models while being comparable for sparse models. Our approach could be attractive for causal model testing because it is easy to implement, can be used with non-parametric or parametric probability models, has the symmetry property, and has reasonable computational requirements. © 2022, CC BY.

关键词： Learning systems

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Brain MR Images Super-Resolution with the Consistent Features 2022

Brain MR Images Super-Resolution with the Consistent Feature...

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14th International Conference on Machine Learning and computing, ICMLC 2022

作者： You, Senrong Shen, Yanyan Wu, Guocheng Wang, Shuqiang Center for Biomedical Information Technology Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Center for Internet of Things Computing Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Data Recovery Key Laboratory of Sichuan Province College of Mathematics and Information Science Neijiang Normal University China

ISBN: (纸本)9781450395700

Magnetic resonance imaging plays an important role in auxiliary diagnosis and brain exploration. However, limited by hardware, scanning time and cost, it's challenging to acquire high-resolution (HR) magnetic resonance (MR) image clinically. In this paper, consistent feature generative adversarial network (CFGAN) is proposed to produce HR MR images from the low-resolution counterparts. Specifically, a consistent-features encoder is employed to extract the multi-scales features and encode them into latent codes. Then, a progressive generator is utilized to decode the latent codes from high-level to low-level features. With the encoder and generator, the shared consistent features between low-resolution and high-resolution can be fully extracted and recovered. Experiments on ADNI dataset demonstrate that CFGAN outperforms the competing methods quantitatively and qualitatively. © 2022 Owner/Author.

关键词： Generative adversarial networks

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Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental Study [Extended Abstract] 40

Matching Knowledge Graphs in Entity Embedding Spaces: An Exp...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zeng, Weixin Zhao, Xiang Tan, Zhen Tang, Jiuyang Cheng, Xueqi National University of Defense Technology Laboratory for Big Data and Decision China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China Institute of Computing Technology CAS China

ISBN: (纸本)9798350317152

Entity alignment (EA) identifies equivalent entities that locate in different knowledge graphs (KGs), and has attracted growing research interests over the last few years with the advancement of KG embedding techniques. Although a pile of embedding-based EA frameworks have been developed, they mainly focus on improving the performance of entity representation learning, while largely overlook the subsequent stage that matches KGs in entity embedding spaces. Nevertheless, accurately matching entities based on learned entity representations is crucial to the overall alignment performance, as it coordinates individual alignment decisions and determines the global matching result. Hence, it is essential to understand how well existing solutions for matching KGs in entity embedding spaces perform on present benchmarks, as well as their strengths and weaknesses. To this end, in this article we provide a comprehensive survey and evaluation of matching algorithms for KGs in entity embedding spaces in terms of effectiveness and efficiency on both classic settings and new scenarios that better mirror real-life challenges. Based on in-depth analysis, we provide useful insights into the design trade-offs and good paradigms of existing works, and suggest promising directions for future development. © 2024 IEEE.

关键词： Embeddings

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Efficient distributed algorithms for holistic aggregation functions on random regular graphs

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science China(information sciences) 2022年第5期65卷 32-50页

作者： Lin JIA Qiang-Sheng HUA Haoqiang FAN Qiuping WANG Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology Institute for Interdisciplinary Information Science Tsinghua University

In this paper, we propose efficient distributed algorithms for three holistic aggregation functions on random regular graphs that are good candidates for network topology in next-generation data *** three holistic aggregation functions include SELECTION（select the k-th largest or smallest element）,DISTINCT（query the count of distinct elements）, MODE（query the most frequent element）. We design three basic techniques — Pre-order Network Partition, Pairwise-independent Random Walk, and Random Permutation Delivery, and devise the algorithms based on the techniques. The round complexity of the distributed SELECTION is Θ（log N） which meets the lower bound where N is the number of nodes and each node holds a numeric element. The round complexity of the distributed DISTINCT and MODE algorithms are O（log3N/log log N） and O（log2N log log N） respectively. All of our results break the lower bounds obtained on general graphs and our distributed algorithms are all based on the CON GE S T model, which restricts each node to send only O（log N） bits on each edge in one round under synchronous communications.

关键词： distributed algorithms holistic aggregation function random regular graph \({\cal C}{\cal O}{\cal N}{\cal G}{\cal E}{\cal S}{\cal T}\) model communication complexity round complexity

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Geographic and Temporal Deep Learning Method for Traffic Flow Prediction in Highway Network 17th

Geographic and Temporal Deep Learning Method for Traffic F...

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17th EAI International Conference on Collaborative computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Zhang, Tianpu Ding, Weilong Xing, Mengda Chen, Jun Du, Yongkang Liang, Ying School of Information Science and Technology North China University of Technology Beijing100144 China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing100144 China Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Science Beijing China

ISBN: (纸本)9783030926373

Traffic congestion has become an inevitable situation faced by all countries and the prediction accuracy of traffic flow, as one of the means to solve this problem, still needs to be improved. Most studies lack the consideration of the influence of multiple factors such as spatial factors, time series factors and other external factors, which makes the prediction effect of traffic flow unsatisfactory. In this paper a method is proposed based on deep learning that can capture the geographic spatial relationship among toll stations, the dynamic temporal relationship of historical traffic flow, extreme weather and calendar types. On the three metrics of MAPE, MAE, and RMSE, the prediction effect of our model has increased by 30% compared with KNN, GBRT and LSTM models. © 2021, ICST institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Traffic congestion

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Massive: Tractable and robust bayesian learning of many-dimensional instrumental variable models

arXiv

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arXiv 2020年

作者： Bucur, Ioan Gabriel Claassen, Tom Heskes, Tom Department of Data Science Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands

The recent availability of huge, many-dimensional data sets, like those arising from genome-wide association studies (GWAS), provides many opportunities for strengthening causal inference. One popular approach is to utilize these many-dimensional measurements as instrumental variables (instruments) for improving the causal effect estimate between other pairs of variables. Unfortunately, searching for proper instruments in a many-dimensional set of candidates is a daunting task due to the intractable model space and the fact that we cannot directly test which of these candidates are valid, so most existing search methods either rely on overly stringent modeling assumptions or fail to capture the inherent model uncertainty in the selection process. We show that, as long as at least some of the candidates are (close to) valid, without knowing a priori which ones, they collectively still pose enough restrictions on the target interaction to obtain a reliable causal effect estimate. We propose a general and efficient causal inference algorithm that accounts for model uncertainty by performing Bayesian model averaging over the most promising many-dimensional instrumental variable models, while at the same time employing weaker assumptions regarding the data generating process. We showcase the efficiency, robustness and predictive performance of our algorithm through experimental results on both simulated and real-world data. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Adversarial color enhancement: Generating unrestricted adversarial images by optimizing a color filter

arXiv

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arXiv 2020年

作者： Zhao, Zhengyu Liu, Zhuoran Larson, Martha Data Science Group Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands

We introduce an approach that enhances images using a color filter in order to create adversarial effects, which fool neural networks into misclassification. Our approach, Adversarial Color Enhancement (ACE), generates unrestricted adversarial images by optimizing the color filter via gradient descent. The novelty of ACE is its incorporation of established practice for image enhancement in a transparent manner. Experimental results validate the white-box adversarial strength and black-box transferability of ACE. A range of examples demonstrates the perceptual quality of images that ACE produces. ACE makes an important contribution to recent work that moves beyond Lp imperceptibility and focuses on unrestricted adversarial modifications that yield large perceptible perturbations, but remain non-suspicious, to the human eye. The future potential of filter-based adversaries is also explored in two directions: Guiding ACE with common enhancement practices (e.g., Instagram filters) towards specific attractive image styles and adapting ACE to image semantics. Code is available at https: //***/ZhengyuZhao/ACE . Copyright © 2020, The Authors. All rights reserved.

关键词： Image enhancement

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