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arXiv 2023年

作者： Iyer, Srikrishna Hou, Teng Teck Data-Analytics Strategic Technology Centre Group Technology Office ST Engineering Pte Ltd Singapore

Generative Adversarial Networks (GANs) have proven to be a powerful tool for generating realistic synthetic data. However, traditional GANs often struggle to capture complex relationships between features which results in generation of unrealistic multivariate time-series data. In this paper, we propose a Graph-Attention-based Generative Adversarial Network (GAT-GAN) that explicitly includes two graph-attention layers, one that learns temporal dependencies while the other captures spatial relationships. Unlike RNN-based GANs that struggle with modeling long sequences of data points, GAT-GAN generates long time-series data of high fidelity using an adversarially trained autoencoder architecture. Our empirical evaluations, using a variety of real-time-series datasets, show that our framework consistently outperforms state-of-the-art benchmarks based on Frechet Transformer distance and Predictive score, that characterizes (Fidelity, Diversity) and predictive performance respectively. Moreover, we introduce a Frechet Inception distance-like (FID) metric for time-series data called Frechet Transformer distance (FTD) score (lower is better), to evaluate the quality and variety of generated data. We also found that low FTD scores correspond to the best-performing downstream predictive experiments. Hence, FTD scores can be used as a standardized metric to evaluate synthetic time-series data. Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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An attention encoder-decoder RNN model with teacher forcing for predicting consumer price index

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Journal of data, Information and Management 2024年第1期6卷 65-83页

作者： Cobbinah, Maame Alnaggar, Aliaa Data Science and Analytics Toronto Metropolitan University 350 Victoria Street Toronto M5B 2K3 ON Canada Mechanical and Industrial Engineering Department Toronto Metropolitan University 350 Victoria Street Toronto M5B 2K3 ON Canada

The aftermath of the COVID-19 pandemic has led to a global surge in inflation rates across the world, eroding consumer purchasing power and sparking concerns of potential recession. A critical gauge of inflation is the Consumer Price Index (CPI), which measures the average price change of different baskets of goods and services over time. Accurate forecasting of CPI is crucial for policymakers to strategically manage inflation as well as take well informed decisions around fiscal and monetary policy. In this paper, we propose a novel deep learning approach for forecasting the Canadian consumer price index (CPI) utilizing a recurrent neural network encoder-decoder attention model that incorporates the teacher-forcing technique. To the best of our knowledge, this paper is the first to examine the value of encoder-decoder attention models for inflation prediction, shedding light on the significance of advanced deep learning models in macroeconomic predictions. Our results demonstrate the efficacy of this model in accurately forecasting CPI due to its capacity to capture long-term dependencies in inflationary factors. Our model outperforms well-established benchmarks from traditional statistical learning, machine learning, and deep learning methods, including ARIMA, Lasso regressor, support vector regressor, random forest, and artificial neural networks, consistently minimizing forecasting errors across all CPI indicators. The proposed model achieves 45% improvement in RMSE score compared to the best performing machine learning benchmark model. Forecasting results suggest an increase in CPI across all categories, with the energy, food, and gasoline exhibiting the sharpest increase. © Crown 2024.

关键词： Attention models Consumer price index Deep learning Inflation prediction Macroeconomic data RNN encoder-decoder Teacher forcing technique

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U-Park: A User-Centric Smart Parking Recommendation System for Electric Shared Micromobility Services

arXiv

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arXiv 2023年

作者： Yan, Sen O’Connor, Noel E. Liu, Mingming The SFI Insight Centre for Data Analytics The School of Electronic Engineering Dublin City University Dublin Ireland

Electric Shared Micromobility Services (ESMS) has become a vital element within the Mobility as a Service framework, contributing to sustainable transportation systems. However, existing ESMS face notable design challenges such as shortcomings in integration, transparency, and user-centred approaches, resulting in increased operational costs and decreased service quality. A key operational issue for ESMS revolves around parking, particularly ensuring the availability of parking spaces as users approach their destinations. For instance, a recent study illustrated that nearly 13% of shared E-Bike users in Dublin, Ireland, encounter difficulties parking their E-Bikes due to inadequate planning and guidance. In response, we introduce U-Park, a user-centric smart parking recommendation system designed for ESMS, providing tailored recommendations to users by analysing their historical mobility data, trip trajectory, and parking space availability. We present the system architecture, implement it, and evaluate its performance using real-world data from an Irish-based shared E-Bike provider, MOBY Bikes. Our results illustrate U-Park’s ability to predict a user’s destination within a shared E-Bike system, achieving an approximate accuracy rate of over 97.60%, all without requiring direct user input. Experiments have proven that this predictive capability empowers U-Park to suggest the optimal parking station to users based on the availability of predicted parking spaces, improving the probability of obtaining a parking spot by 24.91% on average and 29.66% on maximum when parking availability is limited. © 2023, CC BY.

关键词： Bicycles

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AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity

arXiv

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arXiv 2024年

作者： Li, Xunkai Wu, Zhengyu Zhang, Wentao Sun, Henan Li, Rong-Hua Wang, Guoren Beijing Institute of Technology Beijing China Peking University China National Engineering Labratory for Big Data Analytics and Applications Beijing China

Recently, Federated Graph Learning (FGL) has attracted significant attention as a distributed framework based on graph neural networks, primarily due to its capability to break data silos. Existing FGL studies employ community split on the homophilous global graph by default to simulate federated semi-supervised node classification settings. Such a strategy assumes the consistency of topology between the multi-client subgraphs and the global graph, where connected nodes are highly likely to possess similar feature distributions and the same label. However, in real-world implementations, the varying perspectives of local data engineering result in various subgraph topologies, posing unique heterogeneity challenges in FGL. Unlike the well-known label Non-independent identical distribution (Non-iid) problems in federated learning, FGL heterogeneity essentially reveals the topological divergence among multiple clients, namely homophily or heterophily. To simulate and handle this unique challenge, we introduce the concept of structure Non-iid split and then present a new paradigm called Adaptive Federated Graph Learning (AdaFGL), a decoupled two-step personalized approach. To begin with, AdaFGL employs standard multi-client federated collaborative training to acquire the federated knowledge extractor by aggregating uploaded models in the final round at the server. Then, each client conducts personalized training based on the local subgraph and the federated knowledge extractor. Extensive experiments on the 12 graph benchmark datasets validate the superior performance of AdaFGL over state-of-the-art baselines. Specifically, in terms of test accuracy, our proposed AdaFGL outperforms baselines by significant margins of 3.24% and 5.57% on community split and structure Non-iid split, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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STGAT: A Spatio-Temporal Graph Attention Network for Travel Demand Prediction

STGAT: A Spatio-Temporal Graph Attention Network for Travel ...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Zhang, Tao Yi Wang, YuBo Wei, ZhiCheng Key Lab of Network and Information Security China College of Computer and Cyberspace Security China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Normal University Hebei Province Shijiazhuang China

ISBN: (纸本)9798350327380

Forecasting travel demand is a challenging task due to the complex spatial dependencies and dynamic temporal correlation of the traffic data. Furthermore, a limited representation of the given spatial graph structure may restrict the model's ability to effectively learn spatial-temporal dependencies. To extract latent semantic features from traffic data, this paper proposes a method to construct a traffic graph using the Markov cluster algorithm. The traffic semantic correlation matrix is constructed based on a traffic graph to obtain the deep semantic information. Specifically, this study proposes a Spatio-Temporal Graph Attention Network(STGAT) for travel demand prediction. STGAT uses a graph attention layer based on the Node2Vec graph embedding algorithm, two convolutional layers based on the Markov cluster algorithm, and a long short-term memory network to capture spatial-temporal dependencies. Experimental results on the NYC Taxi dataset and the Chengdu online car dataset demonstrate that STGAT achieved state-of-the-art performance compared to other baseline models. © 2023 IEEE.

关键词： Forecasting

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Network Intrusion Detection using Supervised and Unsupervised Machine Learning 16

Network Intrusion Detection using Supervised and Unsupervise...

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16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022

作者： Shahbandayeva, Lala Mammadzada, Ulviyya Manafova, Ilaha Jafarli, Sevinj Adamov, Abzetdin Z. The George Washington University School of Engineering and Applied Science WashingtonDC United States Ada University School of It and Engineering Baku Azerbaijan Ada University Center for Data Analytics Research Baku Azerbaijan

ISBN: (纸本)9781665451628

Traditional intrusion detection systems may effectively detect known attacks and intrusions with predefined signatures. This requires training the systems to detect various versions of the same attack patterns and constantly keep updated databases of known attack signatures. However, as the skills of security researchers and practitioners expand, so do those of attackers. In order to detect attack types that are unknown, undefined, or designed to bypass the signature and pattern-based intrusion detection systems, the need for more intelligent systems arises. Machine learning is widely used in such systems for this purpose. While researchers and security professionals have designed approaches to this problem using various types of machine learning, our hybrid approach attempts to provide a novel way to effectively detect attacks. This is done by using a set of supervised learning algorithms to detect known attacks and unsupervised learning to detect unknown and zero-day attacks. By utilizing the CSE-CIC-IDS 2018 dataset, we have trained our classifiers to detect benign traffic and 14 known attacks with a selection of 23 features. The network traffic flows that are not classified with a specific level of certainty are sent to the clustering phase to be detected as benign or malicious traffic. Our results indicate that the three classification algorithms used, K-Nearest Neighbors, Random Forest, and Artificial Neural Networks, are able to successfully classify the known attacks with F1-scores between 0.93 and 0.969, and the clustering algorithm HDBSCAN is able to successfully cluster unclassified benign and malicious traffic with unknown labels with F1-scores between 0.85 and 0.957. © 2022 IEEE.

关键词： Classification (of information)

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ACCELERATING DISTRIBUTED STOCHASTIC OPTIMIZATION VIA SELF-REPELLENT RANDOM WALKS

arXiv

引用

arXiv 2024年

作者： Hu, Jie Doshi, Vishwaraj Eun, Do Young Department of Electrical and Computer Engineering North Carolina State University United States Data Science & Advanced Analytics IQVIA Inc.

We study a family of distributed stochastic optimization algorithms where gradients are sampled by a token traversing a network of agents in random-walk fashion. Typically, these random-walks are chosen to be Markov chains that asymptotically sample from a desired target distribution, and play a critical role in the convergence of the optimization iterates. In this paper, we take a novel approach by replacing the standard linear Markovian token by one which follows a nonlinear Markov chain - namely the Self-Repellent Radom Walk (SRRW). Defined for any given ‘base’ Markov chain, the SRRW, parameterized by a positive scalar α, is less likely to transition to states that were highly visited in the past, thus the name. In the context of MCMC sampling on a graph, a recent breakthrough in Doshi et al. (2023) shows that the SRRW achieves O(1/α) decrease in the asymptotic variance for sampling. We propose the use of a ‘generalized’ version of the SRRW to drive token algorithms for distributed stochastic optimization in the form of stochastic approximation, termed SA-SRRW. We prove that the optimization iterate errors of the resulting SA-SRRW converge to zero almost surely and prove a central limit theorem, deriving the explicit form of the resulting asymptotic covariance matrix corresponding to iterate errors. This asymptotic covariance is always smaller than that of an algorithm driven by the base Markov chain and decreases at rate O(1/α2) - the performance benefit of using SRRW thereby amplified in the stochastic optimization context. Empirical results support our theoretical findings. © 2024, CC0.

关键词： Optimization

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A Hybrid Evaluation Index Approach in Optimizing Single Tuition Fee Cluster Validity 6

A Hybrid Evaluation Index Approach in Optimizing Single Tuit...

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6th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2022

作者： Yustanti, Wiyli Iriawan, Nur Irhamah Universitas Negeri Surabaya Faculty of Engineering Department of Informatics Surabaya Indonesia Institut Teknologi Sepuluh Nopember Faculty of Science and Data Analytics Department of Statistics Surabaya Indonesia

ISBN: (纸本)9798350399615

The grouping of the socio-economic level of new students at the time of registration at public universities is a problem faced by all state universities. Identifying the right group will have an impact on the students and the university. The quality of the results of a valid grouping will give a sense of fairness to the parents of students in paying tuition fees. On the other hand, the university also expects that the results of a valid grouping will contribute to optimal revenue. This study aims to evaluate the cluster structure of a single tuition fee at the State University of Surabaya. The existing cluster structure is compared with the results of grouping using nine clustering methods, namely K-Mean, Hierarchical, BIRCH, DBSCAN, Mini Batch K-Mean, Mean Shift, OPTICS, Spectral Clustering, and Mixture Gaussian. The proposed evaluation method is a combination of three evaluation concepts, namely internal validity (Silhouette-Index), external validity (Rand Index), and the percentage conformity value to the expected income factor (Revenue-Index). These three indicators are then calculated as the average value for each clustering method as Hybrid-Index. The highest Hybrid-Index is shown by the Mini Batch K-Mean algorithm, with an average value of 0.6420, so the Mini Batch K-Mean algorithm can be recommended as a method for grouping single tuition fees. © 2022 IEEE.

关键词： clustering clustering validity hybrid evaluation rand index silhouette index

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An Efficient On-Demand Virtual Machine Migration in Cloud Using Common Deployment Model

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Computer Systems Science & engineering 2022年第7期42卷 245-256页

作者： C.Saravanakumar R.Priscilla B.Prabha A.Kavitha M.Prakash C.Arun Department of Information Technology St.Joseph’s Institute of TechnologyChennai600119India Department of Information Technology Loyola-ICAM College of Engineering and TechnologyChennai600034India Department of Electronics and Communication Engineering M.Kumarasamy College of EngineeringKarur639113India Data Science and Analytics Center Karpagam College of EngineeringCoimbatore641032India Department of Electronics and Communication Engineering R.M.K College of Engineering and TechnologyChennai601206India

Cloud Computing provides various services to the customer in aﬂex-ible and reliable *** Machines(VM)are created from physical resources of the data center for handling huge number of requests as a *** tasks are executed in the VM at the data center which needs excess hosts for satis-fying the customer *** VM migration solves this problem by migrating the VM from one host to another host and makes the resources available at any *** process is carried out based on various algorithms which follow a pre-deﬁned capacity of source VM leads to the capacity issue at the destination *** proposed VM migration technique performs the migration process based on the request of the requesting host *** technique can perform in three ways namely single VM migration,Multiple VM migration and Cluster VM *** Deployment Manager(CDM)is used to support through negotiation that happens across the source host and destination host for providing the high quality service to their *** VM migration requests are handled with an exposure of the source host *** proposed analysis also uses the retired instructions with execution by the hypervisor to achieve high *** objective of the proposed technique is to perform a VM migration process based on the prior knowledge of the resource availability in the target VM.

关键词： Cloud computing virtualization hypervisor VMmigration virtual machine

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Facial Expression Recognition Using Multi-Block Deep CNN 5

Facial Expression Recognition Using Multi-Block Deep CNN

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Priyadarshini, V. Srinivasulu Reddy, U. Reddy Chirra, Venkata Rami Mrudula, M. Suneetha, M. National Institute of Technology Dept. of Computer Science & Engineering Andhra Pradesh India Machine Learning & Data Analytics Lab National Institute of Technology Dept. of Computer Applications Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Malla Reddy College of Engineering Department of CSE Telangana India Independent Researcher Amaravati India

ISBN: (纸本)9798331528539

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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