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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Qiuhao Shi Xiaolong Xu Xuanyan Liu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

Because of the complex topology of urban road and the dynamic changes of traffic information over time, how to accurately and efficiently predict traffic flow has become a difficult problem. In order to capture spatiotemporal dependence at the same time, in this paper we propose BLRGCN, a dynamic traffic flow prediction model based on spatiotemporal graph convolutional network. This model improves the performance of bidirectional long short-term memory network (Bi-LSTM) by using residual network (ResNet), which can directly connect the input short to the output of nonlinear layer, and then combines the improved Bi-LSTM with graph convolutional neural network (GCN). BLRGCN model uses graphs to construct the network information of urban roads. In the graph, nodes express roads and edges express relationship between roads. The attributes of nodes describe traffic information on roads. We use GCN to extract the topological features of road network to capture spatial dependence. Besides, the BLRGCN model uses Bi-LSTM to obtain the information of the whole input sequence and extract the dynamic change characteristics of traffic data on the road to capture the temporal dependence. Compared with other methods on two traffic datasets, BLRGCN model has improved RMSE index by 0.53%-37.03% and 23.69%-43.83% respectively. The MAE index was improved by 34.89%-37.78% and 7.80%-51.88%, the accuracy index was improved by 0.19%-82.53% and 4.55%- 10.45%, the R2 index was improved by 0.24%-25.65% and 16.67%-29.94%, and the var index is improved by 0.25%-24.24% and 16.99%-30.39% respectively, which indicates that BLRGCN model has good prediction performance.

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ConvTrans-TPS: A Convolutional Transformer Model for Disk Failure Prediction in Large-Scale Network Storage Systems

ConvTrans-TPS: A Convolutional Transformer Model for Disk Fa...

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International Conference on computer Supported Cooperative Work in Design

作者： Shicheng Xu Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

Disk failure is one of the most important reliability problems in large-scale network storage systems. Disk failure may lead to serious data loss and even disastrous consequences if the missing data cannot be recovered. Therefore, predicting disk failures is an important means of ensuring storage security in network storage systems. However, since the fault data in the fast degradation stage is smaller than the healthy data in the normal state, the mixture of healthy data and faulty data leads to extremely unbalanced data, which brings great challenges to finding hidden fault information, thus making fault prediction more accurate. Aiming at the above problems, a convolutional transformer model ConvTrans-TPS model for disk failure prediction in large-scale network storage systems is proposed. The ConvTrans-TPS model acquires dependencies between long-term sequence data through transformers and uses convolutional projections for attention computation to enhance attention to local contextual information. data augmentation to predict failures in the next 7 days. Validated by the analysis on the Backblaze dataset, the F1 is 0.96 and the Matthews correlation coefficient (MCC) is 0.92. Compared with the popular CNN-LSTM model in recent years, our proposed method improves F1 and MCC by 4% and 5%, respectively, improving the prediction accuracy.

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FP-WDDQN: An improved deep reinforcement learning algorithm for adaptive traffic signal control

FP-WDDQN: An improved deep reinforcement learning algorithm ...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Xiao Zhang Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

Current adaptive traffic signal control methods based on centralized deep reinforcement learning are not applicable in large-scale adaptive traffic control environment. The scalability problem is overcome by assigning global control to each local RL agent through multi-intelligence reinforcement learning, but the environment now becomes partially visible ami non-stationarity from the perspective of each local agent due to limited communication between agents. In this paper, we propose a multi-agent framework called Forgetful Priority Weighed Double Deep Q-learning (FP-WDDQN). We firstly extend Weighted Double Deep Q-Learning(WDDQN) to the multi agent domain, so as to reduce the error caused by the algorithm's underestimation of the target network and get more accurate Q-value. Then we propose a new algorithm based on Forgetful Experience Mechanism(FEM) and Priority Experience Replay Mechanism(PERM). This mechanism makes WDDQN choose experience based on Temporal Difference (TD) error and Identify the importance of experience with FEM. It makes WDDQN preferentially select experience with high TD-error in the process of sampling and training its network based on FEM and TD-error, which improved the model training efficiency and stabilized the learning process. In this paper, we construct an urban traffic network with seven intersections in the simulation platform called Simulation of Urban MObility(SUMO), and the proposed FP-WDDQN is compared against Independent Double Deep Q-learning(IDDQN), WDDQN and Multi-agent Advantage Actor Critic (MA2C) under the condition of simulating the traffic dynamics of peak hours. Experiment shows FP-WDDQN has better performance than other algorithms in vehicle speed, intersection delay and intersection waiting queue length.

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Lightweight EEG Classification Model Based on EEG-sensor with Few Channels

Lightweight EEG Classification Model Based on EEG-sensor wit...

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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Haoyan Xu Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

In the traditional work, based on EEG Sensors monitoring the physiological and emotional state of humans, the time-frequency analysis method is usually used to extract the characteristics of the signal, and then analyzed by SVM, KNN and other methods. However, the recognition accuracy of such methods is not ideal. Deep learning methods such as CNN and RNN have become the research hotspots in the field of EEG analysis. However, due to the high structural risk of the deep learning model, the model is prone to poor generalization ability, long training time, over-fitting, and poor real-time performance. In addition, current EEG data acquisition devices generally require dozens of signal channels to provide a data foundation for accurate analysis of subsequent brain signals, which not only brings high hardware costs, but also is not portable enough to be popularized in daily life. However, due to the weak and susceptible interference of the EEG signal, reducing the number of signal channels under the premise of using the above method may further lead to a worse analysis effect. In order to solve these problems, we propose a new lightweight EEG classification model based on a small number of channel EEG sensors, called Armaiti. Armaiti first uses the 5-channel EEG signal collected by the portable EEG acquisition device to perform blind source signal separation to obtain data from multiple sources, including noise signals such as EOG. Instead of identifying and screening these signals, we use a combination of EEG, EOG and other signals. Then Armaiti performs wavelet packet transform on each signal source, decomposes the signal into different frequency bands, and then inputs it into the lightweight convolutional neural network model designed in this paper to obtain five classifiers. Finally, Armaiti uses the ensemble learning to get the final classification results. In order to verify the performance and practicability of the model, from September 2018 to June 2019, we use

关键词： Electroencephalography Brain modeling Electrooculography Computational modeling Analytical models Fatigue Feature extraction

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Lgccn: A Label-Guided Contrastive Capsule Network for Few-Shot Text Classification

SSRN

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SSRN 2023年

作者： Xu, Yang Xu, Xiaolong Xiao, Fu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

Few-shot text classification aims to learn transferable knowledge from a limited dataset to perform classification tasks in unseen domains. Recently, metric-based meta-learning methods have demonstrated their advantages in few-shot text classification. However, the influence of randomly sampling support set on prototype quality has often been overlooked by these methods. In this paper, we propose a Label-Guided Contrastive Capsule Network (LGCCN) to address this issue. LGCCN leverages label information to guide supervised contrastive learning, using labels as anchors to enhance the clustering process, resulting in higher-quality sentence vectors. Furthermore, LGCCN employs label-guided dynamic routing process to iteratively generate a prototype, significantly mitigating the impact of random support set sampling. We conducted extensive experiments on four datasets, comparing LGCCN with recent methods. The results reveal that LGCCN, compared to the recent best work, has achieved an improvement of 10.1% and 2.1% in classification accuracy for 1-shot and 5-shot tasks, respectively. Additionally, ablation studies validate the effectiveness of each module in our approach. © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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TSC-ECFA:A Trusted Service Composition Scheme for Edge Cloud

TSC-ECFA:A Trusted Service Composition Scheme for Edge Cloud

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yu Jiang Xiaolong Xu Kunda Lin Weihua Duan Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781665408790

In order to select a composition scheme that meets user's needs and high performance from large-scale web services in the edge cloud, this paper proposes a trusted service composition optimization scheme called TSC-ECFA for edge cloud, which applies the predation strategy to the firefly algorithm (FA) and divides the services with the same input and output into one category, reducing the number of combinations to search for feasible solutions. The cotangent chaos theory is used to generate the initial firefly population and disturb the location of the individual to improve the overall search efficiency of FA. This paper also improves the step factor and attractiveness formula of FA to give full play to the detection ability of the step factor in the early stage of the algorithm, and balance the local search and the global search. Considering that the QoS attribute values of the service are vulnerable to be tampered with, the blockchain is used to store the QoS attribute values to ensure the tamper-proof and reliability of the QoS attribute value. Finally, simulation experiments compare the number of iterations and optimization of the three algorithms. The experimental results show that the comprehensive performance of TSC-ECFA is better than other algorithms.

关键词： Chaos Web services Conferences Sociology Quality of service Reliability theory Blockchains

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RPSBPT: A Route Planning Scheme with Best Profit for Taxi

RPSBPT: A Route Planning Scheme with Best Profit for Taxi

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Yuhua Qiu Xiaolong Xu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728105499;9781728105482

Reasonable route planning for taxi can not only improve quality of customer experience, but also maximize the benefit of taxi drivers. Most current taxi planning schemes are designed to achieve the shortest route or the shortest time, not to achieve the best profit. In this paper, we propose a route planning scheme with best profit for taxi (RPSBPT). First, we define the optimal profit point and the profit per unit time function. Second, we design the workflow of data cleaning, sampling and partitioning for preprocessing the dataset of taxi trajectory. Then, we integrate the DBSCAN algorithm and the K-means algorithm to obtain the optimal profit points. Finally, the simulate anneal Algorithm (SA), the genetic algorithm (GA), and the ant colony optimization algorithm (ACO) are adopted respectively to plan route for taxi. We constructed the taxi route planning prototype system and applied the proposed route planning scheme to the system. Based on the system and the collected taxi trajectory data at the Jinjiang district of the Chengdu city, we performed a series of experiments to compare the performance of three heuristic algorithms, including optimal route length, algorithm stability, total profit and profitability per unit time. Experimental results show that ACO has the best performance.

关键词： Public transportation Planning Trajectory Clustering algorithms Genetic algorithms Urban areas Partitioning algorithms

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Comparison and Analysis on Typical Network Representation Learning Algorithms

Comparison and Analysis on Typical Network Representation Le...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Yang Wang Xiaolong Xu Yun Li Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781538676738;9781538676721

Large-scale complex networks show complex nonlinear relationships among objects, such as the social relationships in the real world, the citation relationship among papers and the interactions among proteins in biology. The analysis of complex network systems make it possible to reveal network structures, information disseminating laws, and communication patterns. Network representation learning (NRL) algorithms focus on mapping the original network structure information to a low-dimensional vector space through a series of operations under the premise of maximally retaining the network structure. In order to analyze current representative NRL algorithms effectively to provide valuable references for other researchers, we built an experimental platform to perform and test the NRL algorithms based on matrix factorization, the NRL algorithms based on shallow neural network and the NRL algorithms based on deep neural network, with datasets on Collaboration Network, Social Network and Citation Network. We implemented a series of comprehensive experiments, based on metrics include precision@k, micro-F1 and macro-F1. Our experiments include network reconstruction, vertex classification, and link prediction, and show readers principles, performances and applications of typical NRL algorithms.

关键词： Neural networks Prediction algorithms Task analysis Matrix decomposition data visualization Blogs Telecommunications

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Model-based Offline Policy Optimization with Adversarial Network

arXiv

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arXiv 2023年

作者： Yang, Junming Chen, Xingguo Wang, Shengyuan Zhang, Bolei Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China School of Computer Science Nanjing University of Posts and Telecommunications China School of Modern Posts Nanjing University of Posts and Telecommunications China

Model-based offline reinforcement learning (RL), which builds a supervised transition model with logging dataset to avoid costly interactions with the online environment, has been a promising approach for offline policy optimization. As the discrepancy between the logging data and online environment may result in a distributional shift problem, many prior works have studied how to build robust transition models conservatively and estimate the model uncertainty accurately. However, the over-conservatism can limit the exploration of the agent, and the uncertainty estimates may be unreliable. In this work, we propose a novel Model-based Offline policy optimization framework with Adversarial Network (MOAN). The key idea is to use adversarial learning to build a transition model with better generalization, where an adversary is introduced to distinguish between in-distribution and out-of-distribution samples. Moreover, the adversary can naturally provide a quantification of the model’s uncertainty with theoretical guarantees. Extensive experiments showed that our approach outperforms existing state-of-the-art baselines on widely studied offline RL benchmarks. It can also generate diverse in-distribution samples, and quantify the uncertainty more accurately. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Joint Spatial-Temporal Trajectory Clustering Method for Mobile Social Networks

Joint Spatial-Temporal Trajectory Clustering Method for Mobi...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Ji Tang Linfeng Liu Jiagao Wu Jian Zhou Yang Xiang School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728125848

As an important issue in the trajectory mining task, the trajectory clustering technique has attracted lots of the attention in the field of data mining. Trajectory clustering technique identifies the similar trajectories (or trajectory segments) and classifies them into the several clusters which can reveal the potential movement behaviors of nodes. At present, most of the existing trajectory clustering methods focus on some spatial properties of trajectories (such as geographic locations, movement directions), while the spatial-temporal properties (especially the combination of spatial distances and semantic distances) are ignored, and thus some vital information regarding the movement behaviors of nodes is probably lost in the trajectory clustering results. In this paper, we propose a Joint Spatial-Temporal Trajectory Clustering Method (JSTTCM), where some spatialtemporal properties of the trajectories are exploited to cluster the trajectory segments. Finally, the number of clusters and the silhouette coefficient are observed through simulations, and the results show that JSTTCM can cluster the trajectory segments appropriately.

关键词： Trajectory clustering Spatial-temporal properties Spatial distances Semantic distances trajectory Social Networks Clustering methods Social Media Ganglia

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