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International Conference on computer Supported Cooperative Work in Design

作者： Xiaolong Xu Qun Ding Licheng Lin School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

With the increase of car ownership in China, the problem of difficult parking in cities has become more and more serious. In large parking garage, finding an ideal parking space has become a daily problem for people; especially during the peak usage period of parking garage, a large number of vehicles will be driven into the parking garage, which will bring congestion, and at the same time, the demand of a large number of users in the area will lead to network congestion under 4G network, and users cannot access the network to check and find parking spaces. Therefore, this paper proposes a global optimal navigation mechanism with 5G network based on the above mentioned problems by analyzing the congestion situation during the peak usage of the parking garage. This mechanism uses an optimal parking space selection model and a global optimal scheduling model that can avoid congestion. Based on the model proposed in this paper, we construct a navigation planning system based on mobile, front-end and back-end. With the ultra-low latency, ultrahigh transmission efficiency and reliability of 5G network, the mobile port can select the optimal parking space according to the user's preference, and the back-end can show the parking space and congestion in the parking garage in real time according to the actual situation of the parking garage. Finally, the performance of the model and system proposed in this paper is verified through experiments.

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Impact of Feature Selection Methods on data Classification for IDS

Impact of Feature Selection Methods on Data Classification f...

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

作者： Shuai Jiang 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

The rapid increase of data has led to many security issues. Various new technologies and other sub-disciplines have been introduced into the research of intrusion detection system (IDS), which has become a hot topic in the current field of security. However, IDS utilizing traditional machine learning technology suffers from relatively low detection rate, large computational overhead, and high false positive rate, due to the large amount of redundancy and high correlation of network traffic data. Therefore, we select random forest (RF) to handle the classification of the network traffic data, and try the classic feature selection algorithms Extra-Trees (ET) and Chi-square (CHI) to reduce the detection time and improve the efficiency of IDS. The experimental results based on the NSL-KDD dataset indicate that ET-RF and CHI-RF outstand in accuracy and computational overhead. Meanwhile, RF with ET outperforms that with CHI.

关键词： Feature extraction Classification algorithms Intrusion detection Telecommunication traffic Support vector machines Random forests

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Network data Classification Mechanism for Intrusion Detection System

Network Data Classification Mechanism for Intrusion Detectio...

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International Conference on computer Supported Cooperative Work in Design

Intrusion detection system (IDS), as a network security device, monitors network data in real time and responds actively when it detects suspicious transmissions. However, suffered from the large amount of redundancy and high correlation of network data, the traditional IDS have defects in low detection rate and high computational overhead. In this paper, we propose a network data classification mechanism (CPEL). data preprocessing of network traffic data is first performed using correlation-based feature selection (CFS) and principal component analysis (PCA). CFS selects the best features in the data, PCA to dimensionality reduction and denoising. Then, we use multiple classifiers to perform anomaly detection, and select the best three classifiers according to the classification effect. On this basis, we use the majority vote as the ensemble learning (EL) method of combination rules to further improve the performance of the classifier. Experimental results on the benchmark NSL-KDD and WSN-DS datasets indicate that this mechanism outperforms a single algorithm in accuracy and detection rate. In the meantime, computational overhead is remarkably reduced.

关键词： Dimensionality reduction Wireless sensor networks Redundancy Intrusion detection Telecommunication traffic Feature extraction Real-time systems

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HERMES: Pedestrian Real-Time Offline Positioning and Moving Trajectory Tracking System Based on MEMS Sensors

HERMES: Pedestrian Real-Time Offline Positioning and Moving ...

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

作者： Xinxin Liu 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

With the development of positioning technology, sensor-based positioning has become the most commonly used positioning method because it is more low-cost and efficient with the popularity of smart phones. In this paper, we construct a pedestrian real-time offline positioning and moving trajectory tracking system (HERMES) which only relies on the sensors inside smart phones. In HERMES, based on Pedestrian Dead Reckoning (PDR) model, a stable and accurate pedestrian gait recognition is achieved through the rare double sliding window, the pedestrian step length is estimated in real time by a training pedestrian step length model, and the pedestrian real-time course angle is available through orientation sensor inside smart phones. Thus, if given a pedestrian's initial position, the system can implement the real-time offline pedestrian positioning and moving trajectory tracking. Experiments show the pedestrian gait recognition algorithm implemented in this paper has a high accuracy and the positioning effect is satisfactory. The moving trajectory tracked by the HERMES has a small deviation from the experimenter's actual trajectory.

关键词： Acceleration Real-time systems Gait recognition Smart phones Estimation Trajectory tracking Legged locomotion

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BLRGCN: A dynamic traffic flow prediction model based on spatiotemporal graph convolutional network

BLRGCN: A dynamic traffic flow prediction model based on spa...

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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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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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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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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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