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IEEE Conference on Wireless Communications and Networking

作者： Youpeng Wang Xiaojun Zhu Lijie Xu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications China

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

We consider using UAVs to provide location service to ground targets. UAVs fly over the target area and broadcast their locations periodically via wireless signals. When a UAV flies along a straight line, a target will observe the strongest signal when the UAV is at the closest location, based on which it can infer its location. The problem is to minimize the flight length subject to the constraint that the transmission range of a UAV is limited and all targets in the area should be located. We formulate the problem and give an optimal flight path to cover a square with side length no greater than a certain value. In the general case when the given area cannot be covered by the square, we propose to divide UAVs into two groups, whose flight paths are either parallel or orthogonal. We conduct simulations to compare our approach with existing approaches. Results verify the superiority of our approach in terms of flight length and localization error.

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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

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

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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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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A Variance Minimization Approach to Temporal-Difference Learning

arXiv

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arXiv 2024年

作者： Chen, Xingguo Gong, Yu Yang, Shangdong Wang, Wenhao Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China College of Electronic Engineering National University of Defense Technology China

Fast-converging algorithms are a contemporary requirement in reinforcement learning. In the context of linear function approximation, the magnitude of the smallest eigenvalue of the key matrix is a major factor reflecting the convergence speed. Traditional value-based RL algorithms focus on minimizing errors. This paper introduces a variance minimization (VM) approach for value-based RL instead of error minimization. Based on this approach, we proposed two objectives, the Variance of Bellman Error (VBE) and the Variance of Projected Bellman Error (VPBE), and derived the VMTD, VMTDC, and VMETD algorithms. We provided proofs of their convergence and optimal policy invariance of the variance minimization. Experimental studies validate the effectiveness of the proposed algorithms. © 2024, CC BY-SA.

关键词： Contrastive Learning

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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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Multi-agent game abstraction via graph attention neural network

arXiv

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arXiv 2019年

作者： Liu, Yong Wang, Weixun Hu, Yujing Hao, Jianye Chen, Xingguo Gao, Yang National Key Laboratory for Novel Software Technology Nanjing University Tianjin University NetEase Fuxi AI Lab Noah's Ark Lab Huawei Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications

In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process is an important research issue. In many multi-agent systems, the interactions between agents often happen locally, which means that agents neither need to coordinate with all other agents nor need to coordinate with others all the time. Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents. However, the methods cannot be directly used in a large-scale environment due to the difficulty of transforming the complex interactions between agents into rules. In this paper, we model the relationship between agents by a complete graph and propose a novel game abstraction mechanism based on two-stage attention network (G2ANet), which can indicate whether there is an interaction between two agents and the importance of the interaction. We integrate this detection mechanism into graph neural network-based multi-agent reinforcement learning for conducting game abstraction and propose two novel learning algorithms GA-Comm and GA-AC. We conduct experiments in Traffic Junction and Predator-Prey. The results indicate that the proposed methods can simplify the learning process and meanwhile get better asymptotic performance compared with state-of-the-art algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Genetic algorithms

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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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