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IEEE International Conference on data Mining (ICDM)

作者： Yue Liu Yun Li Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

As an important stage in machine learning pipeline, feature selection techniques are mainly used to improve the generalization performance and training efficiency of machine learning model, but few works have focused on the robustness of machine learning models from the perspective of feature selection when dealing with adversarial attacks. In this paper, we propose the adversarial training (AT) based feature selection framework, i.e. AT based feature selection, to improve the robustness of machine learning model built on the feature selection result, which is inspired by using adversarial training to improve the robustness of deep learning model. AT based feature selection framework is the combination of adversarial training with some traditional feature selection algorithm, which can be divided into AT in-processing and AT post-processing feature selection. On the other hand, stability is also a very important property for feature selection. Then we experimentally analyze the relationship between robustness and stability of AT based feature selection, especially theoretically analyze the stability of $\ell_processing$ regularized AT in-processing feature selection algorithm in two different adversarial training forms. Our experimental results on benchmark data sets show that AT based feature selection algorithm is effective to improve the robustness of machine learning model, however, obtain lower stability than corresponding feature selection model without AT.

关键词： Training Deep learning Machine learning algorithms Pipelines Benchmark testing Feature extraction Stability analysis

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An optimal profit route planning scheme based on ACO-OPP 21

An optimal profit route planning scheme based on ACO-OPP

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Qiu, Yuhua Xu, Xiaolong Xia, Fei 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 Information and communication Branch State Grid Jiangsu Electric Power Company Nanjing China

ISBN: (纸本)9781728120584

Taxi plays an important role of urban public transportation system. However, without appropriate route planning scheme, taxi drivers can only choose to wait or seek passengers in the absence of orders, leading to wasting a lot of time and fuel. In this paper, an optimal profit route planning scheme for taxi drivers with Ant Colony Optimization Algorithm based on Optimal Profit Points (ACO-OPP) is proposed. First, the taxi trajectory data are preprocessed and the pick-up and drop-off points are extracted to obtain high-quality data;second, the data spatiotemporal distribution is analyzed to plan a more appropriate and rational route;the clustering algorithm is used to obtain the optimal profit points to increase the occupancy rate of taxis;then, ACO-OPP is utilized to plan the profit routes for each taxi. A series of experiments are implemented based on the real taxi trajectory dataset to test and compare the performance of the simulated annealing algorithm, the genetic algorithm, the ant colony algorithm and ACO-OPP on the optimal route length and the profitability with the unit time profit function. The experimental results show that ACO-OPP has the shortest optimal route length and the best profit per unit time. © 2019 IEEE.

关键词： Profitability

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SNNLog: A Log Parsing Scheme with Siamese Network and Fixed Depth Tree in Networks

SNNLog: A Log Parsing Scheme with Siamese Network and Fixed ...

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International Conference on Computer Supported Cooperative Work in Design

作者： Lei Sun Xiaolong Xu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

Syslog records critical information of network when the system is running, and has been used to help practitioners carry out various network maintenance and operation activities. Because of abundance of syslog, automated log analysis technology is needed to complete the above activities. Usually, log parsing is an important part of log analysis. Previous log parsing methods have good achievements, but they heavily rely on well-designed regular expressions and ignore semantic information of log. To solve these problems, we propose a novel log parsing method SNNLog, which uses Siamese Network to assess the similarity between log messages, and the similarity is used for the subsequent parsing. In addition, after parsing, SNNLog merges log similar events, reducing the misjudgment rate caused by non-numeric token variables at the beginning of messages. We evaluated on five publicly accessible log datasets and the results show that compared with SOTA algorithms such as LogMine, Spell, Drain and MoLFI, SNNLog has an F1-score of 0.999 on five datasets, and parsing accuracy of four datasets is the highest.

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Modeling Disease Progression via Weakly Supervised Temporal Multitask Matrix Completion

Modeling Disease Progression via Weakly Supervised Temporal ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Lingsheng Wang Lei Xu Ping Li Siming Zha Lei Chen Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. Understanding AD progression can empower the patients in taking proactive care. Mini Mental State Examination (MMSE) and AD Assessment Scale Cognitive subscale (ADAS-Cog) are two prevailing clinical measures designed to evaluate the AD progression. In this paper, we propose a weakly supervised Temporal Multitask Matrix Completion (TMMC) framework, which combines a novel transductive multitask feature selection scheme, to simultaneously predict AD progression measured by MMSE and ADAS-Cog, and identify related biomarkers trackable of AD progression. Specifically, by treating the prediction of cognitive scores at each time point as a regression task, we first formulate AD progression problem as a standard Multitask Matrix Completion (MMC) model. Secondly, considering the limited number of samples available in this study, we introduce a transductive feature selection scheme to jointly select the task-shared features for multiple time points and the task-specific features for different time points, and thus alleviate the over-fitting defect caused by Small-Sample-Size issue. Thirdly, aiming at the small change of cognitive scores between successive time points for a patient, we employ a temporal regularization scheme to capture the temporal smoothness of cognitive scores. Furthermore, we design an efficient optimization algorithm based on Alternative Minimization and Difference of Convex Programming techniques to solve the proposed TMMC framework. Finally, the extensive experiments performed on real-world Alzheimer's disease dataset demonstrate the effectiveness of our TMMC framework.

关键词： Predictive models Biomarkers Programming Feature extraction Task analysis Standards Diseases

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A Network Fault Prediction-Based Service Migration Approach for Unstable Mobile Edge Environment

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Wang, Haiyan Tang, Weihao Luo, Jian Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

How to perform efficient service migration in a mobile edge environment has become one of the research hotspots in the field of service computing. Most service migration approaches assume that the mobile edge network on which the migration depends is stable. However, in practice, these networks often fluctuate greatly due to the fault of edge devices, resulting in unexpected service interruptions during the migration process. Besides, most of the existing solutions do not consider the migration cost and path selection in the event of edge network fault. Aiming at the above problems, we propose a service migration approach based on network fault prediction (SMNFP) for mobile edge environment. The SMNFP method first introduces the software-defined network as a global controller, which is used to monitor and collect the changing of the edge network and schedule the migration tasks. Second, a network fault prediction model based on Wide&Deep model is proposed to predict the upcoming faults in the network according to the alarm information of network equipment. Finally, the service migration problem is constructed as a Markov decision process, and a fault penalty function is introduced to avoid faulty nodes, together with the deep Q-learning method to solve the migration strategy. Simulation experiments are conducted on the public metro network fault dataset, and results show the proposed method can effectively predict network faults and complete service migration. Copyright © 2023 Haiyan Wang et al.

关键词： Markov processes

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Latent Group Recommendation based on Double Fuzzy Clustering and Matrix Tri-factorization

Latent Group Recommendation based on Double Fuzzy Clustering...

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IEEE International Conference on Web Services (ICWS)

作者： Haiyan Wang Jinxia Zhu Zhousheng Wang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

Group recommendation has received great attention owing to its practical value in real applications. However, group members are implicit and groups are formed occasionally in some scenarios. Existing solutions for latent group recommendation assumes a user belongs to a specific group, and totally ignore the possible correlation between the user' s preferences and other groups' preferences. In addition, existing methods cannot deal with new items cold-start problem effectively because they only focus on which items are favored by the group without considering the hidden related information between items. These weaknesses usually lead to poor performance of latent group recommendation. To address the problems above, this paper proposes a latent group recommendation method based on double fuzzy clustering and matrix tri-factorization (DFCMTF -LGR). Firstly, this method utilizes unsupervised learning to implement potential feature extraction and double fuzzy clustering for users and items. Secondly, a novel matrix tri-factorization method is presented to adjust the membership of user-to-group, item-to-item category, and the incidence of group-to-item category is obtained. Finally, latent groups are detected according to user-to-group membership, and group rating can be generated in accordance with group-to-item category incidence matrix and item-to-item category membership. Experimental results on real datasets demonstrate that our proposed DFCMTF-LGR has better performance compared with state-of-the art methods.

关键词： Correlation Art Web services Conferences Feature extraction Real-time systems Unsupervised learning

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Multi-Granularity Power Prediction for data Center Operations via Long Short-Term Memory Network

Multi-Granularity Power Prediction for Data Center Operation...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Ziyu Shen Xusheng Zhang Bin Xia Zheng Liu Yun Li Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

The increasing numbers of the applications and requirement of cloud computing have made huge power consumption in data centers, which brings the problems of the high cost and resource waste. This problem attracts significant attention from academia and industry. A critical approach to solve this problem is constructing an intelligent energy management system for data centers. Furthermore, an efficient assessment and prediction module of power consumption in data centers is an essential part of the management system. It facilitates cloud service providers to perform workflow scheduling at the minimal cost and energy efficiency management with the requirement of QoS. Since the assessment and prediction of power consumption correlate, this paper presents a multi-granularity approach for power consumption prediction in data centers, which combines multi-task learning with the LSTM network. We first transfer a multi-granularity power prediction problem into a multi-task regression problem to assess and predict the power consumption of data center system maintenance and scheduling operations. Due to the time requirement for workflow and container scheduling, the prediction interval is 30 seconds. Then we propose an efficient long short-term memory network for the multigranularity prediction. The experimental results show our model outperforms other prediction models on the real datasets.

关键词： Power demand data centers Task analysis Predictive models data models Time series analysis Servers

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Economical Electricity Supplement for Mobile Collectors in Large-Scale Multitask WSNs

Economical Electricity Supplement for Mobile Collectors in L...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Xin Zhai Lijie Xu Jialei Zhang Kun Wang Jia Xu Bei Xu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

For large-scale multitask wireless sensor networks (LSM-WSNs), the traditional data collection mode could suffer low energy-efficiency on data transmission, since the large-scale multitask scenarios could result in much higher packet collision probability, especially for harsh environments. Mobile data collection is an efficient data acquisition way to prolong network lifetime for LSM-WSNs. However, the mobile collectors could suffer electricity shortage problem, since the limited battery capacity of any mobile collector could not afford the energy consumption of its long-distance movement and massive data collection in large-scale multitask scenarios. Deploying wireless chargers to supplement the energy of mobile collectors is a feasible solution to electricity shortage problem, but will incur extra charger deployment cost. In this paper, we focus on the problem that how to optimize such charger deployment cost, which is NP-hard. By transforming it into minimum-cost submodular cover problem, we devise an efficient approximation algorithm with a provable approximation ratio. The extensive simulation results reveal that our solution always outperforms the other solutions under whatever configurations.

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An Answer Summarization Scheme Based on Multilayer Attention Model

An Answer Summarization Scheme Based on Multilayer Attention...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xiaolong Xu Yihao Dong Jian Song Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

At present, deep learning technologies have been widely used in the field of natural language process, such as text summarization. In CQA, the answer summary could help users get a complete answer quickly. There are still some problems with the current answer summary scheme, such as semantic inconsistency, repetition of words, etc. In order to solve this, we propose a novel scheme Answer Summarization based on Multi-layer Attention Scheme (ASMAM). Based on the traditional Seq2Seq, we introduce self-attention and multi-head attention scheme respectively during sentence and text encoding, which could improve text representation ability of the model. In order to solve "long distance dependence" of RNN and too many parameters of LSTM, we all use GRU as the neuron at the encoder and decoder sides. Experiments over the Yahoo! Answers dataset demonstrate that the coherence and fluency of the generated summary are all superior to the benchmark model in ROUGE evaluation system.

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SSR-TA: Sequence to Sequence based expert recurrent recommendation for ticket automation

arXiv

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arXiv 2023年

作者： Cao, Chenhan Fang, Xiaoyu Luo, Bingqing Xia, Bin Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

The ticket automation provides crucial support for the normal operation of IT software systems. An essential task of ticket automation is to assign experts to solve upcoming tickets. However, facing thousands of tickets, inappropriate assignments will make tickets transfer frequently among experts, which causes time delays and wasted resources. Effectively and efficiently finding an appropriate expert in fewer steps is vital to ticket automation. In this paper, we proposed a sequence to sequence based translation model combined with a recurrent recommendation network to recommend appropriate experts for tickets. The sequence to sequence model transforms the ticket description into the corresponding resolution for capturing the potential and useful features of representing tickets. The recurrent recommendation network recommends the appropriate expert based on the assumption that the previous expert in the recommendation sequence cannot solve the expert. To evaluate the performance, we conducted experiments to compare several baselines with SSR-TA on two real-world datasets, and the experimental results show that our proposed model outperforms the baselines. The comparative experiment results also show that SSR-TA has a better performance of expert recommendations for user-generated tickets. Copyright © 2023, The Authors. All rights reserved.

关键词： Automation

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