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3rd International Conference on big data and security, ICBDS 2021

作者： Wang, Rui Wang, Xueli Chen, Yunfang Zhang, Wei School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China

ISBN: (纸本)9789811908514

The lightweight target detection model is deployed in an environment with limited computing power and power consumption, which is widely used in many fields. Most of the current lightweight technologies only focus on a few steps of the model implementation and lack a global perspective. Therefore, this paper proposes a general lightweight model implementation framework, including network construction indicators, lightweight backbone network design, and model optimization. By analyzing the complexity indicator of network structure, the factors that affect network performance such as depth and width are summarized. On this basis, combine the One-Shot Aggregation (OSA) idea and Cross-Stage Partial Network (CSPNet) transformation to construct a general lightweight detection network CSPOSA. Further specific optimization strategies are proposed to prune the network structure and training process. For the network structure, the width and depth of the network are adjusted, the amount of parameters of the model is compressed. The training process is divided into the first, middle and last three stages to improve the detection performance of the model without adding extra computation. Taking embedded platform helmet detection as the experimental scene, the parameter amount of the realized model is 1/10 of the mainstream models YOLOv3 and YOLOv4, and the detection accuracy is similar, so it is more suitable for deployment on devices with limited computing power. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Computing power

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Model-Based Offline Policy Optimization with Adversarial Network 26

Model-Based Offline Policy Optimization with Adversarial Net...

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26th European Conference on Artificial Intelligence, ECAI 2023

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

ISBN: (纸本)9781643684369

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. © 2023 The Authors.

关键词： Reinforcement learning

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ConvTrans-TPS: A Convolutional Transformer Model for Disk Failure Prediction in Large-Scale Network Storage Systems 26

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

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xu, Shicheng Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China

ISBN: (纸本)9798350331684

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. © 2023 IEEE.

关键词： Convolution

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Path Planning based on Reinforcement Learning with Improved APF model for Synergistic Multi-UAVs 26

Path Planning based on Reinforcement Learning with Improved ...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Ding, Qun Xu, Xiaolong Gui, Wenming Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Jinling Institute of Technology School of Computer Science Nanjing211169 China

ISBN: (纸本)9798350331684

As the emerging technology of Unmanned Aerial Vehicles (UAVs) becomes mature, UAVs are widely used in environmental monitoring, communication and other fields. In view of this, this paper analyzed the task of synergistic multi-UAVs exploration of unknown environments, and proposed a path planning method for them based on reinforcement learning. Firstly, the path planning task of the UAVs was divided into two parts: the path travel strategy module and the information exploration strategy module. Models of the two modules were based on the Deep Deterministic Policy Gradient algorithm (DDPG), and an improved Artificial Potential Field (APF) force traction mechanism was introduced in the path travel strategy module. Its aim was to assist in guiding the generation of UAV flight path trajectories. Also it could enhance the learning capability of the model. The path travel strategy module would generate the complete flight path of the whole cluster in a distributed manner. A series of temporary target points provided by the information exploration strategy module helped. In maps with 21.5%, 25.3% and 29.6% of obstacles, multi-UAVs could achieve 84.2%, 76.7% and 69.9% of environmental exploration by the designed method. Compared with the APF method, the A star method and the Breath First Search (BFS) method, the proposed method is not only able to plan feasible paths in a more complex map model, but also the curvature of the planned paths is smoother, thus achieving the goal of reducing the energy cost of UAVs. © 2023 IEEE.

关键词： Reinforcement learning

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Indoor Navigation Mechanism based on 5G network for Large Parking Garage 26

Indoor Navigation Mechanism based on 5G network for Large Pa...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xu, Xiaolong Ding, Qun Lin, Licheng Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9798350331684

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. © 2023 IEEE.

关键词： 5G mobile communication systems

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A Privacy Leak Detection Mechanism based on Service Binding 10

A Privacy Leak Detection Mechanism based on Service Binding

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10th International Symposium on System security, Safety, and Reliability, ISSSR 2024

作者： Wang, Boyang He, Jinling Du, Yuanhan Tang, Ming Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China

ISBN: (纸本)9798350362930

With the development of mobile technology, users gradually store financial information and personal privacy data on mobile terminals, which makes mobile security particularly important. In order to effectively improve the detection speed of application privacy leaks, this paper proposes a concept of service binding, binding a minimum privacy permission set for each service provided by the application. Use the service binding concept to make the static analysis phase track only the sensitive data flow paths between APIs that apply the service's privacy permissions. In order to make up for the lack of static analysis to predict the behavior of application privacy leakage, this paper proposes a static analysis that effectively combines machine learning algorithms, abstracts the path characteristics of the static data streams obtained from static analysis into feature vectors, and further uses machine learning algorithms for learning classification. The experimental demonstration and performance analysis results show that the privacy leak detection mechanism based on service binding has faster detection speed and higher accuracy than FlowDroid. © 2024 IEEE.

关键词： Static analysis

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FBOD: An Outlier Detection Algorithm based on data Features Suitable for processing Large-scale datasets on Distributed Platforms 10

FBOD: An Outlier Detection Algorithm based on Data Features ...

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10th International Symposium on System security, Safety, and Reliability, ISSSR 2024

作者： Zhao, Jinbo He, Jinling Song, Hu Cheng, Xinyun Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China

ISBN: (纸本)9798350362930

The primary objective of outlier detection is to identify values that are significantly different from other data in the dataset. However, most of the current algorithms are effective for small-scale data and their performance is highly dependent on the choice of parameters. To this, the paper proposes an innovative outlier detection algorithm, named FBOD, which is based on data features. Specifically, FBOD utilizes the number of combinations of attribute values in each tuple across the dataset as a key feature of the tuple. This feature reflects the degree of differentiation of each data tuple from the overall dataset. By identifying data features that are significantly different from objects in the entire dataset, FBOD can accurately determine outliers. Based on the knowledge of attribute importance in Rough Set Theory, FBOD can efficiently reduce the data dimensionality, so high-dimensional datasets can be handled by FBOD. Furthermore, this paper employs a distributed parallelism approach in the algorithm. The performance of FBOD is evaluated on several datasets, including standard datasets and artificial datasets, and is compared with common outlier detection algorithms. Experimental results indicate that, compared to existing typical algorithms, FBOD achieves higher outlier detection accuracy without relying on a prior knowledge of datasets or parameter selection. It also proves the suitability of FBOD for large-scale dataset processing on distributed platforms. © 2024 IEEE.

关键词： Anomaly detection

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Identifying Outliers in Astronomical Images with Unsupervised Machine Learning

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Research in Astronomy and Astrophysics 2022年第8期22卷 74-84页

作者： Yang Han Zhiqiang Zou Nan Li Yanli Chen School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210023China Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

Astronomical outliers,such as unusual,rare or unknown types of astronomical objects or phenomena,constantly lead to the discovery of genuinely unforeseen knowledge in *** unpredictable outliers will be uncovered in principle with the increment of the coverage and quality of upcoming survey ***,it is a severe challenge to mine rare and unexpected targets from enormous data with human inspection due to a significant *** learning is also unsuitable for this purpose because designing proper training sets for unanticipated signals is *** by these challenges,we adopt unsupervised machine learning approaches to identify outliers in the data of galaxy images to explore the paths for detecting astronomical *** comparison,we construct three methods,which are built upon the k-nearest neighbors(KNN),Convolutional Auto-Encoder(CAE)+KNN,and CAE+KNN+Attention Mechanism(att CAE_KNN)*** sets are created based on the Galaxy Zoo image data published online to evaluate the performance of the above *** show that att CAE_KNN achieves the best recall(78%),which is 53%higher than the classical KNN method and 22%higher than CAE+*** efficiency of att CAE_KNN(10 minutes)is also superior to KNN(4 h)and equal to CAE+KNN(10 minutes)for accomplishing the same ***,we believe that it is feasible to detect astronomical outliers in the data of galaxy images in an unsupervised ***,we will apply att CAE_KNN to available survey data sets to assess its applicability and reliability.

关键词： Galaxy Physical data and Processes Galaxy:fundamental parameters

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A Lexical Updating Algorithm for Sentiment Analysis on Chinese Movie Reviews 5

A Lexical Updating Algorithm for Sentiment Analysis on Chine...

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5th International Conference on Advanced Cloud and big data, CBD 2017

作者： Song, Yiwei Gu, Kaiwen Li, Huakang Sun, Guozi Bell Honor School Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (纸本)9781538610725

With the prevalence of Internet, sentiment analysis gets popularity among the world. Researchers have made use of kinds of online documents like commodities reivews and movie reviews as training samples to train their models and classfiers, by which they could speculate the underlying emotion in new ones. Douban is a Chinese online community where users share their personal reviews to express their feelings about movies. Those Chinese movie reviews were utilized by us to train our lexicon-based model. Yet multiple words in a ready-made lexicon do not agree with the movie reviews in a specific domain, which means the original lexicon acquires being updated to gain higher accuracy. In this paper we introduce a lexical updating algorithm based on a widely used lexicon. After turns of training of updating, this lexicon is capable of classifying sentiment among movie reviews. The experimental result shows our model using the updated lexicon could get a better performance than the primitive lexicon-based model. © 2017 IEEE.

关键词： Sentiment analysis

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Visco-acoustic Wave-equation Traveltime Inversion with Correct and Incorrect Attenuation Profiles 87

Visco-acoustic Wave-equation Traveltime Inversion with Corre...

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Society of Exploration Geophysicists International Exposition and 87th Annual Meeting, SEG 2017

作者： Yu, Han Chen, Yuqing Guo, Bowen Schuster, Gerard T. Division of Physical Sciences and Engineering King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Jiangsu Key Lab of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210003 China

A visco-acoustic wave-equation traveltime inversion method is presented that inverts for a shallow subsurface velocity distribution with correct and incorrect attenuation profiles. Similar to the classical wave equation traveltime inversion, this method applies the misfit functional that minimizes the first break differences between the observed and predicted data. Although, WT can partly avoid the cycle skipping problem, an initial velocity model approaches to the right or wrong velocity models under different setups of the attenuation profiles. However, with a Q model far away from the real model, the inverted tomogram is obviously different from the true velocity model while a small change of the Q model does not improve the inversion quality in a strong manner if low frequency information is not lost.. © 2017 SEG.

关键词： Wave equations

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