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arXiv 2022年

作者： Jiang, Yuezihan Cheng, Yu Zhao, Hanyu Zhang, Wentao Miao, Xupeng He, Yu Wang, Liang Yang, Zhi Cui, Bin School of Software and Microelectronics Peking University China School of CS Key Laboratory of High Confidence Software Technologies Peking University China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China Alibaba Group China

We introduce ZOOMER, a system deployed at Taobao, the largest e-commerce platform in China, for training and serving GNN-based recommendations over web-scale graphs. ZOOMER is designed for tackling two challenges presented by the massive user data at Taobao: low training/serving efficiency due to the huge scale of the graphs, and low recommendation quality due to the information overload which distracts the recommendation model from specific user intentions. ZOOMER achieves this by introducing a key concept, Region of Interests (ROI) in GNNs for recommendations, i.e., a neighborhood region in the graph with significant relevance to a strong user intention. ZOOMER narrows the focus from the whole graph and "zooms in" on the more relevant ROIs, thereby reducing the training/serving cost and mitigating the information overload at the same time. With carefully designed mechanisms, ZOOMER identifies the interest expressed by each recommendation request, constructs an ROI subgraph by sampling with respect to the interest, and guides the GNN to reweigh different parts of the ROI towards the interest by a multi-level attention module. Deployed as a large-scale distributed system, ZOOMER supports graphs with billions of nodes for training and thousands of requests per second for serving. ZOOMER achieves up to 14x speedup when downsizing sampling scales with comparable (even better) AUC performance than baseline methods. Besides, both the offline evaluation and online A/B test demonstrate the effectiveness of ZOOMER. Copyright © 2022, The Authors. All rights reserved.

关键词： Graphic methods

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An Energy-Aware UAVs Path Coverage for Critical Infrastructure Inspections

An Energy-Aware UAVs Path Coverage for Critical Infrastructu...

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International Conference on Advanced Cloud and big data (CBD)

作者： Yifan Wang Wei Zeng Guanyu Li Chenglong Xiong Zicheng Wang Yingchi Mao College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China Huaneng Lancang River Hydropower Co. CHINA HUANENG Group Kunming China Power China Kunming Engineering Co. Yunnan China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Critical infrastructures increasingly rely on unmanned aerial vehicles (UAVs) for inspection tasks. The significance of different components within these infrastructures is subject to variability and can be influenced by external factors. The principal aim of routine UAV inspections is to ensure differentiated coverage of pivotal sections with minimal energy consumption. However, extant research on UAV path coverage fails to fully account for the variability and dynamic shifts in regional significance and their impact on coverage efficacy. This paper presents an energy-aware collaborative coverage policy for UAVs, designated as ***-MATD3 employs a dynamic weight region partitioning method tailored to real-world environments and addresses the action selection challenge for UAVs using a discrete Partially Observable Markov Decision Process (Dec-POMDP). By amalgamating MATD3 with stacked LSTM, this approach mitigates redundant path overlaps and unnecessary action replication across multiple agents, thus optimizing coverage and diminishing energy usage. Simulation outcomes demonstrate that EA-MATD3 reduces energy consumption by an average of 9.65% relative to the Greedy, MADDPG, and MATD3 algorithms while sustaining a superior coverage rate.

关键词： Energy consumption Simulation Markov decision processes Termination of employment Inspection Autonomous aerial vehicles Critical infrastructure Vehicle dynamics Long short term memory Optimization

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Short-term Traffic Flow Prediction Based on Improved Deep Echo State Network

Short-term Traffic Flow Prediction Based on Improved Deep Ec...

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第40届中国控制会议

作者： Changwu Li Xiao Ren Qingyong Zhang Huiwen Xia Jiahua Chen Yutong Gao School of Automation Wuhan University of Technology School of Big Data & Software Engineering Chongqing University

In order to improve the processing capability of deep echo networks for short-time traffic flow prediction problems,an improved deep echo state network（IDESN） is proposed in this *** improved deep echo state network algorithm firstly improves the activation function in the traditional echo state network and uses a particle swarm algorithm to optimize the parameters in the new activation ***,the circular greedy algorithm is used to find the hyperparameters of the improved deep echo state ***,the wavelet threshold denoising algorithm is used to denoise the traffic flow *** this paper,three short-term traffic flow datasets are used for *** results show that the MSE values of the three datasets are reduced by 57.13%,57.80% and 51.59%,respectively,compared with the original deep echo state network as well as the improved deep echo state network has higher accuracy.

关键词： Short-term traffic flow prediction Deep echo state network Cyclic greedy algorithm Activation function Wavelet threshold denoising algorithm

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Pointsurface: Discriminative Point Cloud Surface Feature Extraction for 3d Face Recognition

SSRN

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SSRN 2024年

作者： Yang, Junpeng Li, Qiufu Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Guangdong Shenzhen518172 China

3D face recognition performs better than 2D face recognition, in terms of robustness against lighting and digital attacks. Compared to 2D data, the rich geometric information in 3D data could be very useful to improve the performance of 3D face recognition. This paper introduces PointSurFace, a surface feature extraction integrated model tailored for point cloud based 3D face recognition. PointSurFace adopts a specially designed surface feature extraction module, which explores information about the position and normals of the 3D face surface to explicitly extract local discriminative features from 3D point cloud faces. Subsequently, it alternates between set abstraction (SA) layers and Inverted Residual MLP (InvResMLP) modules to effectively extract features from the point cloud data. To better handle the spatial relationships in the point cloud data, PointSurFace integrates coordinate and feature data within each SA layer and InvResMLP module. It also applies channel fusion operations to align these different types of data, thereby more effectively extracts point cloud features. Experimental results in 3D facial recognition and verification across multiple datasets demonstrate that PointSurFace achieves the best 3D face recognition performance to date. For example, on Lock3DFace, it achieves the state-of-the-art recognition and verification accuracy of 90.03% and 80.01%, respectively, surpassing previous methods by 2.02% and 3.19%. Results on cross-quality datasets also affirm PointSurFace’s strong generalization capabilities. Ablation experiments validate the effectiveness of each module within PointSurFace, which suggests that the proposed surface feature extraction module significantly enhances the point cloud based 3D facial recognition performance. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Adapting Site Selection of Sewage Treatment Plant to Urban Planning: A Hesitant Fuzzy Linguistic Mcdm Model

SSRN

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SSRN 2022年

作者： Liu, Jun Xie, Mingyue Lin, Mingwei Chen, Shuyu School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou350117 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China

The sewage treatment plant is the basic facility of urban construction. However, selecting an appropriate site to construct a sewage treatment plant is challenging task. Therefore, this paper puts forward a novel hesitant fuzzy linguistic multi-criteria decision-making (HFLMCDM) model for the sewage treatment plants siting decision. For this purpose, the hesitant fuzzy linguistic term set (HFLTS) is introduced to accurately evaluate the alternative sites. Then, a novel score function based on trigonometric semantic value (Score-TSV) and distance measure based on trigonometric semantic value and dispersion (Dis-TSVD) are proposed for HFLTSs. A hesitant fuzzy linguistic VIKOR (HFLV) method combining the novel Score-TSV and Dis-TSVD is utilized to rank the alternative sites for sewage treatment plants. The proposed HFLMCDM model is applied to determine the best site for a sewage treatment plant in Chongqing. Ultimately, the superiority of the HFLMCDM model is elaborated by comparative analysis. © 2022, The Authors. All rights reserved.

关键词： Site selection

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Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning

arXiv

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arXiv 2024年

作者： Wang, Tianfu Shen, Li Fan, Qilin Xu, Tong Liu, Tongliang Xiong, Hui Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science University of Science and Technology of China Hefei230027 China The School of Big Data and Software Engineering Chongqing University ChinaScience Chongqing401331 China JD Explore Academy The UBTECH Sydney Artificial Intelligence Centre The School of Information Technologies Faculty of Engineering and Information Technologies The University of Sydney DarlingtonNSW2008 Australia Thrust of Artificial Intelligence Nansha Guangdong Guangzhou511400 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

As an essential resource management problem in network virtualization, virtual network embedding (VNE) aims to allocate the finite resources of physical network to sequentially arriving virtual network requests (VNRs) with different resource demands. Since this is an NP-hard combinatorial optimization problem, many efforts have been made to provide viable solutions. However, most existing approaches have either ignored the admission control of VNRs, which has a potential impact on long-term performances, or not fully exploited the temporal and topological features of the physical network and VNRs. In this paper, we propose a deep Hierarchical Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for VNE, named HRL-ACRA. Specifically, the whole VNE process is decomposed into an upper-level policy for deciding whether to admit the arriving VNR or not and a lower-level policy for allocating resources of the physical network to meet the requirement of VNR through the HRL approach. Considering the proximal policy optimization as the basic training algorithm, we also adopt the average reward method to address the infinite horizon problem of the upper-level agent and design a customized multi-objective intrinsic reward to alleviate the sparse reward issue of the lower-level agent. Moreover, we develop a deep feature-aware graph neural network to capture the features of VNR and physical network and exploit a sequence-to-sequence model to generate embedding actions iteratively. Finally, extensive experiments are conducted in various settings, and show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue. Our code is available at https://***/GeminiLight/hrl-acra. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Research on Malicious software Detection Based on CNN-LSTM Hybrid Model

Research on Malicious Software Detection Based on CNN-LSTM H...

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International Conference on Machine Learning, big data and Business Intelligence (MLBDBI)

作者： Xiaoxia Qi Md Gapar Md Johar Ali Khatibi Jacquline Tham Rui Zhang Sijie Mao School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China Software Engineering and Digital Innovation Centre Management and Science University Shah Alam Malaysia School of Graduate Studies Management and Science University Shah Alam Malaysia

The proliferation of malicious applications has increased rapidly, requiring a more effective approach than traditional methods for auditing and matching code. The behavior of malicious software provides valuable information, with the API call sequence, which represents the order in which operations are generated by both malicious and benign software in a sandbox environment, has become a crucial data source. In this paper, we propose a hybrid model that combines convolutional neural network (CNN) and long short-term memory network (LSTM) to detect and classify malware. We train and classify the model using a large dataset of API sequences called by both malicious and non-malicious software. Compared to traditional machine learning methods, our model eliminates the need for manual extraction of specific instruction calls from assembly code. Furthermore, compared to other deep learning models, hybrid model with LSTM shows significantly improved detection performance. Specifically, the CNN-LSTM model achieves a 24% and 22% increase in accuracy and at least a 16% and 14% higher F1 score compared to other models. These results demonstrate the effectiveness of our proposed model in enhancing malware detection capabilities.

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Container Anomaly Detection System Based on Rule Mining and Matching

Container Anomaly Detection System Based on Rule Mining and ...

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2022 International Conference on Blockchain Technology and Information Security, ICBCTIS 2022

作者： Yu, Daisong Xie, Yulai Long, Huachao Jin, Minpeng Li, Xuan School of Cyber Science and Engineering Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan China Wuhan National Lab for Optoelectronics F311 Wuhan China School of Computer Science Huazhong University of Science and Technology Wuhan China Nsfocus Inc. A9-3/F Optical Valley Software Park Wuhan China

ISBN: (数字)9781665496919

ISBN: (纸本)9781665496919

Compared with virtual machine technology, container technology is more and more popular by virtue of its excellent convenience and reliable security isolation. Containers will encounter various anomalies during operation. For maintenance personnel, a large number of abnormal logs makes the work of anomaly analysis very difficult, so anomaly detection technology is needed. In this paper, a container anomaly detection system based on rule mining and matching is proposed, which collects and simplifies the container's logs generated in the abnormal time period, then delivers them to association rule mining algorithm for rule mining and display rules to the maintainer who makes a decision after a simple analysis. The experimental results show that the rule mining algorithm can process log entries in less time consumption compared with traditional association rule mining algorithm, and container anomaly detection system can effectively help system administrator to locate anomalies. © 2022 IEEE.

关键词： Anomaly detection

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Context-Aware Collaborative Ranking 15th

Context-Aware Collaborative Ranking

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15th Asia Information Retrieval Societies Conference, AIRS 2019

作者： Dai, Wei Pan, Weike Ming, Zhong National Engineering Laboratory for Big Data System Computing Technology College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9783030428341

Recommender systems (RS) are being used in a broad range of applications, from online shopping websites to music streaming platforms, which aim to provide users high-quality personalized services. Collaborative filtering (CF) is a promising technique to ensure the accuracy of a recommender system, which can be divided into specific tasks such as rating prediction and item ranking. However, there is a larger volume of published works studying the problem of rating prediction, rather than item ranking though it is recognized to be more appropriate for the final recommendation in a real application. On the other hand, many studies on item ranking devoted to leveraging implicit feedback are limited in performance improvements due to the uniformity of implicit feedback. Hence, in this paper, we focus on item ranking with informative explicit feedback, which is also called collaborative ranking. In particular, we propose a novel recommendation model termed context-aware collaborative ranking (CCR), which adopts a logistic loss function to measure the predicted error of ranking and exploits the inherent preference context derived from the explicit feedback. Moreover, we design an elegant strategy to distinguish between positive and negative samples used in the process of model training. Empirical studies on four real-world datasets clearly demonstrate that our CCR outperforms the state-of-the-art methods in terms of various ranking-oriented evaluation metrics. © Springer Nature Switzerland AG 2020.

关键词： Collaborative filtering

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Site Selection of Urban Sewage Treatment Plant Based on Hesitant Fuzzy Linguistic Mcdm Model

SSRN

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SSRN 2022年

With the increasing discharge of municipal wastewater and domestic sewage, the construction of sewage treatment plant is imperative. However, selecting a suitable site for the construction of sewage treatment plants is a challenging issue. Therefore, a novel hesitant fuzzy linguistic multi-criteria decision-making (HFLMCDM) model for the location of sewage treatment plants is proposed. In this model, the hesitant fuzzy linguistic term set (HFLTS) is introduced to accurately evaluate the alternative sites. A novel score function based on trigonometric semantic value (Score-TSV) and distance measure based on trigonometric semantic value and dispersion (Dis-TSVD) are proposed for HFLTSs. Then, A hesitant fuzzy linguistic VIKOR (HFLV) method combining the novel Score-TSV and Dis-TSVD is utilized to rank the alternative sites. Through the analysis of Chongqing case, the effectiveness and superiority of the HFLMCDM model are illustrated and it can provide useful reference for policymakers. © 2022, The Authors. All rights reserved.

关键词： Site selection

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