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8th IEEE International Conference on big data Security on Cloud, 8th IEEE International Conference on High Performance and Smart Computing, and 8th IEEE International Conference on Intelligent data and Security, bigdataSecurity/HPSC/IDS 2022

作者： Zhang, Pengyu Li, Yufei Ge, Xinliang Hu, Wenhui Sun, Lizhuang Liu, Xueyang State Grid Corporation of China Big Data Center Beijing China Peking University National Engineering Research Center for Software Engineering Beijing China Peking University School of Software & Microelectronics Beijing China

ISBN: (纸本)9781665480697

State Grid Enterprise Common data Model (SG-CIM) is a semantically unified data model for smart grid business applications. The model is divided into entity and relation files. Since the relations in SG-CIM model are manually labeled, which consumes a lot of labor costs and is also prone to omission and mislabeling. In order to solve these problems, this paper introduces a method for predicting complex relations in SG-CIM model based on graph structure and semantic information. The relations of graph structure and semantic information are extracted and used by BERT and ERNIE models to predict the relation types between head and tail entities. In order to improve the efficiency of relation prediction, the five relation types are classified into the parallel type and the inheritance type based on the structural relation between entities, and the pairs of entities are separately mined using frequent term mining and K-nearest neighbor methods. Using these methods for predicting complex relations in SG-CIM models can effectively save labor costs. © 2022 IEEE.

关键词： Forecasting

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Vehicle Tracking Method Based on Pyramid Layered Optical Flow Network 22

Vehicle Tracking Method Based on Pyramid Layered Optical Flo...

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4th World Symposium on software engineering, WSSE 2022

作者： Cheng, Jingya Ma, Zhiqiang Bao, Caijilahu Gao, Wenjun Li, Leixiao Wang, Hongbin College of Data Science and Application Inner Mongolia University of Technology China Inner Mongolia Autonomous Region Engineering Technology Research Centre of Big Data Based Software Service China

ISBN: (纸本)9781450396950

In order to solve the problem that the vehicles associated with the track cannot be uniquely identified when tracking a moving vehicle by MOT approaches in computer vision field, a moving vehicle tracking method based on pyramid hierarchical optical flow network was proposed. The Pyramid Optical Flow Net (POFN) was used to estimate the motion of moving vehicles, and the optical flow map containing the movement information of vehicles was obtained. The action refined net was designed to identify the optical flow map and estimate the more accurate position of vehicles. The motion information of the vehicle was correlated with the Intersection over Union (IOU), and the cosine similarity algorithm was used to verify the correlation of the appearance information of the vehicle boundary frame after the correlation of the motion information, so as to complete the vehicle tracking. The test results of UA-DETRAC data set show that the number of track ID switching of POFN model is less than that of current advanced tracking methods. © 2022 ACM.

关键词： Optical flows

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Ha-D3qn: Embedding Virtual Private Cloud in Cloud data Centers with Heuristic Assisted Deep Reinforcement Learning

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SSRN 2023年

作者： Chen, Meng Hou, Jiaxin Sheng, Yongpan Wu, Yingbo Wang, Sen Lu, Jianyuan Fan, Qilin School of Big Data and Software Engineering Chongqing University China Alibaba Group China

The virtual network embedding problem is embedding virtual networks (VNs) in a substrate network so that revenue or accept ratio is maximized. Previous study usually assumes disclosed communication demand among the virtual nodes in a VN, mismatching real-world cloud computing scenarios. In this paper, we propose a new VN abstraction based on the widely used Virtual Private Cloud model, where internal communication demand is unknown to cloud providers. In contrast with the majority of existing research, we allow the co-location of the virtual nodes belonging to the same VN, and introduce the concept of switching capacity for practical resource reservation. We categorize the substrate resources in cloud data centers into additive and non-additive for the first time, and devise our algorithms accordingly. After formulating the problem, we propose a solution framework named HA-D3QN (Heuristic Assisted Dueling Double Deep Q Network). Essentially, HA-D3QN selects the best responses to different system states by combining the D3QN deep reinforcement learning structure and the candidate actions, which are generated by our proposed heuristic algorithms for addressing the exponentially large action space. Finally, we conduct extensive simulation experiments, the results of which verify the effectiveness of our approach. © 2023, The Authors. All rights reserved.

关键词： Heuristic algorithms

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Injections Attacks Efficient and Secure Techniques Based on Bidirectional Long Short Time Memory Model

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Computers, Materials & Continua 2023年第9期76卷 3605-3622页

作者： Abdulgbar A.R.Farea Gehad Abdullah Amran Ebraheem Farea Amerah Alabrah Ahmed A.Abdulraheem Muhammad Mursil Mohammed A.A.Al-qaness School of Big Data&Software Engineering Chongqing UniversityChongqing401331China Department of Management Science Engineering Dalian University of TechnologyDalian116024China Software College Northeastern UniversityShenyang110169China Department of Information Systems College of Computer and Information ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Management Science and Engineering South China University of TechnologyGuangzhou510641China Department of Computer Engineering and Mathematics University of Rovira i VirgiliTarragonaSpain College of Physics and Electronic Information Engineering Zhejiang Normal UniversityJinhua321004China

E-commerce,online ticketing,online banking,and other web-based applications that handle sensitive data,such as passwords,payment information,and financial information,are widely *** web developers may have varying levels of understanding when it comes to securing an online *** Query language SQL injection and cross-site scripting are the two vulnerabilities defined by the OpenWeb Application Security Project(OWASP)for its 2017 Top Ten List Cross Site Scripting(XSS).An attacker can exploit these two flaws and launch malicious web-based actions as a result of these *** published articles focused on these attacks’binary *** article described a novel deep-learning approach for detecting SQL injection and XSS *** datasets for SQL injection and XSS payloads are combined into a single *** dataset is labeledmanually into three labels,each representing a kind of *** work implements some pre-processing algorithms,including Porter stemming,one-hot encoding,and the word-embedding method to convert a word’s text into a *** model used bidirectional long short-term memory(BiLSTM)to extract features automatically,train,and test the payload *** payloads were classified into three types by BiLSTM:XSS,SQL injection attacks,and *** outcomes demonstrated excellent performance in classifying payloads into XSS attacks,injection attacks,and non-malicious ***’s high performance was demonstrated by its accuracy of 99.26%.

关键词： Web security SQL injection XSS deep learning RNN LSTM BiLSTM

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Fine-Grained Tuple Transfer for Pipelined Query Execution on CPU-GPU Coprocessor 28th

Fine-Grained Tuple Transfer for Pipelined Query Execution o...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Yang, Zhenhua Pan, Qingfeng Xu, Chen Shanghai Engineering Research Center of Big Data Management East China Normal University Shanghai200062 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783031306365

To leverage the massively parallel capability of GPU for query execution, GPU databases have been studied for over a decade. Recently, researchers proposed to execute queries with both CPU and GPU in a pipelined approach. In the pipelined query execution, the cross-processor tuple transfer plays a crucial role for the overall query execution performance. The state-of-the-art solution achieves cross-processor tuple transfer using a queue-like data structure. However, it is coarse-grained due to the use of a single spin lock to achieve thread-safety. This design causes performance issues as it prevents the threads from accessing the queue simultaneously. In this paper, we propose a fine-grained tuple transfer mechanism. It employs decoupled enqueue/dequeue to enable two threads on different processors to access the queue at the same time. Moreover, this mechanism explores subqueue-based locking to enable the threads on the same processor to access the queue at the same time. In particular, we implement a prototype system, namely π QC, which adopts fine-grained tuple transfer. Our experiments show that π QC achieves an order of magnitude better performance than existing GPU databases such as HeavyDB. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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New Estimation on C-Eigenvalue Inclusion Sets of Piezoelectric-Type Tensors

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SSRN 2023年

作者： Liu, Qingbing College of Big Data and Software Engineering Zhejiang Wanli University Ningbo315100 China

C-eigenvalues of piezoelectric-type tensors play an important role in Piezoelectric effect and converse Piezoelectric effect. In this article, using the largest absolute value components of left and right C-eigenvectors, we obtain a class of new inclusion sets to locate all C-eigenvalues for a given piezoelectric-type tensor. Numerical examples are given to show the efficiency of the proposed inclusion sets. © 2023, The Authors. All rights reserved.

关键词： Tensors

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A Complex Belief Jensen-Shannon Divergence in Complex Evidence Theory for Decision-Making

A Complex Belief Jensen-Shannon Divergence in Complex Eviden...

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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Gao, Linlu Yin, Mingrui Xiao, Fuyuan Cao, Zehong The School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Mawson Lakes Campus University of South Australia Stem Adelaide Australia

ISBN: (纸本)9798350316308

Effective target detection and recognition are essential in remote sensing image field, and complex evidence theory (CET) is widely used for this purpose. However, measuring conflict between complex basic belief assignments (CBBAs) in CET is challenging. This study proposes a complex belief Jensen-Shannon divergence based on the complex Pignistic transformation to measure conflict, accounting for quantum interference effects in CBBAs. We analyze the properties of the CBJS divergence, including boundedness, nondegeneracy, and symmetry. Numerical examples are presented to show the effectiveness of the conflict measure method and the potential of improving the robustness of intelligent interpretation system. Moreover, an algorithm for decision-making is presented and applied in pattern recognition illustrating its superiority. © 2023 IEEE.

关键词： Decision making

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Behavior Recognition Network for Small Target Vehicle based on Attention Mechanism 22

Behavior Recognition Network for Small Target Vehicle based ...

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4th World Symposium on software engineering, WSSE 2022

作者： Wu, Zhuoyi Ma, Zhiqiang Bao, Caijilahu Li, Leixiao Zhang, Xiaoxu Zhu, Fangyuan College of Data Science and Application Inner Mongolia University of Technology China Inner Mongolia Autonomous Region Engineering Technology Research Centre of Big Data Based Software Service China

ISBN: (纸本)9781450396950

Vehicle behavior recognition is one of the most important research fields in intelligent transportation. In the field of traffic surveillance videos, target vehicles only occupy small proportion of the video frame,making it difficult for the network to extract key features of small target vehicles. To solve this problem, this paper constructed vehicle-3C, a video dataset for vehicle behavior recognition, and was combined with deep learning theory. As a result, it proposed a vehicle behavior recognition method based on the theory of Two-Stream with Attention Network (TSAN). For this method, the two-stream convolutional network is used as a basic framework and attention units are embedded in it to extract temporal (motion) features and spatial features of target vehicles in traffic surveillance videos, and then the temporal and spatial features are fused for category judgment. The results showed that TSAN could achieve 81.8% identification accuracy in vehicle-3C dataset, which was better than other behavior recognition methods based on deep learning. In addition, TSAN also achieves a recognition accuracy of 77.2% in dataset UCF-101, which verifies the generalization performance of the network. Experimental results showed that TSAN could accurately extract and effectively fuse the temporal and spatial features of the foreground target vehicle in the video and achieve high recognition accuracy in the vehicle behavior recognition task. © 2022 ACM.

关键词： Vehicles

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Research on Optimization Strategy of Cold-chain Logistics Car Scheduling

Research on Optimization Strategy of Cold-chain Logistics Ca...

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2021 International Conference on Computer Application in Transportation engineering, CATE 2021

作者： Yang, Aimin Liu, Jie Wang, Yue College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China

In this paper, aiming at the vehicle information supervision problem existing in the transportation process of the third-party small and medium-sized cold chain logistics enterprises, combined with the actual monitoring demand of the vehicle, the cold chain logistics vehicle dynamic monitoring system is designed, and the vehicle scheduling optimization algorithm in the system is carried out. The study. In the aspect of vehicle scheduling in the system, an optimized genetic algorithm is used to find the optimal transportation path and reduce the transportation cost before transportation by planning the transportation sequence and path of the site. © Published under licence by IOP Publishing Ltd.

关键词： Genetic algorithms

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Weak Signal Detection based on Beta Divergence 24

Weak Signal Detection based on Beta Divergence

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Liu, Jiahao Liu, Haoxuan Pu, Wenqiang Zhou, Rui You, Ming-Yi Shi, Qingjiang Science and Technology on Communication Information Security Control Laboratory Jiaxing China The Chinese University of HongKong Shenzhen Research Institute of Big Data Shenzhen China Tongji University School of Software Engineering Shanghai China

ISBN: (纸本)9781665496261

Among different electromagnetic spectrum monitoring tasks, a fundamental one is detecting the presence of weak communication signal under non-cooperative setting. Various detection methods have been studied in the literature. These detection methods usually construct a proper hypothesis testing metric to distinguish the signal-plus-noise and noise-only signals. Information entropy based metric has recently been studied and shown its superiority over traditional energy based metrics. We find that it can be explained as a realization of the general Kullback-Leibler divergence. Inspired by that, in this paper, we propose to detect the signal using a statistic divergence called β-divergence. Different choices of β will lead to several well known divergences, e.g., Itakura-Saito divergence and Euclidean distance. We study its detection performance for non-cooperative signal sources. Both simulation and real experiments demonstrate that β-divergence can improve detection performance for various modulation types of communication signals. © 2023 IEEE.

关键词： Cooperative communication

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