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IEEE International Conference on Networking, Sensing and Control

作者： Jie Zong Zhijun Ding The key laboratory of embedded system and service computing ministry of education TONGJI University Shanghai China

ISBN: (纸本)9781665472449

Link prediction is to predict whether there is a link between two nodes in the graph, it is a very important application and plays a great role in various industries. In recent years, with the development of graph neural network technology, many algorithms make effort to study the expression of each node from the original graph data and use them to infer new links. However, most of the existing algorithms have a common problem when facing heterogeneous graphs, which is, they do not consider the weight of edges in graphs. Instead, they put all their energies into computing node features. Although a few algorithms such as RGCN are trying to take the influence of different link types into account while extracting node features, these implicit feature extractions do not start from the global information, but just calculate independently for each node. In other words, in these algorithms, even the same type of links will be abstracted into different features on different nodes. This is obviously inconsistent with reality. On the same map, the feature of the same link should be relatively fixed and should not be changed just because of different positions. In addition, when the current graph neural network algorithm is applied to link prediction, the link type to be predicted must be specified in advance, which makes the algorithm extremely inflexible. In order to solve these problems, we propose an edge weight calculation algorithm that extracts the edge feature from the whole graph. We also propose the edge-weight-based link prediction algorithm. By introducing edge weight into the MLP, there is no need to specify the target link type at the beginning of model training. It improves both the performance and efficiency of the link prediction model. Experiments on two datasets show that this edge-weight-based link prediction algorithm performs better than current algorithms and reaches SOTA.

关键词： Training Industries Semantics Predictive models Prediction algorithms Feature extraction Graph neural networks

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Analysis for one-place unbounded Petri nets based on modified reachability trees

Analysis for one-place unbounded Petri nets based on modifie...

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IEEE International Conference on Networking, Sensing and Control

作者： Yu Wang Zhi Jun Ding Key Laboratory of Embedded System and Service Computing Ministry of Education University of Tongji Shanghai China Key Laboratory of Embedded ystem nd Service Computing Ministry of Education University of Tongji Shanghai China

Analysis approaches of reachability decision is fundamental to Petri nets theory; however, the relevant research work in the past cannot always guarantee the correctness of analysis conclusions. This paper firstly reveals the root cause of wrong conclusions, which are drawn in analysis for deadlock of Petri nets based on modified reachability trees (MRT), and then proposes the rectified calculation of the next-state function and some corresponding definitions, which guarantee MRT's usefulness and correctness in analyzing reachability and deadlock of one-place unbounded Petri nets. Finally, a necessary and sufficient condition of MRT's reachability and the theoretical proof are presented. Examples are given to illustrate the method.

关键词： Petri nets system recovery Firing Laboratories Educational institutions Analytical models Mathematical model

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Robust analog precoding designs for millimeter wave MIMO transceivers 20

Robust analog precoding designs for millimeter wave MIMO tra...

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20th International ITG Workshop on Smart Antennas, WSA 2016

作者： Xia, Pengfei Heath, Robert W. Prelcic, Nuria Gonzalez Key Lab of Embedded System and Service Computing Tongji University Shanghai China University of Texas at Austin AustinTX United States University of Vigo Vigo Pontevedra Spain

ISBN: (纸本)9783800741779

Millimeter wave communication provides high data rates thanks to large arrays at the transmitter and receiver, coupled with large bandwidth channels. Exploiting the arrays is challenging due to the need to configure precoding at the transmitter based on the large frequency selective channel. In this paper we exploit the power iteration principle and propose a robust analog precoding training algorithm that can be applied in both frequency division duplex transmission (FDD) systems and time division duplex transmission (TDD) systems with or without RF calibration. We further analyze the convergence of the proposed algorithm and show how it converges to the singular value decomposition optimality exponentially. We propose null space projection on top of the power iteration to form multiple orthogonal beams at the transmitter and receiver. Strongest tap selection with proper energy pruning is used to collect as much precoding gain as possible from a frequency selective fading channel. The exponential effective SINR mapping performance is evaluated and demonstrates that the overall approach works effectively. Numerical simulation results demonstrate algorithm robustness and the algorithm works effectively not only for the simplified millimeter wave directional channels, but also for more general rich scattering channels. © VDE VERLAG GMBH · Berlin · Offenbach, Germany.

关键词： Millimeter waves

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The preference relation and its application in the information system

The preference relation and its application in the informati...

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IEEE International Conference on Computer Science and Automation Engineering (CSAE)

作者： Zhao Mingqing Key Laboratory of Embedded System and Service Computing Ministry of Education University of Tongji Shanghai China

In order to strengthen the traditional information system's capacity of describing practical problems, preference relation is introduced into information system and the concepts of preference information system, preference decision table, and information utility function are offered in this paper. In addition, the existence theorem of information utility function and several applications of information utility function in knowledge acquisition are provided.

关键词： Information systems Knowledge acquisition Economics Clustering methods Transforms Information science Approximation methods

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Quality assessment for color images with tucker decomposition

Quality assessment for color images with tucker decompositio...

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IEEE International Conference on Image Processing

作者： Cheng, Cheng Wang, Hanli Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

As an extension of the singular value decomposition based approaches, a novel metric based on Tucker decomposition for color image quality assessment is proposed in this paper. It extracts both the spacial and chromatic information of a color image with Tucker decomposition. As compared to most of the other existing quality metrics for color images, the key advantage of the proposed metric is that it treats the color image as a whole entity instead of an assembly of three independent visual channels, and thus the interrelation among the different visual channels is well involved. Experimental results on the LIVE Database Release 2 demonstrate that the proposed metric can generally achieve similar or better performance than other metrics.

关键词： Tensile stress Measurement Image color analysis Color Matrix decomposition Image quality Vectors

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An Efficient and Scalable Algorithm for the Traveling Salesman Problem

An Efficient and Scalable Algorithm for the Traveling Salesm...

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2014 IEEE 5th International Conference on Software Engineering and service Science

作者： Chen Ye Zhongcheng Yang Tianxing Yan Key Laboratory on Embedded System and Service Computing College of Electronics and Information EngineeringTongji University

This paper presents a genetic algorithm based on dynamic programming for solving large-scale instance of the Traveling Salesman Problem(TSP) to ***,an improved dynamic programming algorithm is described to deal with large-scale data,and then it is used as crossover and mutation operator in the genetic *** results show that this novel method with good stability can solve TSP with very-large-scale,effectively reduce the error rate,and improve the solution precision while keeping computational complexity relatively low.

关键词： traveling salesman problem genetic algorithm dynamic programming optimization

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An Adaptive Heterogeneous Credit Card Fraud Detection Model Based on Deep Reinforcement Training Subset Selection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4026-4041页

作者： Zhu, Kun Zhang, Nana Ding, Weiping Jiang, Changjun the Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China the School of Computer Science and Technology Donghua University Shanghai201620 China the School of Information Science and Technology Nantong University Nantong226019 China

Heterogeneous fraud detection is an important means of credit card security assurance, which can utilize historical transaction records in a source and target domain to build an effective fraud detection model. Nevertheless, large feature distribution differences between source and target transaction instances and the complex intrinsic structure hidden behind transaction data make it difficult for existing credit card fraud detection (CCFD) models to capture and transfer the most informative feature representations and seriously hinder detection performance. In this work, we propose a novel adaptive heterogeneous CCFD model named adaptive heterogeneous credit card fraud detection model based on deep reinforcement training subset selection (RTAHC) based on deep reinforcement training subset selection, which mainly contains two components: selection distribution generator (SDG) and transaction fraud detector (TFD, including feature extractor with an attention mechanism and classifier). The SDG can generate the selection probability distribution vector via the reinforcement reward mechanism, and then transaction instances in the source domain relevant to the target domain are selected. The feature extractor with an attention mechanism can learn the abstract deep semantic feature representations of selected source transaction instances and the target domain. The joint training of SDG and TFD can provide more real-time and accurate transaction feature representations to reduce the distribution discrepancy between the two domains. We verify the detection performance of RTAHC across a large real-world credit card transaction dataset and four public datasets. Experimental results demonstrate that the RTAHC model can exhibit competitive CCFD performance. Impact Statement—With the rise of artificial intelligence (AI)generated models, credit card fraud has become increasingly rampant, which also causes tens of billions of U.S. dollars in credit card losses worldwide every year

关键词： Hidden Markov models

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Mining World Indicators for Analyzing and Modeling the Development of Countries

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ACM/IMS Transactions on Data Science 2021年第4期2卷 1–27页

作者： Huang, Hong Chi, Mingyuan Song, Yu Jin, Hai The National Engineering Research Center for Big Data Technology and System Key Laboratory of Service Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

The world indicators released by the World Bank or other organizations usually give the basic public knowledge about the world. However, separate and static index lacks the complex interplay among different indicators and thus cannot help us have an overall understanding of the world. To this end, we study the world indicators from a different angle. Firstly, we discover that there exist correlations between indicators either from a static view or from a dynamic view. Moreover, taking the trade and diplomatic relationships into consideration, we construct a multi-relational network to depict the interactions between different countries, and propose a Multiple Relations to Vector (MR2vec) model to study world indicators from a network perspective. The experimental results show the changes of world indicators are predictable with the proposed model, and our proposed MR2vec has wide adaptability in predicting multi-relation networks. © 2022 Copyright held by the owner/author(s).

关键词： Data mining

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Reader activation scheduling in multi-reader RFID systems: A study of general case

Reader activation scheduling in multi-reader RFID systems: A...

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25th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2011

作者： Tang, Shaojie Wang, Cheng Li, Xiang-Yang Jiang, Changjun Department of Computer Science Illinois Institute of Technology Chicago IL 60616 United States Department of Computer Science Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

ISBN: (纸本)9780769543857

Radio frequency identification (RFID) is a technology where a reader device can "sense" the presence of a close by object by reading a tag device attached to the object. To guarantee the coverage quality, multiple RFID readers can be deployed in the given region. In this paper, we consider the problem of activation schedule for readers in a multi-reader environment. In particular, we try to design a schedule for readers to maximize the number of served tags per time-slot while avoiding various interferences. We first develop a centralized algorithm under the assumption that different readers may have different interference and interrogation radius. Next, we propose a novel algorithm which does not need any location information of the readers. Finally, we extend the previous algorithm in distributed manner in order to suit the case where no central entity exists. We conduct extensive simulations to study the performances of our proposed algorithm. And our evaluation results corroborate our theoretical analysis. © 2011 IEEE.

关键词： Graph theory

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KNOB particle swarm optimizer 1

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1st International Conference on Advances in Swarm Intelligence, ICSI 2010

作者： Zhang, Junqi Liu, Kun Tan, Ying Key Laboratory of Embedded System and Service Computing Department of Computer Science and Technology Tongji University Shanghai 200092 China Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

ISBN: (数字)9783642134951

ISBN: (纸本)3642134947

It is not trivial to tune the swarm behavior just by parameter setting because of the randomness, complexity and dynamic involved in particle swarm optimizer (PSO). Hundreds of variants in the literature of last decade, brought various mechanism or ideas, sometimes also from outside of the traditional metaheuristics field, to tune the swarm behavior. While, in the same time, additional parameters have to be afforded. This paper proposes a new mechanism, named KNOB, to directly tune the swarm behavior through parameter setting of PSO. KNOB is defined as the first principal component of the statistical probability sequence of exploration and exploitation allocation along the search process. The using of the KNOB to tune PSO by parameter setting is realized through a statistical mapping, between the parameter set and the KNOB, learned by a radial basis function neural network (RBFNN) simulation model. In this way, KNOB provides an easy way to tune PSO directly by its parameter setting. A simple application of KNOB to promote is presented to verify the mechanism of KNOB. © 2010 Springer-Verlag.

关键词： Particle swarm optimization (PSO)

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