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Minimizing Influence of Rumors by Blockers on Social Networks: algorithms and Analysis

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2020年第3期7卷 1067-1078页

作者： Yan, Ruidong Li, Deying Wu, Weili Du, Ding-Zhu Wang, Yongcai Rennain Univ China Sch Informat Beijing 100872 Peoples R China Univ Texas Dallas Dept Comp Sci Richardson TX 75080 USA

Online social networks, such as Facebook, Twitter, and Wechat have become major social tools. The users can not only keep in touch with family and friends, but also send and share the instant information. However, in some practical scenarios, we need to take effective measures to control the negative information spreading, e.g., rumors spread over the networks. In this paper, we first propose the minimizing influence of rumors (MIR) problem, i.e., selecting a blocker set B with k nodes such that the users' total activation probability by rumor source set S is minimized. Then, we employ the classical independent cascade (IC) model as an information diffusion model. Based on the IC model, we prove that the objective function is monotone decreasing and non-submodular. To address the MIR problem effectively, we propose a two-stages method generating candidate set and selecting blockers for the general networks. Furthermore, we also study the MIR problem on the tree network and propose a dynamic programming guaranteeing the optimal solution. Finally, we evaluate proposed algorithms by simulations on synthetic and real-life social networks, respectively. Experimental results show our algorithms are superior to the comparative heuristic approaches, such as out-degree, betweenness centrality, and PageRank.

关键词： Social networking (online) Heuristic algorithms Integrated circuit modeling greedy algorithms Approximation algorithms Dynamic programming Social network rumor blocking submodularity greedy algorithm dynamic programming

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Sensor Placement for Optimal Control of Infinite-Dimensional Systems

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2020年第1期7卷 360-371页

作者： Hou, Qiqiang Clark, Andrew Worcester Polytech Inst Dept Elect & Comp Engn Worcester MA 01609 USA

An important challenge in controlling distributed parameter systems is implementing feedback control laws over an infinite-dimensional space. One widely studied approach is to place sensors at a set of discrete locations and then approximate the state feedback using the sensor outputs. This approach naturally raises the question of where the sensors should be located. In this paper, we investigate the problem of placing a set of sensors on the unit interval in order to minimize the mean-square deviation between a desired infinite-dimensional control law, and an approximate finite-dimensional controller obtained by applying state feedback at the chosen sensor positions. We propose a greedy algorithm and derive optimality bounds on the selected set of sensors. We also present a simplified greedy algorithm in which the incremental improvements from adding each sensor are not updated at each step. We analyze the performance of the approaches under two scenarios. In the case where the sensor placements are constrained such that each of the detection radii of each pair of sensors do not overlap, we show that the two algorithms are equivalent and achieve a $1/2+\epsilon$ optimality guarantee, where $\epsilon$ can be made arbitrarily small at the cost of increased computational overhead. When overlaps exist between sensor detection areas, we derive an optimality bound for the simplified algorithm. The value of the optimality bound is determined by the sensor model, cardinality of sensor set, and the allowed minimum sensor distance. Our approach is illustrated through numerical study, in which we compare our proposed greedy algorithm, the current state-of-the-art approach, and the true optimum obtained from exhaustive search.

关键词： greedy algorithm partial differential equations (PDEs) sensor placement

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A Risk Management Approach to Defending Against the Advanced Persistent Threat

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2020年第6期17卷 1163-1172页

作者： Yang, Lu-Xing Li, Pengdeng Yang, Xiaofan Tang, Yuan Yan Deakin Univ Sch Informat Technol Melbourne Vic 3125 Australia Chongqing Univ Sch Software Engn Chongqing 400044 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing 100083 Peoples R China Univ Macau Dept Comp & Informat Sci Taipa 999078 Macau Peoples R China

The advanced persistent threat (APT) as a new kind of cyber attack has posed a severe threat to modern organizations. When the APT has been detected, the organization has to deal with the APT response problem, i.e., to allocate the available response resources to fix her insecure hosts so as to mitigate her potential loss. This paper addresses the APT response problem by using the risk management approach. First, we introduce a model characterizing the evolution of the organization's expected state. By analyzing this model, we find the organization's expected state approaches a common limit expected state. Then, we use the organization's expected loss per unit time to measure her potential loss, and we find this measure is determined by the organization's limit expected state. On this basis, we model the APT response problem as a game-theoretic problem (the APT response game) in which the organization seeks a Nash equilibrium. We present a greedy algorithm for solving the game. Comparative experiments show that the algorithm is effective. Therefore, we recommend the response strategy generated by performing the algorithm. These findings contribute to defending against the APT. To our knowledge, this is the first time the APT response problem is addressed.

关键词： Organizations Loss measurement Games Nash equilibrium Mathematical model Time measurement Risk management Advanced persistent threat potential loss APT response problem risk management state evolution model equilibrium risk evaluation APT response game greedy algorithm

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Efficient multiscale methods for the semiclassical Schrodinger equation with time-dependent potentials

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2020年 369卷 113232-113232页

作者： Chen, Jingrun Li, Sijing Zhang, Zhiwen Soochow Univ Math Ctr Interdisciplinary Res Suzhou Peoples R China Soochow Univ Sch Math Sci Suzhou Peoples R China Univ Hong Kong Dept Math Pokfulam Rd Hong Kong Peoples R China

The semiclassical Schrodinger equation with time-dependent potentials is an important model to study electron dynamics under external controls in the mean-field picture. In this paper, we propose two multiscale finite element methods to solve this problem. In the offline stage, for the first approach, the localized multiscale basis functions are constructed using sparse compression of the Hamiltonian operator at the initial time;for the latter, basis functions are further enriched using a greedy algorithm for the sparse compression of the Hamiltonian operator at later times. In the online stage, the Schrodinger equation is approximated by these localized multiscale basis functions in space and is solved by the Crank-Nicolson method in time. These multiscale basis functions have compact supports in space, leading to the sparsity of the stiffness matrix, and thus the computational complexity of these two methods is comparable to that of the standard finite element method. The spatial mesh size in the multiscale finite element methods is O(epsilon), while the mesh size in the standard finite element method is o(epsilon), where e is the semiclassical parameter. Through a number of numerical examples in 1D and 2D, for approximately the same number of basis, we show that the approximation error of the multiscale finite element method is at least two orders of magnitude smaller than that of the standard finite element method, and the enrichment further reduces the error by another one order of magnitude. (C) 2020 ElsevierB.V. All rights reserved.

关键词： Semiclassical Schrodinger equation Time-dependent potential Multiscale finite element method Enriched multiscale basis greedy algorithm

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Propagation-Model-Free Base Station Deployment for Mobile Networks: Integrating Machine Learning and Heuristic Methods

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IEEE ACCESS 2020年 8卷 83375-83386页

作者： Dai, Lingcheng Zhang, Hongtao Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

As densification is the promising trend of future mobile networks, deployment of base stations (BSs) becomes increasingly difficult due to the laborious procedures in network planning;besides, unreasonable layout may lead to poor coverage performance. Hence, this paper firstly trains a propagation-model-free received signal strength (RSS) predictor based on machine learning (ML) models, and then optimizes coverage performance of BS deployment via multi-objective heuristic methods. Specifically, many practical features that affect signal propagation like geographical types and operating parameters of BS, are fed into ML models to predict RSS in a rasterized area;then based on the trained model, a well-designed multi-objective genetic algorithm (GA) is proposed to minimize the number of deployed BSs with coverage constraint. For the practical considerations of fast convergence and output-consistence, greedy algorithm with fixed initial solution and searching direction is also carried out. Moreover, the typical scenarios of incremental deployment (the mobile operator needs to deploy more BSs on the basis of the existing deployment) and BS outage compensation (one BS fails and other BSs need to adjust their configurations to fill the coverage gap), are also investigated for practical needs. Simulations show that multi-layer perceptron outperforms other ML algorithms in terms of RSS prediction with mean absolute error (MAE) yielded to 3.78 dB;and numerical results verify the convergence and availability of the proposed algorithms, which shows 18.5 & x0025;gain than the real-world deployment in terms of coverage rate.

关键词： Propagation-model-free base station deployment machine learning genetic algorithm greedy algorithm

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Multiuser Space-Time Line Code With Transmit Antenna Selection

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IEEE ACCESS 2020年 8卷 71930-71939页

作者： Joung, Jingon Choi, Jihoon Chung Ang Univ Sch Elect & Elect Engn Seoul 06974 South Korea Korea Aerosp Univ Sch Elect & Informat Engn Goyang 10540 South Korea

In this paper, a multiuser space-time line code (MU-STLC) scheme is newly designed that concurrently delivers multiple STLC signals to multiple users, and a preprocessing matrix for the MU-STLC is derived based on the minimum mean square error criterion. The novel MU-STLC method retains the conventional STLC receiver structure so that each user linearly combines the received signals without using the full channel state information to decode the STLC signals. With more transmit antennas than the number of users having two receive antennas, a transmit antenna selection (TAS) scheme is investigated in combination with the proposed MU-STLC method, and the detection signal-to-interferenceplus-noise ratio (SINR) is derived depending on a specific TAS pattern. The performance improvement obtained from the TAS is significant, yet finding the optimal TAS pattern is a combinatorial problem that requires prohibitively high computational complexity. To resolve this issue, a greedy TAS algorithm is also proposed that iteratively selects the transmit antenna maximizing the detection SINR in each greedy step. The numerical results verify the efficacy of the proposed MU-STLC system with the SINR-based greedy TAS algorithm in terms of the bit error rate performance and computational complexity. For example, comparing with a scheme that selects four antennas from eight antennas randomly to support four users, the proposed TAS scheme can reduce the required signal-to-noise ratio for achieving 10(-3) bit-error-rate by approximately 6 dB when quadrature phase-shift keying is employed. Furthermore, the proposed method can achieve comparable performance to the optimal antenna selection scheme with the reduced computational complexity by O(M-5) from O(MU+3), where M and U are the numbers of transmit antennas and selected antennas (or users), respectively.

关键词： Space-time line code multiuser transmit antenna selection greedy algorithm

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Online action recognition from RGB-D cameras based on reduced basis decomposition

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2020年第2期17卷 341-356页

作者： Arunraj, Muniandi Srinivasan, Andy Juliet, A. Vimala SRM Inst Sci & Technol Dept EIE Kattankulathur Campus Kattankulathur 603203 Tamil Nadu India Valliammai Engn Coll Dept EIE Kattankulathur Campus Kattankulathur 603203 Tamil Nadu India

Human action recognition from RGB-D cameras has recently become one of the major fields of research. While accuracy improvement was given more importance in previous action/gesture recognition methods, there are opportunities to work on improving the computational efficiency too. This paper introduces an efficient dimensionality reduction technique and classification mechanism to recognize actions from depth motion map features. For our proposed work, a recently introduced technique called reduced basis decomposition ( RBD) is employed, which manages faster dimensional reduction with its unique mechanism of generating compressed basis vectors. The RBD has an offline error-determination and an online approximation mechanism, and it is faster than PCA/SVD. For classification, this paper employs a Probabilistic Collaborative Representation Classifier (Pro-CRC). The recommended classifier works based on probability in connection with l(2)-regularization. The combined effect of the methods above helps in achieving the state-of-the-art efficiency. In the standard protocol tests carried out in the MSR-Action3D dataset, our proposed method achieved a considerable accuracy of 91.7% which is better than the currently efficient method. Further, our proposed method also proved its effectiveness in the challenging, subject-generic test with a reported accuracy of 89.64% and an average accuracy of 85.70% in the cross fixed tests which included 252 combinations of all the subjects without repetition.

关键词： Human action recognition Depth motion map Collaborative representation classifier greedy algorithm Reduced basis decomposition

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A greedy region growing algorithm for anisotropic stretch adaptive triangulation of geometry images

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GRAPHICAL MODELS 2019年 106卷 101045-000页

作者： Bayazit, Ulug Istanbul Tech Univ Istanbul Turkey

The geometry image representation is a remeshing of an irregular triangle surface mesh onto a rectangular grid of points and facilitates the processing of surface geometry. Its connectivity is commonly generated by splitting each rectangular quad of grid points along the shorter diagonal. However, this simple approach typically yields skinny triangles on the surface when the underlying local surface metric tensor eigenvalues differ significantly. Sequences of such triangles can appear like jaggedness. This paper presents a greedy, region growing algorithm for anisotropic triangulation of geometry images obtained by geometric stretch parametrization. The algorithm compensates for the local stretch anisotropy and variations in the principal directions of the metric tensor by minimizing the total length of the new edges of triangles added to the grown region. The surface reconstructed is more faithful to the original surface than the ones reconstructed by quad-splitting connectivity and recognized triangulation approaches for point clouds.

关键词： Triangulation Geometry image Surface reconstruction greedy algorithm Region growing

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Computation Offloading and Task Scheduling for DNN-Based Applications in Cloud-Edge Computing

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IEEE ACCESS 2020年 8卷 115537-115547页

作者： Chen, Zheyi Hu, Junqin Chen, Xing Hu, Jia Zheng, Xianghan Min, Geyong Univ Exeter Coll Engn Math & Phys Sci Exeter EX4 4QF Devon England Fuzhou Univ Coll Math & Comp Sci Fuzhou 350108 Peoples R China

Due to the high demands of deep neural network (DNN) based applications on computational capability, it is hard for them to be directly run on mobile devices with limited resources. Computation offloading technology offers a feasible solution by offloading some computation-intensive tasks of neural network layers to edges or remote clouds that are equipped with sufficient resources. However, the offloading process might lead to excessive delays and thus seriously affect the user experience. To address this important problem, we first regard the average response time of multi-task parallel scheduling as our optimization goal. Next, the problem of computation offloading and task scheduling for DNN-based applications in cloud-edge computing is formulated with a scheme evaluation algorithm. Finally, the greedy and genetic algorithms based methods are proposed to solve the problem. The extensive experiments are conducted to demonstrate the effectiveness of the proposed methods for scheduling tasks of DNN-based applications in different cloud-edge environments. The results show that the proposed methods can obtain the near-optimal scheduling performance, and generate less average response time than traditional scheduling schemes. Moreover, the genetic algorithm leads to less average response time than the greedy algorithm, but the genetic algorithm needs more running time.

关键词： Cloud-edge computing DNN-based applications computation offloading task scheduling greedy algorithm genetic algorithm

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Sparse Identification of Volterra Models for Power Amplifiers Without Pseudoinverse Computation

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2020年第11期68卷 4570-4578页

作者： Becerra, Juan A. Ayora, Maria Jose Madero Reina-Tosina, Javier Crespo-Cadenas, Carlos Univ Seville Escuela Tecn Super Ingn Dept Teoria Senal & Comunicac Seville 41092 Spain

We present a new formulation of the doubly orthogonal matching pursuit (DOMP) algorithm for the sparse recovery of Volterra series models. The proposal works over the covariance matrices by taking advantage of the orthogonal properties of the solution at each iteration and avoids the calculation of the pseudoinverse matrix to obtain the model coefficients. A detailed formulation of the algorithm is provided along with a computational complexity assessment, showing a fixed complexity per iteration compared with its previous versions in which it depends on the iteration number. Moreover, we empirically demonstrate the reduction in computational complexity in terms of runtime and highlight the pruning capabilities through its application to the digital predistortion of a class J power amplifier operating under 5G-NR signals with the bandwidth of 20 and 30 MHz, concluding that this proposal significantly outperforms existing techniques in terms of computational complexity.

关键词： Computational modeling Matching pursuit algorithms Computational complexity Mathematical model Covariance matrices Signal processing algorithms Behavioral modeling digital predistortion (DPD) doubly orthogonal matching pursuit (DOMP) greedy algorithm model identification power amplifier (PA) sparse regression Volterra series

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