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Computation of Activation Probabilities in the Independent Cascade Model 5

Computation of Activation Probabilities in the Independent C...

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5th International conference on Control, Decision and Information Technologies (CoDIT)

作者： Yang, Wenjing Brenner, Leonardo Giua, Alessandro Univ Toulon & Var Aix Marseille Univ CNRS LIS UMR 7020 Toulon France Univ Cagliari DIEE Cagliari Italy

ISBN: (纸本)9781538650653

Based on the concepts of word-of-mouth effect and viral marketing, the diffusion of an innovation may be triggered starting from a set of initial users. Estimating the influence spread is a preliminary step to determine a suitable or even optimal set of initial users to reach a given goal. In this paper, we focus on a stochastic model called the Independent Cascade model, and compare a few approaches to compute activation probabilities of nodes in a social network, i.e., the probability that a user adopts the innovation. In the paper, first we propose the Path Method which computes the exact value of the activation probabilities but it has high complexity. Second an approximated method, called SSS-Noself, is obtained by modification of the existing SteadyStateSpread algorithm, based on fixed-point computation, to achieve a better accuracy. Finally an efficient approach, also based on fixed-point computation, is proposed to compute the probability that a node is activated though a path of minimal length from the seed set. this algorithm, called SSS-Bound-t algorithm, can provide a lower-bound for the computation of activation probabilities.

关键词： Technological innovation Computational modeling Social network services Approximation algorithms complexity theory Companies Analytical models

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Hardware-Oriented Fast CU Size and Prediction Mode Decision Algorithm for HEVC Intra Prediction

Hardware-Oriented Fast CU Size and Prediction Mode Decision ...

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International conference for Convergence of Technology (I2CT)

作者： Yanling Xu Xin Huang School of Software Engineering Tongji University Shanghai China

ISBN: (数字)9781538680759

ISBN: (纸本)9781538680766

To achieve higher coding efficiency, High Efficiency Video Coding (HEVC) introduces a quadtree-based coding structure and provides 35 prediction modes in intra prediction. As a result, HEVC can reduce about 50% bit-rate compared with H.264/AVC. However, the higher efficiency is along with higher complexity. Computational complexity increases at least 2 times in HEVC which makes it difficult for hardware implementation, especially for some real-time encoders. In this paper, a hardware-oriented fast algorithm is proposed to reduce computational complexity for HEVC intra prediction. Firstly, an improved discretization total variation (IDTV) computation process will be executed for a largest coding unit (LCU). With the IDTV, coding unit (CU) partition can be early terminated if the IDTV is lower than or equal to a predefined threshold. the intermediate result directional variations (DVs) are utilized to construct the candidate mode list before the Rough Mode Decision (RMD) starts. We also adjust the number of best modes after the RMD stage to go through Rate-Distortion Optimization (RDO). the experiment results show that the proposed algorithm can save about 34.2% encoding time and increase only 0.52% Bjontegaard Delta Rate (BD-Rate) [13] on average.

关键词： HDTV Encoding Prediction algorithms complexity theory Copper High efficiency video coding Partitioning algorithms

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complexity Analysis of the Parallel Memetic Algorithm for the Pickup and Delivery Problem with Time Windows 5th

Complexity Analysis of the Parallel Memetic Algorithm for th...

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5th International conference on Man-Machine Interactions (ICMMI)

作者： Blocho, Miroslaw Nalepa, Jakub Silesian Tech Univ Gliwice Poland

ISBN: (纸本)9783319677927;9783319677910

Estimating the theoretical complexity of a parallel algorithm can give an impression on how it will perform in practice. However, this complexity analysis is very often omitted in the works from the parallel computation field. In this paper, we theoretically analyze the time complexity of our parallel algorithm for the pickup and delivery problem with time windows (PDPTW), which is an NP-hard discrete optimization task. the PDPTW is a hierarchical objective problem the main objective is to minimize the number of trucks serving the transportation requests, whereas the second objective is to optimize the travel distance. In our approach, the fleet size is optimized using the parallel ejection search, and the distance is minimized using the parallel memetic algorithm. Finally, we report example experimental results showing that our parallel algorithms work very fast in practice.

关键词： complexity analysis PDPTW Parallel memetic algorithm

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Explaining Explanations: An Overview of Interpretability of Machine Learning 5

Explaining Explanations: An Overview of Interpretability of ...

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5th IEEE International conference on Data Science and Advanced Analytics (IEEE DSAA)

作者： Gilpin, Leilani H. Bau, David Yuan, Ben Z. Bajwa, Ayesha Specter, Michael Kagal, Lalana MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781538650905

there has recently been a surge of work in explanatory artificial intelligence (XAI). this research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, providing explanations of their decisions in some level of detail. these explanations are important to ensure algorithmic fairness, identify potential bias/problems in the training data, and to ensure that the algorithms perform as expected. However, explanations produced by these systems is neither standardized nor systematically assessed. In an effort to create best practices and identify open challenges, we describe foundational concepts of explainability and show how they can be used to classify existing literature. We discuss why current approaches to explanatory methods especially for deep neural networks are insufficient. Finally, based on our survey, we conclude with suggested future research directions for explanatory artificial intelligence.

关键词： Artificial intelligence Computational modeling Decision trees Biological neural networks Taxonomy complexity theory

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Introducing Compressed Mixture Models for Predicting Long-Lasting Brake Events 5

Introducing Compressed Mixture Models for Predicting Long-La...

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5th International-Federation-of-Automatic-Control (IFAC) conference on Engine and Powertrain Control, Simulation and Modeling (E-COSM)

作者： Staf, Emil McKelvey, Tomas Volvo Cars Prop Controls & Calibrat S-41878 Gothenburg Sweden Chalmers Univ Technol Elect Engn S-41258 Gothenburg Sweden

ISBN: (纸本)9781510874572

With tougher restrictions on emissions the automotive industry is in dire need of additional functionality to reduce emissions. We conduct a case study trying to predict long-lasting brake events, to support the decision-making process when the engine can beneficially be put to idle or shut down to achieve emission reduction. We introduce Compressed Mixture Models, a multivariate and mixed variate kernel density model featuring online training and complexity reduction, and use it for prediction purposes. the results show that the proposed method produces comparable prediction results as a Random Forest Classifier and outperform a Support Vector Classifier. On an urban road a prediction accuracy of 87.4 % is obtained, while a prediction accuracy of 76.4 % on a highway segment using the proposed method. Furthermore, it is possible to use a trained Compressed Mixture Model as a tool for statistical inference to study the properties of the observed realization of the underlying random variables. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Machine learning recursive algorithms model complexity reduction prediction methods probabilistic models information theory

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Research of Pattern Matching Algorithm Based on KMP and BMHS2

Research of Pattern Matching Algorithm Based on KMP and BMHS...

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International conference on Computer and Communications (ICCC)

作者： Yansen Zhou Ruixuan Pang Department of Information Science and Technology University of International Relations Beijing China

ISBN: (数字)9781728147437

ISBN: (纸本)9781728147444

the improvement of the time performance of pattern matching algorithm mainly lies in reducing the number of character comparisons and increasing the distance of the matching window moving to the right when mismatch. In this paper, it adopts the second idea of improvement. the paper first analyzes KMP algorithm and its improved one, and then introduces BMHS2 algorithm. the distance of moving to the right of two improved algorithms is calculated when mismatch occurs respectively, and then proposes an improved algorithm based on the combination of improved KMP and BMHS2. the idea of this matching algorithm is that the overall matching is carried out from left to right, and every time the matching is carried out from left to right. When the text substring in matching window is mismatched with the pattern string, the larger jump distance of the I_KMP and BMHS2 is adopted to move the matching window to the right. Finally, two experiments to compare the time performance of the three algorithms above are carried out. the result shows that in the same experiment condition, compared to improved KMP and BMHS2, the time performance of improved pattern matching algorithm I_KMP_BMHS2 improved to some extent.

关键词： Pattern matching Time complexity Probability

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Proceedings - 5th IEEE International conference on Cyber Security and Cloud Computing and 4th IEEE International conference on Edge Computing and Scalable Cloud, CSCloud/EdgeCom 2018

Proceedings - 5th IEEE International Conference on Cyber Sec...

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5th IEEE International conference on Cyber Security and Cloud Computing and 4th IEEE International conference on Edge Computing and Scalable Cloud, CSCloud/EdgeCom 2018

ISBN: (纸本)9781538658505

the proceedings contain 38 papers. the topics discussed include: a system for exploratory analysis in cloud;privacy-preserving and unforgeable searchable encrypted audit logs for cloud storage;a real-time sensor network aggregation computing system;a distributed technique for repairing inconsistencies of functional dependencies;MTComm based virtualization and integration of physical machine operations with digital-twins in cyber-physical manufacturing cloud;difference function projective synchronization for secure communication based on complex chaotic systems;an approach of fog detecting magnitude using referenceless perceptual image defogging;reliability ranking prediction for cloud services via skyline;a cooperative game for online cloud federation formation based on security risk assessment;a knowledge-based approach to reducing complexity of maintaining semantic constraints in data exchange and integration;review on big data fusion methods of quality inspection for consumer goods;unique topic query processing on cloud;enterprise workflow modeling based on priced timed Petri nets;performance evaluation of BATMAN-adv wireless mesh network routing algorithms;FASTBEE: a fast and self-adaptive clustering algorithm towards to edge computing;cryptanalysis of an RFID ownership transfer protocol based on cloud;network security based on d-s evidence theory optimizing CS-BP neural network situation assessment;access control scheme based on combination of blockchain and XOR-coding for ICN;and an effective scheme to detect and prevent tampering on the physical layer of WSN.

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Scalable linear classifiers based on exponential loss function 18

Scalable linear classifiers based on exponential loss functi...

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5th ACM India Joint International conference on Data Science and Management of Data (CoDS) / International conference on Management of Data (COMAD)

作者： Tripathi, Sandhya Hemachandra, N. Indian Inst Technol Mumbai 400076 Maharashtra India

ISBN: (纸本)9781450363419

We first propose an empirical risk minimization based binary classification algorithm, ExpERM, with exponential function as surrogate loss function. Our ExpERM algorithm is scalable in both the dimension of feature space as well as in the number of data points as it is an unconstrained differentiable convex optimization problem or a convex optimization problem with a single constraint in the regularized ExpERM framework. Under mild assumption on data, we show that ExpERM classifier is unique. We implement ExpERM on wide collection (large-features, large-examples) of real datasets. We use Wilcoxon signed-rank test to show that these easily computable classifiers have good generalization property as their 5 or 10-fold cross validation error is not significantly different from those classifiers obtained by standard hinge loss based SVMs or AdaBoost classifiers. Using some large feature sized datasets, we make a statistical comparison of SVM and ExpERM and observe that ExpERM takes remarkably less time to train the model. Towards better understanding this simple and effective learning algorithm, we obtain PAC like sample complexity bounds for regularized ExpERM and high probability bounds for ExpERM based on Rademacher complexity and uniform stability notions. Our computational experience suggests that the regularization does not significantly improve the performance of the ExpERM based classifier. Due to repeated calls to be made to the binary classifier routines in implementation of combined multi-class algorithms, ExpERM scheme is expected to be significantly computationally cheaper and hence scalable. We statistically show that CV error of our binary ExpERM classifiers on many multi-class datasets is comparable to those obtained using computationally expensive binary class SVMs.

关键词： Exponential loss function High probability bounds Multi-class classification Algorithmic stability Sample complexity ERM Linear classifiers

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5th International Symposium on Combinatorial Optimization, ISCO 2018

5th International Symposium on Combinatorial Optimization, I...

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5th International Symposium on Combinatorial Optimization, ISCO 2018

ISBN: (纸本)9783319961507

the proceedings contain 35 papers. the special focus in this conference is on Combinatorial Optimization. the topics include: Online firefighting on trees;a multigraph formulation for the generalized minimum spanning tree problem;the distance polytope for the vertex coloring problem;A PTAS for the time-invariant incremental knapsack problem;on bounded pitch inequalities for the min-knapsack polytope;Efficient algorithms for measuring the funnel-likeness of DAGs;jointly optimizing replica placement, requests distribution and server storage capacity on content distribution networks;an exact column generation-based algorithm for bi-objective vehicle routing problems;multi-start local search procedure for the maximum fire risk insured capital problem;Compact MILP formulations for the p-center problem;a branch-and-bound procedure for the robust cyclic job shop problem;an exact algorithm for the split-demand one-commodity pickup-and-delivery travelling salesman problem;descent with mutations applied to the linear ordering problem;characterization and approximation of strong general dual feasible functions;preemptively guessing the center;improved algorithms for k-domination and total k-domination in proper interval graphs;A heuristic for maximising energy efficiency in an OFDMA system subject to QoS constraints;an integer programming approach to the student-project allocation problem with preferences over projects;even flying cops should think ahead;a generalization of the minimum branch vertices spanning tree problem;the next release problem: complexity, exact algorithms and computations;a polyhedral view to generalized multiple domination and limited packing;alternating current optimal power flow with generator selection;parameterized algorithms for module map problems;2 CSPs all are approximable within a constant differential factor.

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Centroid Selection in Kernel Extreme Learning Machine using K-means

Centroid Selection in Kernel Extreme Learning Machine using ...

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International conference on Signal Processing and Integrated Networks (SPIN)

作者： Mona Singhal Sanyam Shukla Department of Computer Science and Engineering Maulana Azad National Institute of Technology Bhopal Madhya Pradesh

Kernel Extreme Learning Machine (KELM) is used for classification, regression, clustering and feature selection with the help of kernel functions. Conventional KELM uses all training instances as centroids for classification problem while reduced KELM uses randomly choosen training instances as centroids. Furthermore, reduced KELM is used for reducing the computational complexity of conventional KELM. To further improve the computational complexity of KELM, K-means clustering algorithm for centroid selection in KELM is proposed in this paper. In this proposed approach, number of centroids are selected as 1/10 or 5/10 of the total number of training instances and then centroids are computed by using K-means algorithm. Experiments have been carried out by using 15 data sets to illustrate the effectiveness of the proposed method. the results obtained show the reduction in computational time and increment in G-mean which verify the proposed method as an efficient approach in comparison to earlier works.

关键词： Testing Kernel Training Clustering algorithms Mathematical model Computational complexity Partitioning algorithms

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