A Heuristic Method of Identifying Key Microbloggers

2016 ◽  
pp. 417-426 ◽  
Author(s):  
Lidong Huang ◽  
Wenxian Wang ◽  
Xiaozhou Chen ◽  
Junhua Chen
Keyword(s):  
Heuristic Method

A Heuristic Method Based on Multi-Objective Optimization Concept for Solving RNA Multiple Alignment

2017 ◽  
Vol 10 (5) ◽  
pp. 371
Author(s):  
Arakil Chentoufi ◽  
Abdelhakim El Fatmi ◽  
Molay Ali Bekri ◽  
Said Benhlima ◽  
Mohamed Sabbane
Keyword(s):  
Heuristic Method ◽  
Multiple Alignment ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals gbt-HIPS: Explaining the Classifications of Gradient Boosted Tree Ensembles

2021 ◽  
Vol 11 (6) ◽  
pp. 2511
Author(s):  
Julian Hatwell ◽  
Mohamed Medhat Gaber ◽  
R. Muhammad Atif Azad
Keyword(s):  
State Of The Art ◽  
Heuristic Method ◽  
Good Explanation ◽  
Classification Rule ◽  
Data Sets ◽  
Classification Models ◽  
Boundary Values ◽  
Class Label ◽  
Input Space ◽  
Boosted Tree

This research presents Gradient Boosted Tree High Importance Path Snippets (gbt-HIPS), a novel, heuristic method for explaining gradient boosted tree (GBT) classification models by extracting a single classification rule (CR) from the ensemble of decision trees that make up the GBT model. This CR contains the most statistically important boundary values of the input space as antecedent terms. The CR represents a hyper-rectangle of the input space inside which the GBT model is, very reliably, classifying all instances with the same class label as the explanandum instance. In a benchmark test using nine data sets and five competing state-of-the-art methods, gbt-HIPS offered the best trade-off between coverage (0.16–0.75) and precision (0.85–0.98). Unlike competing methods, gbt-HIPS is also demonstrably guarded against under- and over-fitting. A further distinguishing feature of our method is that, unlike much prior work, our explanations also provide counterfactual detail in accordance with widely accepted recommendations for what makes a good explanation.

scholarly journals Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3936
Author(s):  
Yannis Spyridis ◽  
Thomas Lagkas ◽  
Panagiotis Sarigiannidis ◽  
Vasileios Argyriou ◽  
Antonios Sarigiannidis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Heuristic Method ◽  
Sensor Nodes ◽  
Convolutional Network ◽  
Received Signal Strength Indicator ◽  
Anchor Nodes ◽  
Time Required ◽  
Rf Signal ◽  
Deep Learning Model

Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs.

scholarly journals A Novel Heuristic Method for Emergency Path Planning Based on Dynamic Spatial-Temporal Characteristics Map

2021 ◽  
Vol 1756 (1) ◽  
pp. 012005
Author(s):  
Bowen Yang ◽  
Lei Yuan ◽  
Jin Yan ◽  
Zhiming Ding ◽  
Zhi Cai ◽  
...  
Keyword(s):  
Path Planning ◽  
Heuristic Method ◽  
Temporal Characteristics

scholarly journals Heuristic rank selection with progressively searching tensor ring network

2021 ◽  
Author(s):  
Nannan Li ◽  
Yu Pan ◽  
Yaran Chen ◽  
Zixiang Ding ◽  
Dongbin Zhao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Compression Ratio ◽  
State Of The Art ◽  
Heuristic Method ◽  
Ring Networks ◽  
Ring Network ◽  
Narrow Region ◽  
Network Search ◽  
Deep Networks ◽  
Evolutionary Phase

AbstractRecently, tensor ring networks (TRNs) have been applied in deep networks, achieving remarkable successes in compression ratio and accuracy. Although highly related to the performance of TRNs, rank selection is seldom studied in previous works and usually set to equal in experiments. Meanwhile, there is not any heuristic method to choose the rank, and an enumerating way to find appropriate rank is extremely time-consuming. Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region. Therefore, based on the above phenomenon, we propose a novel progressive genetic algorithm named progressively searching tensor ring network search (PSTRN), which has the ability to find optimal rank precisely and efficiently. Through the evolutionary phase and progressive phase, PSTRN can converge to the interest region quickly and harvest good performance. Experimental results show that PSTRN can significantly reduce the complexity of seeking rank, compared with the enumerating method. Furthermore, our method is validated on public benchmarks like MNIST, CIFAR10/100, UCF11 and HMDB51, achieving the state-of-the-art performance.

scholarly journals Locating and Imaging through Scattering Medium in a Large Depth

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 90
Author(s):  
Shuo Zhu ◽  
Enlai Guo ◽  
Qianying Cui ◽  
Lianfa Bai ◽  
Jing Han ◽  
...  
Keyword(s):  
Phase Space ◽  
Signal To Noise Ratio ◽  
Speckle Pattern ◽  
Heuristic Method ◽  
Scattering Medium ◽  
Scattering Media ◽  
Signal To Noise ◽  
Practical Applications ◽  
Large Depth ◽  
Strong Scattering

Scattering medium brings great difficulties to locate and reconstruct objects especially when the objects are distributed in different positions. In this paper, a novel physics and learning-heuristic method is presented to locate and image the object through a strong scattering medium. A novel physics-informed framework, named DINet, is constructed to predict the depth and the image of the hidden object from the captured speckle pattern. With the phase-space constraint and the efficient network structure, the proposed method enables to locate the object with a depth mean error less than 0.05 mm, and image the object with an average peak signal-to-noise ratio (PSNR) above 24 dB, ranging from 350 mm to 1150 mm. The constructed DINet firstly solves the problem of quantitative locating and imaging via a single speckle pattern in a large depth. Comparing with the traditional methods, it paves the way to the practical applications requiring multi-physics through scattering media.

scholarly journals Secure the IoT Networks as Epidemic Containment Game

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 156
Author(s):  
Juntao Zhu ◽  
Hong Ding ◽  
Yuchen Tao ◽  
Zhen Wang ◽  
Lanping Yu
Keyword(s):  
Heuristic Algorithms ◽  
Heuristic Method ◽  
Exact Algorithms ◽  
Epidemic Threshold ◽  
Solution Quality ◽  
Sis Model ◽  
Linear Programming Formulation ◽  
Iot Devices ◽  
General Graphs ◽  
The Internet Of Things

The spread of a computer virus among the Internet of Things (IoT) devices can be modeled as an Epidemic Containment (EC) game, where each owner decides the strategy, e.g., installing anti-virus software, to maximize his utility against the susceptible-infected-susceptible (SIS) model of the epidemics on graphs. The EC game’s canonical solution concepts are the Minimum/Maximum Nash Equilibria (MinNE/MaxNE). However, computing the exact MinNE/MaxNE is NP-hard, and only several heuristic algorithms are proposed to approximate the MinNE/MaxNE. To calculate the exact MinNE/MaxNE, we provide a thorough analysis of some special graphs and propose scalable and exact algorithms for general graphs. Especially, our contributions are four-fold. First, we analytically give the MinNE/MaxNE for EC on special graphs based on spectral radius. Second, we provide an integer linear programming formulation (ILP) to determine MinNE/MaxNE for the general graphs with the small epidemic threshold. Third, we propose a branch-and-bound (BnB) framework to compute the exact MinNE/MaxNE in the general graphs with several heuristic methods to branch the variables. Fourth, we adopt NetShiled (NetS) method to approximate the MinNE to improve the scalability. Extensive experiments demonstrate that our BnB algorithm can outperform the naive enumeration method in scalability, and the NetS can improve the scalability significantly and outperform the previous heuristic method in solution quality.

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