Machine-Learning-Based White-Hat Worm Launcher in Botnet Defense System

This article proposes a white-hat worm launcher based on machine learning (ML) adaptable to large-scale IoT network for Botnet Defense System (BDS). BDS is a cyber-security system that uses white-hat worms to exterminate malicious botnets. White-hat worms defend an IoT system against malicious bots, the BDS decides the number of white-hat worms, but there is no discussion on the white-hat worms' deployment in IoT network. Therefore, the authors propose a machine-learning-based launcher to launch the white-hat worms effectively along with a divide and conquer algorithm to deploy the launcher to large-scale IoT networks. Then the authors modeled BDS and the launcher with agent-oriented Petri net and confirmed the effect through the simulation of the PN2 model. The result showed that the proposed launcher can reduce the number of infected devices by about 30-40%.

A divide-and-conquer algorithm for seismic data approximation by the Laguerre series

An algorithm of the Laguerre transform for approximating functions on large intervals is proposed. The idea of the considered approach is that the calculation of improper integrals of rapidly oscillating functions is replaced by a solution of an initial boundary value problem for the one-dimensional transport equation. It allows one to successfully avoid the problems associated with the stable implementation of the Laguerre transform. A divide-and-conquer algorithm based on shift operations made it possible to significantly reduce the computational cost of the proposed method. Numerical experiments have shown that the methods are economical in the number of operations, stable, and have satisfactory accuracy for seismic data approximation.

A divide and conquer metacell algorithm for scalable scRNA-seq analysis

Scaling scRNA-seq to profile millions of cells is increasingly feasible. Such data is crucial for the construction of high-resolution maps of transcriptional manifolds. But current analysis strategies, in particular dimensionality reduction and two-phase clustering, offers only limited scaling and sensitivity to define such manifolds. Here we introduce Metacell-2, a recursive divide and conquer algorithm allowing efficient decomposition of scRNA-seq datasets of any size into small and cohesive groups of cells denoted as metacells. We show the algorithm outperforms current solutions in time, memory and quality. Importantly, Metacell-2 also improves outlier cell detection and rare cell type identification, as we exemplify by analysis of human bone marrow cell atlas and mouse embryonic data. Metacell-2 is implemented over the scanpy framework for easy integration in any analysis pipeline.

Classification of Human Activity Recognition Utilizing Smartphone Data of CNN-LSTM

Human activity recognition has been applied in various areas of life by utilizing the gyroscope and accelerometer sensors embedded in smartphones. One of the functions of recognizing human activities is by understanding the pattern of human activity, thereby minimizing the possibility of unexpected incidents. This study classified of human activity recognition through CNN-LSTM on the UCI HAR dataset by applying the divide and conquer algorithm. This study additionally employs tuning hyperparameter to obtain the best accuracy value from the parameters and the proposed architecture. From the test results with the CNN-LSTM method, the accuracy rate for dynamic activity is 99.35%, for static activity is 96.08%, and the combination of the two models is 97.62%.

A divide-and-conquer algorithm for quantum state preparation

Advantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O(N) to load an N-dimensional vector. Here, we show that it is possible to load an N-dimensional vector with exponential time advantage using a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices.

An Effective Algorithm to Find a Cost Minimizing Gateway Deployment for Node-Replaceable Wireless Sensor Networks

In this paper, we present an efficient way to find a gateway deployment for a given sensor network topology. We assume that the expired sensors and gateways can be replaced and the locations of the gateways are chosen among the given sensor nodes. The objective is to find a gateway deployment that minimizes the cost per unit time, which consists of the maintenance and installation costs. The proposed algorithm creates a cost reference and uses it to find the optimal deployment via a divide and conquer algorithm. Comparing all cases is the most reliable way to find the optimal gateway deployment, but this is practically impossible to calculate, since its computation time increases exponentially as the number of nodes increases. The method we propose increases linearly, and so is suitable for large scale networks. Additionally, compared to stochastic algorithms such as the genetic algorithm, this methodology has advantages in computational speed and accuracy for a large number of nodes. We also verify our methodology through several numerical experiments.