149,771 research outputs found
GinJinn: An objectâdetection pipeline for automated feature extraction from herbarium specimens
Premise
The generation of morphological data in evolutionary, taxonomic, and ecological studies of plants using herbarium material has traditionally been a laborâintensive task. Recent progress in machine learning using deep artificial neural networks (deep learning) for image classification and object detection has facilitated the establishment of a pipeline for the automatic recognition and extraction of relevant structures in images of herbarium specimens.
Methods and Results
We implemented an extendable pipeline based on stateâofâtheâart deepâlearning objectâdetection methods to collect leaf images from herbarium specimens of two species of the genus Leucanthemum . Using 183 specimens as the training data set, our pipeline extracted one or more intact leaves in 95% of the 61 test images.
Conclusions
We establish GinJinn as a deepâlearning objectâdetection tool for the automatic recognition and extraction of individual leaves or other structures from herbarium specimens. Our pipeline offers greater flexibility and a lower entrance barrier than previous imageâprocessing approaches based on handâcrafted features
A Survey on Compiler Autotuning using Machine Learning
Since the mid-1990s, researchers have been trying to use machine-learning
based approaches to solve a number of different compiler optimization problems.
These techniques primarily enhance the quality of the obtained results and,
more importantly, make it feasible to tackle two main compiler optimization
problems: optimization selection (choosing which optimizations to apply) and
phase-ordering (choosing the order of applying optimizations). The compiler
optimization space continues to grow due to the advancement of applications,
increasing number of compiler optimizations, and new target architectures.
Generic optimization passes in compilers cannot fully leverage newly introduced
optimizations and, therefore, cannot keep up with the pace of increasing
options. This survey summarizes and classifies the recent advances in using
machine learning for the compiler optimization field, particularly on the two
major problems of (1) selecting the best optimizations and (2) the
phase-ordering of optimizations. The survey highlights the approaches taken so
far, the obtained results, the fine-grain classification among different
approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our
Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated
quarterly here (Send me your new published papers to be added in the
subsequent version) History: Received November 2016; Revised August 2017;
Revised February 2018; Accepted March 2018
Automatic generation of hardware Tree Classifiers
Machine Learning is growing in popularity and spreading across different fields for various applications. Due to this trend, machine learning algorithms use different hardware platforms and are being experimented to obtain high test accuracy and throughput. FPGAs are well-suited hardware platform for machine learning because of its re-programmability and lower power consumption. Programming using FPGAs for machine learning algorithms requires substantial engineering time and effort compared to software implementation. We propose a software assisted design flow to program FPGA for machine learning algorithms using our hardware library. The hardware library is highly parameterized and it accommodates Tree Classifiers. As of now, our library consists of the components required to implement decision trees and random forests. The whole automation is wrapped around using a python script which takes you from the first step of having a dataset and design choices to the last step of having a hardware descriptive code for the trained machine learning model
Identifying Web Tables - Supporting a Neglected Type of Content on the Web
The abundance of the data in the Internet facilitates the improvement of
extraction and processing tools. The trend in the open data publishing
encourages the adoption of structured formats like CSV and RDF. However, there
is still a plethora of unstructured data on the Web which we assume contain
semantics. For this reason, we propose an approach to derive semantics from web
tables which are still the most popular publishing tool on the Web. The paper
also discusses methods and services of unstructured data extraction and
processing as well as machine learning techniques to enhance such a workflow.
The eventual result is a framework to process, publish and visualize linked
open data. The software enables tables extraction from various open data
sources in the HTML format and an automatic export to the RDF format making the
data linked. The paper also gives the evaluation of machine learning techniques
in conjunction with string similarity functions to be applied in a tables
recognition task.Comment: 9 pages, 4 figure
DL-Droid: Deep learning based android malware detection using real devices
open access articleThe Android operating system has been the most popular for smartphones and tablets since 2012. This popularity has led to a rapid raise of Android malware in recent years. The sophistication of Android malware obfuscation and detection avoidance methods have significantly improved, making many traditional malware detection methods obsolete. In this paper, we propose DL-Droid, a deep learning system to detect malicious Android applications through dynamic analysis using stateful input generation. Experiments performed with over 30,000 applications (benign and malware) on real devices are presented. Furthermore, experiments were also conducted to compare the detection performance and code coverage of the stateful input generation method with the commonly used stateless approach using the deep learning system. Our study reveals that DL-Droid can achieve up to 97.8% detection rate (with dynamic features only) and 99.6% detection rate (with dynamic + static features) respectively which outperforms traditional machine learning techniques. Furthermore, the results highlight the significance of enhanced input generation for dynamic analysis as DL-Droid with the state-based input generation is shown to outperform the existing state-of-the-art approaches
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