Intelligent Hotel ROS-based Service Robot

With the advances of artificial intelligence (AI) technology, many studies and work have been carried out on how robots could replace human labor. In this paper, we present a ROS based intelligence hotel robot, which simplifies the check-in process. We use pioneer 3dx robot and considered different environment settings. The robot combined with Hokuyo Lidar and Kinect Xbox camera, can plan the routes accurately and reach rooms in different floors. In addition, we added an intelligent voice system which provides an assistant for the customers

An Implementation of the HDBSCAN* Clustering Algorithm

An implementation of the HDBSCAN* clustering algorithm, Tribuo Hdbscan, is presented in this work. The implementation is developed as a new feature of the Java machine learning library Tribuo. This implementation leverages concurrency and achieves better performance than the reference Java implementation. Tribuo Hdbscan provides prediction functionality, which is a novel technique to make fast predictions for unseen data points using an HDBSCAN* clustering model. Tribuo Hdbscan cluster results and performance measurements are also compared with the state-of-the-art HDBSCAN* implementation, the Python module hdbscan

Multivariate and Dimensionality-Reduction-Based Machine Learning Techniques for Tumor Classification of RNA-Seq Data

Cancer, a genetic disease, is considered one of the leading causes of death globally and affects people of all ages. Ribonucleic acid sequencing (RNA-Seq) is a technique used to quantify the expression of genes of interest and can be used to classify cancer tumor types. This paper describes a machine learning technique to classify cancer tissue samples by tumor type, such as breast cancer, lung cancer, colon cancer, and others. More than 60,000 RNA-Seq features were analyzed using six different machine learning classification algorithms, both individually and as an ensemble. Numerous dimensionality reduction techniques addressed the challenges of working with enormous amounts of genetic data. In particular, we were able to reduce the number of features from over 60,000 to 660 in the random forest feature selection and to 68 factor features using factor analysis with an accuracy of 99% in classifying tumor types

Automatic Detection of Clickbait Headlines Using Semantic Analysis and Machine Learning Techniques

Clickbait headlines are misleading headiness designed to attract attention and entice users to click on the link. Links can host malware, trojans and phishing attacks. Clickbaiting is one of the more subtle methods used by hackers and scammers. For these reasons, clickbait is a serious issue that must be addressed. This paper presents a method for identifying clickbait headlines using semantic analysis and machine learning techniques. The method involves analyzing thirty unique semantic features and exploring six different machine learning classification algorithms individually and in ensemble forms. Results show that the top models have an accuracy of 98% in classifying clickbait headlines. The proposed models can serve as a template for developing practical applications to detect clickbait headlines automatically

Using NLP for Fact Checking: A Survey

In recent years, disinformation and “fake news” have been spreading throughout the internet at rates never seen before. This has created the need for fact-checking organizations, groups that seek out claims and comment on their veracity, to spawn worldwide to stem the tide of misinformation. However, even with the many human-powered fact-checking organizations that are currently in operation, disinformation continues to run rampant throughout the Web, and the existing organizations are unable to keep up. This paper discusses in detail recent advances in computer science to use natural language processing to automate fact checking. It follows the entire process of automated fact checking using natural language processing, from detecting claims to fact checking to outputting results. In summary, automated fact checking works well in some cases, though generalized fact checking still needs improvement prior to widespread use

Discovering Community Structure in Multiplex Networks via a Co-Regularized Robust Tensor-Based Spectral Approach

Complex networks arise in various fields, such as biology, sociology and communication, to model interactions among entities. Entities in many real-world systems exhibit different types of interactions, which requires modeling these type of systems properly. Multiplex networks are used to model these systems, as they can reflect the nodes’ pair-wise interactions as multiple distinct types of links across layers. Community detection is a widely studied application in network analysis as it provides insights into the structure and organization of the network. Even though multiple algorithms have been developed in the community detection field, many of them have a limited performance in the presence of noise. In this article, we develop a novel algorithm that combines tensor low-rank representation, spectral clustering and distance regularization to improve the accuracy in discovering communities in multiplex networks. The low-rank representation leads to reducing the noise and errors existing in the network and the optimization of an accurate consensus set of eigenvectors that reveals the communities in the network. Moreover, the proposed approach balances the agreement between the eigenvectors of each layer, i.e., individual subspaces, and the consensus set of eigenvectors, i.e., common subspaces, by minimizing the projection distance between them. The common and individual subspaces are computed efficiently through Tucker decomposition and modified spectral clustering, respectively. Finally, multiple experiments are conducted on real and simulated networks to evaluate the proposed approach and compare it to state-of-the-art algorithms. The proposed approach shows its robustness and efficiency in discovering the communities in multiplex networks