A case study in authorship attribution: The Mondrigo1

In this paper, we present authorship attribution methods applied to ¡El Mondrigo! (1968), a controversial text supposedly created by order of the Mexican Government to defame a student strike. Up to now, although the authorship of the book has been attributed to several journalists and writers, it could not be demonstrated and remains an open problem. The work aims at establishing which one of the most commonly attributed writers is the real author. To do that, we implement methods based on stylometric features using textual distance, supervised, and unsupervised learning. The distance-based methods implemented in this work are Kilgarriff and Delta of Burrows, an SVM algorithm is used as the supervised method, and the k-means algorithm as the unsupervised algorithm. The applied methods were consistent by pointing out a single author as the most likely one.

Combining Supervised and Unsupervised Learning Algorithms for Human Activity Recognition

Human activity recognition is an extensively researched topic in the last decade. Recent methods employ supervised and unsupervised deep learning techniques in which spatial and temporal dependency is modeled. This paper proposes a novel approach for human activity recognition using skeleton data. The method combines supervised and unsupervised learning algorithms in order to provide qualitative results and performance in real time. The proposed method involves a two-stage framework: the first stage applies an unsupervised clustering technique to group up activities based on their similarity, while the second stage classifies data assigned to each group using graph convolutional networks. Different clustering techniques and data augmentation strategies are explored for improving the training process. The results were compared against the state of the art methods and the proposed model achieved 90.22% Top-1 accuracy performance for NTU-RGB+D dataset (the performance was increased by approximately 9% compared with the baseline graph convolutional method). Moreover, inference time and total number of parameters stay within the same magnitude order. Extending the initial set of activities with additional classes is fast and robust, since there is no required retraining of the entire architecture but only to retrain the cluster to which the activity is assigned.

A Simplistic Overview of Machine Learning

While dealing with machine learning, a computer learns first to perform a roles/task by learning a set of training examples. The computer performs then the same task along with data it hasn't found before. This paper presents a brief overview of machine-learning types along with instances. The paper also covers differences between supervised and unsupervised learning.

Predicting running time of aerodynamic jobs in HPC system by combining supervised and unsupervised learning method

Improving resource utilization is an important goal of high-performance computing systems of supercomputing centers. To meet this goal, the job scheduler of high-performance computing systems often uses backfilling scheduling to fill short-time jobs into job gaps at the front of the queue. Backfilling scheduling needs to obtain the running time of the job. In the past, the job running time is usually given by users and often far exceeded the actual running time of the job, which leads to inaccurate backfilling and a waste of computing resources. In particular, when the predicted job running time is lower than the actual time, the damage caused to the utilization of the system’s computing resources becomes more serious. Therefore, the prediction accuracy of the job running time is crucial to the utilization of system resources. The use of machine learning methods can make more accurate predictions of the job running time. Aiming at the parallel application of aerodynamics, we propose a job running time prediction framework SU combining supervised and unsupervised learning and verify it on the real historical data of the high-performance computing systems of China Aerodynamics Research and Development Center (CARDC). The experimental results show that SU has a high prediction accuracy (80.46%) and a low underestimation rate (24.85%).