There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the relative Pearson divergence as a divergence measure, and it is accurately and efficiently estimated by a method of direct density-ratio estimation. Through experiments on artificial and real-world datasets including human-activity sensing, speech, and Twitter messages, we demonstrate the usefulness of the proposed method.

Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation (情報論的学習理論と機械学習)

High-level vision: Object recognition and visual cognition, Shimon Ullman. MIT Press, Bradford (1996), ISBN 0 262 21013 4

Synthetic data is an increasingly popular tool for training deep learning models, especially in computer vision but also in other areas. In this work, we attempt to provide a comprehensive survey of the various directions in the development and application of synthetic data. First, we discuss synthetic datasets for basic computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, simulation environments for robotics, applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more); we also survey the work on improving synthetic data development and alternative ways to produce it such as GANs. Second, we discuss in detail the synthetic-to-real domain adaptation problem that inevitably arises in applications of synthetic data, including synthetic-to-real refinement with GAN-based models and domain adaptation at the feature/model level without explicit data transformations. Third, we turn to privacy-related applications of synthetic data and review the work on generating synthetic datasets with differential privacy guarantees. We conclude by highlighting the most promising directions for further work in synthetic data studies.

Synthetic Data for Deep Learning

Loss of the ability to speak or hear exerts psychological and social impacts on the affected persons due to the lack of proper communication. Multiple and systematic scholarly interventions that vary according to context have been implemented to overcome disability-related difficulties. Sign language recognition (SLR) systems based on sensory gloves are significant innovations that aim to procure data on the shape or movement of the human hand. Innovative technology for this matter is mainly restricted and dispersed. The available trends and gaps should be explored in this research approach to provide valuable insights into technological environments. Thus, a review is conducted to create a coherent taxonomy to describe the latest research divided into four main categories: development, framework, other hand gesture recognition, and reviews and surveys. Then, we conduct analyses of the glove systems for SLR device characteristics, develop a roadmap for technology evolution, discuss its limitations, and provide valuable insights into technological environments. This will help researchers to understand the current options and gaps in this area, thus contributing to this line of research.

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017.

Sign Language Recognition Application Systems for Deaf-Mute People

Analysis of Gamification Models in Education Using MDA Framework

Classification of Land Use/Land Cover (LULC) data is a typical task in remote-sensing domain. However, because the classes distribution in LULC data is naturally imbalance, it is difficult to do the classification. In this paper, we employ Variational Semi-Supervised Learning (VSSL) to solve imbalance problem in LULC of Jakarta City. This VSSL exploits the use of semi-supervised learning on deep learning model. Therefore, it is suitable for classifying data with abundant unlabeled like LULC. The result shows that VSSL achieves 80.17% of overall accuracy, outperforming other algorithms in comparison.

/pdf/classification-of-imbalanced-land-use-land-cover-data-using-2bou94w13r.pdf

Classification of imbalanced land-use/land-cover data using variational semi-supervised learning

Sleep quality is an important factor for human physical and mental health, day-time performance, and safety. Sufficient sleep quality can reduce risk of chronic disease and mental depression. Sleep helps brain to work properly that can improve productivity and prevent accident because of falling asleep. In order to analyze the sleep quality, reliable continuous monitoring system is required. The emergence of internet-of-things technology has provided a promising opportunity to build a reliable sleep quality monitoring system by leveraging the rapid improvement of sensor and mobile technology. This paper presents the literature study about internet of things for sleep quality monitoring systems. The study is started from the review of sleep quality problem, the importance of sleep quality monitoring, the enabling internet of things technology, and the open issues in this field. Finally, our future research plan for sleep apnea monitoring is presented.

/pdf/internet-of-things-for-sleep-quality-monitoring-system-a-1h3tmpi4mz.pdf

Internet of things for sleep quality monitoring system: A survey

Article history: Received: 25 August, 2020 Accepted: 21 October, 2020 Online: 10 November, 2020 Facial emotion recognition is one among many popular and challenging tasks in the field of computer vision. Numerous researches have been conducted on this task and each proposed either standaloneor ensemble-based processing technique. While many researches strive for better accuracy, this research also attempts to increase the processing efficiency of computer correctly classifying human emotions based on human face by utilizing a single standalone-based neural network. This research proposes the use of standalone-based modified Convolutional Neural Network (CNN) based on Visual Geometry Group – 16 (VGG-16) classification model which was pretrained on ImageNet dataset and fine-tuned for emotion classification. The classification is performed on the publicly available FER-2013 dataset of over 35,000 face images with in-the-wild setting for 7 distinct emotions with the provided 80% training, 10% validation, and 10% testing data distributions. The proposed approach outperforms most standalone-based model results with 69.40% accuracy.

/pdf/emotion-recognition-on-fer-2013-face-images-using-fine-tuned-1grly0a5hj.pdf

Gede Putra Kusuma

Papers

Sign Language Recognition Application Systems for Deaf-Mute People

Analysis of Gamification Models in Education Using MDA Framework

Classification of imbalanced land-use/land-cover data using variational semi-supervised learning

Internet of things for sleep quality monitoring system: A survey

Emotion Recognition on FER-2013 Face Images Using Fine-Tuned VGG-16