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Precise positioning plays a key role in successful navigation of autonomous vehicles. A fusion architecture of Global Positioning System (GPS) and Laser-SLAM (Simultaneous Localization and Mapping) is widely adopted. While Laser-SLAM is known for its highly accurate localization, GPS is still required to overcome accumulated error and give SLAM a required reference coordinate. However, there are multiple...
The increased popularity of Massive Open Online Courses (MOOC) and e-learning has constantly increased video-based online education platforms. There are also many video lectures for software engineering education in online education platforms. Although online lectures have many advantages, there are also limitations. We performed a verification research to see if high frequency words can detect mind...
Based on the current situation and development trend of small and medium - sized libraries in China, this paper analyzes the main problems existing in the current public library construction system based on the new demand of Lib2.0 era.; The data and indicators of the current public library construction are often determined for large-scale city-level libraries, which is lack of hierarchical research,...
The importance of functional status information (FSI) has become increasingly evident in recent years [1, 2]. However, implementation, application, and normalization of FSI in health care and Electronic Health Records (EHRs) have been largely underexplored. The World Health Organization's International Classification of Functioning, Disability and Health (ICF) [3] is considered to be the international...
Most physical activity (PA) assessment studies use wearable accelerometers attached to the hip. However, there is significant and recent interest in understanding the usefulness of wrist based accelerometer data collection due to ease of use and higher compliance. This paper develops machine learning methods for identifying activity types and computing energy expenditures. Our approach converts the...
Big data analysis has been pervasively adopted as a method to analyze the tremendous amount of daily generated high throughput data in an efficient and accurate manner. Among the series of tools available in the field of big biomedical data, correlation networks are one of the most powerful tools for modelling gene expression, which is important in the study of disease and ageing. With the help of...
This paper presents detailed anomaly detection evaluation on operational time-series data of Internet of Things (IoT) based household devices in general and Heating, Ventilation and Air Conditioning (HVAC) systems in specific. Due to the number of issues observed during evaluation of widely used distance-based, statistical-based, and cluster-based anomaly detection techniques, we also present a pattern-based...
The paper considers the problem of feature selection in learning using privileged information (LUPI), where some of the features (referred to as privileged ones) are only available for training, while being absent for test data. In the latest implementation of LUPI, these privileged features are approximated using regressions constructed on standard data features, but this approach could lead to polluting...
Following the conventions developed in general domain, most of the current work on clinical temporal relation identification aims to identify a comprehensive set of temporal relations from source documents. This includes both explicit relations that is described in the documents and implicit relations that are identifiable only through inference. Although such an approach may provide a complete view...
Generative models are used in an increasing number of applications that rely on large amounts of contextually rich information about individuals. Owing to possible privacy violations, however, publishing or sharing generative models is not always viable. In this paper, we introduce a novel solution for privately releasing generative models and entire high-dimensional datasets produced by these models...
One of the most current challenging problems in Gaussian process regression (GPR) is to handle large-scale datasets and to accommodate an online learning setting where data arrive irregularly on the fly. In this paper, we introduce a novel online Gaussian process model that could scale with massive datasets. Our approach is formulated based on alternative representation of the Gaussian process under...
This study evaluates the current state of wireless network security in the CBD of Nuku'alofa. Wireless Networks have grown significantly since their introduction. Security protocols supplied with the wireless equipment have reached a level of strong and robust security. However, security remains a problem; this study aims to answer two questions. What is the status of wireless network growth and security...
Subgroup discovery is a local pattern mining technique to find interpretable descriptions of sub-populations that stand out on a given target variable. That is, these sub-populations are exceptional with regard to the global distribution. In this paper we argue that in many applications, such as scientific discovery, subgroups are only useful if they are additionally representative of the global distribution...
In order to solve the self-localization problem, the Monte-Carlo Localization is proposed as a technique which is able to solve any localization problem. However, the implemented algorithm needs to be adapted to the robot and domain. Thus, this work presents a novel implementation for humanoid robots whose main sensory input is a camera, to be used in the domain of RoboCup Humanoid Soccer League....
In the face of energy crisis and environmental pollution problems, all the countries in the world vigorously promote the development of electric vehicles. Electric vehicle charging infrastructure is the necessary infrastructure to the development of electric vehicles. This work has been focused on Electric Vehicles' Charging Stations (CSs) deployment. It has been considered in this study several constraints...
This paper discusses the open issues in incorporating trust techniques in the NFV environment specified by the ETSI NFV Industry Specification Group, and analyses the available technologies to fill this gap. ETSI is developing security and trust specifications within its NFV-SEC working group, with the aim of establishing and assessing trust of both the hardware platform and the virtualised infrastructure...
A 25–28Gb/s full-rate reference-less CDR is presented and designed in a standard 0.13μm SiGe BiCMOS process, which can be applicable to nearly all classical 100G communication protocols by multi-channel configuration. It consists mainly of a full-rate phase frequency detector, a quadrature voltage control oscillator and two voltage to current convertors with loop filters. A dual-loop topology is adopted...
With the steady increase of offered cloud storage services, they became a popular alternative to local storage systems. Beside several benefits, the usage of cloud storage services can offer, they have also some downsides like potential vendor lock-in or unavailability. Different pricing models, storage technologies and changing storage requirements are further complicating the selection of the best...
Code bloat is a phenomenon in Genetic Programming (GP) that increases the size of individuals during the evolutionary process. Over the years, there has been a large number of research that attempted to address this problem. In this paper, we propose a new method to control code bloat and reduce the complexity of the solutions in GP. The proposed method is called Substituting a subtree with an Approximate...
A recent trend in multiobjective evolutionary algorithms is to increase the population size to approximate the Pareto front with high accuracy. On the other hand, the NSGA-II algorithm widely used in multiobjective optimization performs nondominated sorting in solution ranking, which means an increase in computational complexity proportional to the square of the population. This execution time becomes...
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