scholarly journals A Review on Ontology Learning Approaches of Creating a Topic Map of Cybercrime Research

2019 ◽  
Vol 18 ◽  
pp. 7451-7469
Author(s):  
Kijung Lee
Keyword(s):  
Academic Research ◽  
Relevant Information ◽  
Research Field ◽  
Information Resources ◽  
Research Review ◽  
Learning Approaches ◽  
Ontology Learning ◽  
Ongoing Research ◽  
Research Issues ◽  
Topic Map

Conducting an academic research requires getting a firm grasp of ongoing research issues as well as locating research materials effectively. Often research in different fields on a similar topic can assume diverse approaches due to different objectives and research goals in their own fields. Especially in an interdisciplinary research field like cybercrime, many research topics overlap with those of other research fields. Researchers in such a field, therefore, can benefit from understanding the related domains of one’s own research.  Topic maps provide methods for understanding research domain and managing relevant information resources at the same time. In this paper, we review a topic map solution to acquire knowledge structure and to locate information resources effectively. We address current problems of cybercrime research, review previous studies that use automated methods for topic map creation, and examine existing sets of methods for automatically extracting topic map components. Especially, the methods we discuss here are text mining techniques for extracting ontology components, denoted as ontology learning.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

scholarly journals Past, present, and future of smart learning: a topic-based bibliometric analysis

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Xieling Chen ◽  
Di Zou ◽  
Haoran Xie ◽  
Fu Lee Wang
Keyword(s):  
Cloud Computing ◽  
Blended Learning ◽  
Information And Communication Technologies ◽  
Content Management ◽  
Research Field ◽  
Smart Devices ◽  
Major Research ◽  
Research Issues ◽  
Course Content ◽  
Smart Learning

AbstractInnovative information and communication technologies have reformed higher education from the traditional way to smart learning. Smart learning applies technological and social developments and facilitates effective personalized learning with innovative technologies, especially smart devices and online technologies. Smart learning has attracted increasing research interest from the academia. This study aims to comprehensively review the research field of smart learning by conducting a topic modeling analysis of 555 smart learning publications collected from the Scopus database. In particular, it seeks answers to (1) what the major research topics concerning smart learning were, and (2) how these topics evolved. Results demonstrate several major research issues, for example, Interactive and multimedia learning, STEM (science, technology, engineering, and mathematics) education, Attendance and attention recognition, Blended learning for smart learning, and Affective and biometric computing. Furthermore, several emerging topics were identified, for example, Smart learning analytics, Software engineering for e-learning systems, IoT (Internet of things) and cloud computing, and STEM education. Additionally, potential inter-topic directions were highlighted, for instance, Attendance and attention recognition and IoT and cloud computing, Semantics and ontology and Mobile learning, Feedback and assessment and MOOCs (massive open online courses) and course content management, as well as Blended learning for smart learning and Ecosystem and ambient intelligence.

scholarly journals Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

2021 ◽  
Vol 13 (16) ◽  
pp. 3065
Author(s):  
Libo Wang ◽  
Rui Li ◽  
Dongzhi Wang ◽  
Chenxi Duan ◽  
Teng Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Texture Features ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Research Field ◽  
Learning Approaches ◽  
Fine Grained ◽  
Urban Scene ◽  
Fine Resolution ◽  
With Memory

Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classification, urban planning, etc. However, the tremendous details contained in the VFR image, especially the considerable variations in scale and appearance of objects, severely limit the potential of the existing deep learning approaches. Addressing such issues represents a promising research field in the remote sensing community, which paves the way for scene-level landscape pattern analysis and decision making. In this paper, we propose a Bilateral Awareness Network which contains a dependency path and a texture path to fully capture the long-range relationships and fine-grained details in VFR images. Specifically, the dependency path is conducted based on the ResT, a novel Transformer backbone with memory-efficient multi-head self-attention, while the texture path is built on the stacked convolution operation. In addition, using the linear attention mechanism, a feature aggregation module is designed to effectively fuse the dependency features and texture features. Extensive experiments conducted on the three large-scale urban scene image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid dataset, demonstrate the effectiveness of our BANet. Specifically, a 64.6% mIoU is achieved on the UAVid dataset.

scholarly journals Reinforcement Learning Approaches in Social Robotics

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1292
Author(s):  
Neziha Akalin ◽  
Amy Loutfi
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Social Robotics ◽  
Research Field ◽  
Social Robots ◽  
Learning Approaches ◽  
Reward Function ◽  
Optimal Behavior ◽  
Learning Challenges ◽  
Starting Point

This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field.

scholarly journals Clustering of Maxima: Spatial Dependencies among Heavy Rainfall in France

2013 ◽  
Vol 26 (20) ◽  
pp. 7929-7937 ◽  
Author(s):  
Elsa Bernard ◽  
Philippe Naveau ◽  
Mathieu Vrac ◽  
Olivier Mestre
Keyword(s):  
Spatial Patterns ◽  
Extreme Value Theory ◽  
Heavy Rainfall ◽  
Spatial Clustering ◽  
Relevant Information ◽  
Value Theory ◽  
Research Field ◽  
Multivariate Extreme Value Theory ◽  
Spatial Dependencies ◽  
The Mean

Abstract One of the main objectives of statistical climatology is to extract relevant information hidden in complex spatial–temporal climatological datasets. To identify spatial patterns, most well-known statistical techniques are based on the concept of intra- and intercluster variances (like the k-means algorithm or EOFs). As analyzing quantitative extremes like heavy rainfall has become more and more prevalent for climatologists and hydrologists during these last decades, finding spatial patterns with methods based on deviations from the mean (i.e., variances) may not be the most appropriate strategy in this context of studying such extremes. For practitioners, simple and fast clustering tools tailored for extremes have been lacking. A possible avenue to bridging this methodological gap resides in taking advantage of multivariate extreme value theory, a well-developed research field in probability, and to adapt it to the context of spatial clustering. In this paper, a novel algorithm based on this plan is proposed and studied. The approach is compared and discussed with respect to the classical k-means algorithm throughout the analysis of weekly maxima of hourly precipitation recorded in France (fall season, 92 stations, 1993–2011).

Application of Community Discovery in SNS Scientific Paper Management Platform

2011 ◽  
Vol 267 ◽  
pp. 247-252
Author(s):  
Rui Xin Ma ◽  
Gui Shi Deng ◽  
Xiao Wang
Keyword(s):  
Virtual Communities ◽  
Academic Research ◽  
Scientific Paper ◽  
Research Field ◽  
Collaborative Relationship ◽  
Community Discovery ◽  
Management Platform ◽  
Accurate Relation

SNS provides us with a brand new platform to communicate, interact and share. To better suit the need of scholars to get more authoritative and more satisfactory information about academic research, we construct a SNS scientific paper management platform. In this platform, scholars are divided into different virtual communities accord to their research field and their collaborative relationship with others. Ideas in CF are applied in the procedure of community division which helps us to find the accurate relation structures. At the end of this paper, we use compare the running results of normal platform and SNS to illustrate how useful it is.

A Qualitative Analysis of Factors Affecting the Successful Completion of Postgraduate Studies

2021 ◽  
Vol 12 (3) ◽  
pp. 152-161
Author(s):  
Abdul Munir Ismail Et.al
Keyword(s):  
Interpersonal Relationships ◽  
Relevant Information ◽  
Learning Approaches ◽  
Successful Completion ◽  
Structured Interviews ◽  
Postgraduate Students ◽  
Factors Affecting ◽  
Face To Face ◽  
Postgraduate Studies ◽  
Research Findings

The study aims to highlight the current learning approaches used by postgraduate students to complete their postgraduate studies on time, as studies have shown many students have failed to finish their studies as planned. In particular, this study focuses on factors and methods that are perceived to be most effective by students to help them complete their studies on time.  Methodology: Thisstudy was based on a qualitative approach involving semi-structured interviews. The study sample consisted of 14 postgraduate students and one lecturers as respondents. The research instrument was based on interview questions to elicit relevant information on their demography and learning practices. Data were collected and organized into four themes and were subsequently analyzed descriptively.     Findings: The findings showed that face-to-face discussions were the most popular practice adopted by the respondents. The findings also showed several factors had significant impacts on student learning, such as interpersonal relationships between students and supervisors, commitment, financial commitment, and moral support, which needs to be taken into account in helping students to complete on time.     Significance: The research findings can inform all the stakeholders, notably students, supervisors, and administrative officers, factors that have profound impacts on postgraduate students’ efforts to graduate on time.

scholarly journals Frontiers in Vitamin Research: New Antibodies, New Data

2011 ◽  
Vol 11 ◽  
pp. 1226-1242 ◽  
Author(s):  
Rafael Coveñas ◽  
Arturo Mangas ◽  
Dominique Bodet ◽  
Sébastien Duleu ◽  
Pilar Marcos ◽  
...  
Keyword(s):  
Folic Acid ◽  
Vitamin C ◽  
Research Field ◽  
General Idea ◽  
Hypothalamic Nucleus ◽  
Ongoing Research ◽  
Hypothalamic Region ◽  
Anatomical Distribution ◽  
Monkey Brain ◽  
Immunohistochemical Techniques

Since 2004, the anatomical distribution of vitamins in the monkey brain, studied using immunohistochemical techniques and new tools (specific antisera that discriminate different vitamins reasonably well), has been an ongoing research field. The visualization of immunoreactive structures containing vitamins (folic acid, riboflavin, thiamine, pyridoxal, and vitamin C) has recently been reported in the monkey brain (Macaca fascicularis), all these vitamins showing a restricted or very restricted distribution. Folic acid, thiamine, and riboflavin have only been observed in immunoreactive fibers, vitamin C has only been found in cell bodies (located in the primary somatosensory cortex), and pyridoxal has been found in both fibers and cell bodies. Perikarya containing pyridoxal have been observed in the paraventricular hypothalamic nucleus, the periventricular hypothalamic region, and in the supraoptic nucleus. The fibers containing vitamins are thick, smooth (without varicosities), and are of medium length or long, whereas immunoreactive cell bodies containing vitamins are round or triangular. At present, there are insufficient data to elucidate the roles played by vitamins in the brain, but the anatomical distribution of these compounds in the monkey brain provides a general idea (although imprecise and requiring much more study) about the possible functional implications of these molecules. In this sense, here the possible functional roles played by vitamins are discussed.

Research Approaches in Computer-Supported Collaborative Learning

2013 ◽  
Vol 9 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Tharrenos Bratitsis ◽  
Stavros Demetriadis
Keyword(s):  
Collaborative Learning ◽  
Research Field ◽  
Computer Supported Collaborative Learning ◽  
Research Issues ◽  
Online Distance Education ◽  
Theoretical Perspectives ◽  
History Of ◽  
How People Learn ◽  
Online Distance ◽  
New Research

Computer Supported Collaborative Learning (CSCL) is concerned with how people learn when working and interacting in groups with the assistance of ICTs. The field involves collaboration, computer mediation, online – distance education which raises interesting theoretical considerations regarding the actual studying of learning within CSCL settings. Being a rather interdisciplinary research field in nature, it has a long history of controversy about its theory, methods, and definition. In this editorial, through a quick review of the literature the diversity of issues examined under the CSCL research field becomes obvious. Moreover, an attempt to categorize these research issues is made. In this vein, the four interesting contributions of this Special Issue, regarding theoretical perspectives and issues of research of the field, are introduced. They comply with the distinguished categories, but they open new research borders as well.

scholarly journals Turning chemistry education on its head: Design, experience and evaluation of a learning-centred ‘Modern Chemistry’ subject

2020 ◽  
Vol 17 (3) ◽  
pp. 179-194
Author(s):  
Ingo Koeper ◽  
◽  
Joe Shapter ◽  
Vanessa North ◽  
Don Houston ◽  
...  
Keyword(s):  
Flipped Classroom ◽  
Chemistry Education ◽  
Academic Staff ◽  
Analytical Techniques ◽  
Relevant Information ◽  
Initial Assessment ◽  
First Year ◽  
Learning Approaches ◽  
Structured Interviews ◽  
Final Exam

In science courses in general, but especially in first year chemistry classes, the amount of content that is delivered is often overwhelming and too complex for the student to easily cope with. Students not only have to gain knowledge in a variety of different field, they also have to learn new laboratory skills and analytical techniques. Additionally, there is an issue with more and more information being available to everybody through the internet, while our education often still focusses on delivering that knowledge, rather than exploring ways how students can be guided to understanding and using the knowledge provided. There have been different approaches on how to make ‘dry’ scientific concepts more interesting and how enhance student engagement, ranging from problem-based learning approaches, case studies or flipped classroom models. We have recently turned a fairly classic first year chemistry course on its head. In the new structure, students are gaining knowledge and understanding purely through the completion of a range of challenges. We have removed all lectures, tutorials and the final exam, and all interaction with the student happens in the laboratory. Throughout the semester, students attempt to complete a range of challenges, both theoretical and practical, find relevant information, propose approaches to solving the challenges, and discuss these and subsequent outcomes with academic staff. In order to analyse the design, we have conducted structured interviews with students from 2016-2018. Initial assessment of the data suggests a high level of engagement of the students, paired with a better preparation of students for their subsequent studies. Students enjoyed having the freedom to choose and design their own experiments. Additionally, students improved significantly in non-content related aspects such as timemanagement, organisation, planning and self-learning, with notable impact on their learning in higher years.

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