scholarly journals A review of collective robotic construction

2019 ◽  
Vol 4 (28) ◽  
pp. eaau8479 ◽  
Author(s):  
Kirstin H. Petersen ◽  
Nils Napp ◽  
Robert Stuart-Smith ◽  
Daniela Rus ◽  
Mirko Kovac
Keyword(s):  
Architectural Design ◽  
Large Scale ◽  
Performance Metrics ◽  
Research Area ◽  
Sustainable Construction ◽  
Shared Environment ◽  
Comprehensive Overview ◽  
Active Research ◽  
High Level ◽  
Robotic Construction

The increasing need for safe, inexpensive, and sustainable construction, combined with novel technological enablers, has made large-scale construction by robot teams an active research area. Collective robotic construction (CRC) specifically concerns embodied, autonomous, multirobot systems that modify a shared environment according to high-level user-specified goals. CRC tightly integrates architectural design, the construction process, mechanisms, and control to achieve scalability and adaptability. This review gives a comprehensive overview of research trends, open questions, and performance metrics.

scholarly journals Wireless sensor node localization

2012 ◽  
Vol 370 (1958) ◽  
pp. 85-99 ◽  
Author(s):  
Ákos Lédeczi ◽  
Miklós Maróti
Keyword(s):  
Research Area ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Node ◽  
Node Localization ◽  
Comprehensive Overview ◽  
Positioning Systems ◽  
Active Research ◽  
High Level ◽  
Physical Phenomena

For most wireless sensor network (WSN) applications, the positions of the sensor nodes need to be known. Global positioning systems have not fitted into WSNs very well owing to their price, power consumption, accuracy and limitations in their operating environment. Hence, the last decade has brought about a large number of proposed methods for WSN node localization. They show tremendous variation in the physical phenomena they use, the signal properties they measure, the resources they consume, as well as in their accuracy, range, advantages and limitations. This paper provides a high-level, comprehensive overview of this very active research area.

Advances in Water Treatment Application of Sepiolite Mineral Materials

2013 ◽  
Vol 710 ◽  
pp. 217-220 ◽  
Author(s):  
Fei Wang ◽  
Lei Feng ◽  
Meng Ran Tang ◽  
Ji Yuan Li ◽  
Qing Guo Tang
Keyword(s):  
Water Treatment ◽  
Size Effect ◽  
Large Scale ◽  
Surface Effect ◽  
Development Trend ◽  
High Energy ◽  
Research Area ◽  
Group Mineral ◽  
Active Research ◽  
Treatment Application

Synthetic nanomaterials have the disadvantages of large-scale investment, high energy consumption, complex production process and heavy environmental load. Mineral nanomaterials such as sepiolite group mineral nanomaterials are characterized by small size effect, quantum size effect and surface effect. Water treatment application of sepiolite group mineral nanomaterials has become an active research area and showed good development and application prospects. Based on the above reasons, this paper systematically summarizes the water treatment application of sepiolite group mineral nanomaterials, and development trend related to water treatment application of sepiolite group mineral nanomaterials were also proposed.

scholarly journals Combined global and local semantic feature–based image retrieval analysis with interactive feedback

2019 ◽  
Vol 53 (1-2) ◽  
pp. 3-17
Author(s):  
A Anandh ◽  
K Mala ◽  
R Suresh Babu
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Research Work ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Image Retrieval System ◽  
False Image ◽  
Active Research ◽  
High Level ◽  
Interactive Feedback

Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on the low-level features, high-level features, or the combination of both. Content-based image retrieval results can be improved by considering various features like directionality, contrast, coarseness, busyness, local binary pattern, and local tetra pattern with modified binary wavelet transform. In this research work, appropriate features are identified, applied and results are validated against existing systems. Modified binary wavelet transform is a modified form of binary wavelet transform and this methodology produced more similar retrieval images. The proposed system also combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap. The quantitative evaluations such as average retrieval rate, false image acceptation ratio, and false image rejection ratio are evaluated to ensure the user expected results of the system. In addition to that, precision and recall are evaluated from the proposed system against the existing system results. When compared with the existing content-based image retrieval methods, the proposed approach provides better retrieval accuracy.

scholarly journals Neutrosophic Logic for Software Effort Estimation: An Improvement over Fuzzy Logic

2019 ◽  
Vol 8 (12S) ◽  
pp. 709-714
Keyword(s):  
Fuzzy Logic ◽  
Research Area ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Software Application ◽  
Neutrosophic Logic ◽  
Logic Approach ◽  
Human Thinking ◽  
Active Research ◽  
High Level

Software effort estimation is big and active research area. Software effort estimation is useful for time and efforts required to perform a particular task. But, it is very rare to estimate the effort with high level of reliability. There are various approaches to estimate the software application effort. In the present paper, to estimate the effort for software applications efforts, neutrosophic logic approach is used. Neutrosophic logic is a mathematical model for ambiguity, uncertainty, incompleteness, vagueness, redundancy, contradiction and inconsistency in data. It is the extension to the fuzzy logic. It is capable of handling those errors which are not handled by fuzzy logic like indeterminacy in the data. Neutrosophic logic gives the results very similar to human thinking. The present work concludes that neutrosophic logic optimizes the performance of fuzzy logic while calculating the software efforts..

scholarly journals Machine Learning for Mental Health in Social Media: Bibliometric Study (Preprint)

2020 ◽  
Author(s):  
Jina Kim ◽  
Daeun Lee ◽  
Eunil Park
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Large Scale ◽  
Web Of Science ◽  
Research Area ◽  
Research Subjects ◽  
Comprehensive Overview ◽  
Time Saving ◽  
Continuous Growth

BACKGROUND Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention. OBJECTIVE We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media. METHODS Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described. RESULTS We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with <i>Lecture Notes in Computer Science</i> and <i>Journal of Medical Internet Research</i> as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated. CONCLUSIONS The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.

Robot Swarm for Efficient Area Coverage Inspired by Ant Foraging: The Case of Adaptive Switching Between Brownian Motion and Lévy Flight

2017 ◽  
Author(s):  
Aditya Deshpande ◽  
Manish Kumar ◽  
Subramanian Ramakrishnan
Keyword(s):  
Brownian Motion ◽  
Performance Metrics ◽  
Research Area ◽  
Area Coverage ◽  
Lévy Flight ◽  
Levy Flight ◽  
Ant Foraging ◽  
Robot Swarm ◽  
Potential Applications ◽  
Active Research

Design of robot swarms inspired by self-organization in social insect groups is currently an active research area with a diverse portfolio of potential applications. In this work, the authors propose a control law for efficient area coverage by a robot swarm in a 2D spatial domain, inspired by the unique dynamical characteristics of ant foraging. The novel idea pursued in the effort is that dynamic, adaptive switching between Brownian motion and Lévy flight in the stochastic component of the search increases the efficiency of the search. Influence of different pheromone (the virtual chemotactic agent that drives the foraging) threshold values for switching between Lévy flights and Brownian motion is studied using two performance metrics — area coverage and visit entropy. The results highlight the advantages of the switching strategy for the control framework, particularly in cases when the object of the search is scarce in quantity or getting depleted in real-time.

scholarly journals Deep Learning for Anomaly Detection

2021 ◽  
Vol 54 (2) ◽  
pp. 1-38
Author(s):  
Guansong Pang ◽  
Chunhua Shen ◽  
Longbing Cao ◽  
Anton Van Den Hengel
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Novelty Detection ◽  
Research Area ◽  
Unique Problem ◽  
Fine Grained ◽  
Advantages And Disadvantages ◽  
Active Research ◽  
High Level ◽  
Critical Direction

Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection , has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.

scholarly journals Transformers in Vision: A Survey

2022 ◽  
Author(s):  
Salman Khan ◽  
Muzammal Naseer ◽  
Munawar Hayat ◽  
Syed Waqas Zamir ◽  
Fahad Shahbaz Khan ◽  
...  
Keyword(s):  
Computer Vision ◽  
Video Processing ◽  
Architectural Design ◽  
Large Scale ◽  
Question Answering ◽  
Short Term Memory ◽  
Super Resolution ◽  
Input Sequence ◽  
3D Analysis ◽  
Comprehensive Overview

Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Among their salient benefits, Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent networks e.g. , Long short-term memory (LSTM). Different from convolutional networks, Transformers require minimal inductive biases for their design and are naturally suited as set-functions. Furthermore, the straightforward design of Transformers allows processing multiple modalities ( e.g. , images, videos, text and speech) using similar processing blocks and demonstrates excellent scalability to very large capacity networks and huge datasets. These strengths have led to exciting progress on a number of vision tasks using Transformer networks. This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline. We start with an introduction to fundamental concepts behind the success of Transformers i.e., self-attention, large-scale pre-training, and bidirectional feature encoding. We then cover extensive applications of transformers in vision including popular recognition tasks ( e.g. , image classification, object detection, action recognition, and segmentation), generative modeling, multi-modal tasks ( e.g. , visual-question answering, visual reasoning, and visual grounding), video processing ( e.g. , activity recognition, video forecasting), low-level vision ( e.g. , image super-resolution, image enhancement, and colorization) and 3D analysis ( e.g. , point cloud classification and segmentation). We compare the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value. Finally, we provide an analysis on open research directions and possible future works. We hope this effort will ignite further interest in the community to solve current challenges towards the application of transformer models in computer vision.

Study and analysis of various sentiment classification strategies: A challenging overview

2021 ◽  
Author(s):  
Mandar Kundan Keakde ◽  
Akkalakshmi Muddana
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Performance Metrics ◽  
Research Area ◽  
Sentiment Classification ◽  
Future Research ◽  
Classification Schemes ◽  
Social Events ◽  
Election Prediction ◽  
Media Sentiment

In large-scale social media, sentiment classification is a significant one for connecting gaps among social media contents as well as real-world actions, including public emotional status monitoring, political election prediction, and so on. On the other hand, textual sentiment classification is well studied by various platforms, like Instagram, Twitter, etc. Sentiment classification has many advantages in various fields, like opinion polls, education, and e-commerce. Sentiment classification is an interesting and progressing research area due to its applications in several areas. The information is collected from various people about social, products, and social events by web in sentiment analysis. This review provides a detailed survey of 50 research papers presenting sentiment classification schemes such as active learning-based approach, aspect learning-based method, and machine learning-based approach. The analysis is presented based on the categorization of sentiment classification schemes, the dataset used, software tools utilized, published year, and the performance metrics. Finally, the issues of existing methods considering conventional sentiment classification strategies are elaborated to obtain improved contribution in devising significant sentiment classification strategies. Moreover, the probable future research directions in attaining efficient sentiment classification are provided.

scholarly journals Machine Learning for Mental Health in Social Media: Bibliometric Study

10.2196/24870 ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. e24870
Author(s):  
Jina Kim ◽  
Daeun Lee ◽  
Eunil Park
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Large Scale ◽  
Web Of Science ◽  
Research Area ◽  
Research Subjects ◽  
Comprehensive Overview ◽  
Time Saving ◽  
Continuous Growth

Background Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention. Objective We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media. Methods Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described. Results We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with Lecture Notes in Computer Science and Journal of Medical Internet Research as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated. Conclusions The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.

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