scholarly journals Violence Detection With Two-Stream Neural Network Based on C3D

2021 ◽  
Vol 15 (4) ◽  
pp. 1-17
Author(s):  
zanzan Lu ◽  
Xuewen Xia ◽  
Hongrun Wu ◽  
Chen Yang
Keyword(s):  
Large Scale ◽  
Research Area ◽  
Video Data ◽  
Small Scale ◽  
Stream Network ◽  
Generalization Ability ◽  
Violence Detection ◽  
Good Ability ◽  
Important Research Area ◽  
Public Datasets

In recent years, violence detection has gradually turned into an important research area in computer vision, and have proposed many models with high accuracy. However, the unsatisfactory generalization ability of these methods over different datasets. In this paper, the authors propose a violence detection method based on C3D two-stream network for spatiotemporal features. Firstly, the authors preprocess the video data of RGB stream and optical stream respectively. Secondly, the authors feed the data into two C3D networks to extract features from the RGB flow and the optical flow respectively. Third, the authors fuse the features extracted by the two networks to obtain a final prediction result. To testify the performance of the proposed model, four different datasets (two public datasets and two self-built datasets) are selected in this paper. The experimental results show that our model has good generalization ability compared to state-of-the-art methods, since it not only has good ability on large-scale datasets, but also performs well on small-scale datasets.

User-Independent Hand Motion Classification With Electromyography

2013 ◽  
Author(s):  
Alison E. Gibson ◽  
Mark R. Ison ◽  
Panagiotis Artemiadis
Keyword(s):  
Real Time ◽  
Large Scale ◽  
High Specificity ◽  
Research Area ◽  
Human Machine Interaction ◽  
Hand Motion ◽  
Intensive Training ◽  
Current State ◽  
Important Research Area ◽  
Leave One Out

Electromyographic (EMG) processing is an important research area with direct applications to prosthetics, exoskeletons and human-machine interaction. Current state of the art decoding methods require intensive training on a single user before it can be utilized, and have been unable to achieve both user-independence and real-time performance. This paper presents a real-time EMG classification method which generalizes across users without requiring an additional training phase. An EMG-embedded sleeve quickly positions and records from EMG surface electrodes on six forearm muscles. An optimized decision tree classifies signals from these sensors into five distinct movements for any given user using EMG energy synergies between muscles. This method was tested on 10 healthy subjects using leave-one-out validation, resulting in an overall accuracy of 79±6.6%, with sensitivity and specificity averaging 66% and 97.6%, respectively, over all classified motions. The high specificity values demonstrate the ability to generalize across users, presenting opportunities for large-scale studies and broader accessibility to EMG-driven applications.

scholarly journals LOCAL MAXIMUM DENSITY APPROACH FOR SMALL-SCALE CLUSTERING OF URBAN TAXI STOPS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1361-1367
Author(s):  
H. Wang ◽  
X.-J. Chen ◽  
Y. Wang ◽  
J. Shan
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Local Maximum ◽  
Research Area ◽  
Maximum Density ◽  
Small Scale ◽  
Urban Residents ◽  
Mobility Patterns ◽  
Trajectory Data ◽  
Day Of The Week

<p><strong>Abstract.</strong> Taxi trajectory data contains the detailed spatial and temporal traveling information of urban residents. By using a clustering algorithm, the hotspots’ distributions of pick-up and drop-off points can be extracted to explore the patterns of taxi traveling behaviors and its relationship with urban environment. Comparing with traditional methods that determine hotspots at a relatively large scale, we propose an approach to detect small-scale hotspots, so called docking points, to represent the local clusters in both sparse and dense stops areas. In this method, we divide the research area into grids and extract the docking points by finding local maximums of a certain range. The extracted docking points are classified into five levels for the subsequent analysis. Finally, to uncover detail characteristics of taxi mobility patterns, we analyze the distributions of docking points from three aspects &amp;ndash; the overall, by day of the week, and by time of the day.</p>

scholarly journals Research on the Development of Small and Medium-sized Navy in Southeast Asian Countries

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Shyi-Min Lu ◽  
Keyword(s):  
Large Scale ◽  
Research Area ◽  
Arms Race ◽  
Asia Pacific ◽  
Important Research ◽  
Asian Countries ◽  
Asia Pacific Region ◽  
International Trends ◽  
Important Research Area ◽  
Modernization Process

There is no doubt that the entire Asia-Pacific region, especially Southeast Asia, is undergoing a large-scale naval modernization process. However, most analyses of this phenomenon have focused on its scope and nature, especially its possible consequences for peace and stability in the region. Of particular concern is whether we are seeing the beginning of an unstable naval arms race in the region. This is completely grounded, and it is indeed an important research area, which will be discussed in the article. In addition, we also look at the general naval modernization process, and discuss in essence how the country to develop or maintain the navy, as well as the special problems and challenges that the navy often faces in this process, for example, with the current economic growth and international trends, increasing the naval budget and procurement of related potential ship. The purpose of this paper is to review the naval modernization of the six countries in the South China Sea, which can be served as guidance for Taiwan's navy construction.

scholarly journals The Impact of Farming and Land Ownership on Soil Erosion

2014 ◽  
Vol 62 (5) ◽  
pp. 883-890
Author(s):  
Olga Čermáková ◽  
Miloslav Janeček ◽  
Andrea Jindrová ◽  
Jan Kořínek
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Large Scale ◽  
Research Area ◽  
Water Erosion ◽  
Small Scale ◽  
Slope Length ◽  
The Difference ◽  
Factor C ◽  
The Impact

The aim of this paper was to compare two methods of farming, especially their effect on water soil erosion. The examined methods were (1) large-scale farming, where more than 50% of the land was leased, and (2) small-scale farming, where the land was almost exclusively privately owned. The research area was 8 cadastres in the district of Hodonín, South Moravia, Czech Republic. In these cadastres 48 land blocks representing both large-scale and small-scale farming (i.e. owners and tenants) were chosen. The long-term average annual soil loss caused by water erosion (G) was calculated using the erosion model USLE 2D and ArcGIS 10.1. The nonparametric Mann-Whitney test was used for the statistical evaluation of the data. The difference between the soil loss (G) on land blocks farmed by small producers (owners) and large producers (tenants) was significant (p < 0.05). Differences between the values of the cropping-management factor (C) were not statistically significant (p = 0.054). Based on the analysis of other variables in the USLE equation it can be stated that a continuous slope length, conditioned by the size of land blocks, played an important role in the amount of soil loss caused by water erosion. Above all, to protect the soil from erosion and maintain soil quality it is necessary to reduce the size of land blocks farmed by tenants and improve the crop rotation systems.

scholarly journals SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2771
Author(s):  
Muhammad Asif Razzaq ◽  
Ian Cleland ◽  
Chris Nugent ◽  
Sungyoung Lee
Keyword(s):  
Deep Learning ◽  
Research Area ◽  
Data Sources ◽  
Sensor Data ◽  
Similarity Learning ◽  
Multiple Sensors ◽  
Important Research Area ◽  
Artificial Neural Network Ann ◽  
Public Datasets ◽  
Syntactic Differences

The recognition of activities of daily living (ADL) in smart environments is a well-known and an important research area, which presents the real-time state of humans in pervasive computing. The process of recognizing human activities generally involves deploying a set of obtrusive and unobtrusive sensors, pre-processing the raw data, and building classification models using machine learning (ML) algorithms. Integrating data from multiple sensors is a challenging task due to dynamic nature of data sources. This is further complicated due to semantic and syntactic differences in these data sources. These differences become even more complex if the data generated is imperfect, which ultimately has a direct impact on its usefulness in yielding an accurate classifier. In this study, we propose a semantic imputation framework to improve the quality of sensor data using ontology-based semantic similarity learning. This is achieved by identifying semantic correlations among sensor events through SPARQL queries, and by performing a time-series longitudinal imputation. Furthermore, we applied deep learning (DL) based artificial neural network (ANN) on public datasets to demonstrate the applicability and validity of the proposed approach. The results showed a higher accuracy with semantically imputed datasets using ANN. We also presented a detailed comparative analysis, comparing the results with the state-of-the-art from the literature. We found that our semantic imputed datasets improved the classification accuracy with 95.78% as a higher one thus proving the effectiveness and robustness of learned models.

Developing an Autonomous Hovercraft for Benthic Surveying in Very Shallow Waters

2020 ◽  
Author(s):  
Meghan Troup ◽  
David Barclay ◽  
Matthew Hatcher
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Water Clarity ◽  
Research Area ◽  
Optical Methods ◽  
Small Scale ◽  
Sediment Type ◽  
Side Scan Sonar ◽  
Single Beam ◽  
Remote Sensing Methods

&lt;p&gt;Benthic surveys in very shallow water (&lt; 1 meter) are often carried out by remote sensing methods such as LiDAR, satellite imagery, and aerial photography, or by written observations paired with GPS point measurements and underwater video. Remote sensing can be helpful for large scale mapping endeavors, but the optical methods commonly used are limited in their effectiveness by cloud cover and water clarity. In situ surveys are often carried out manually and can therefore be quite inefficient. A proposed alternative method of small scale, high resolution mapping is an autonomous, amphibious hovercraft, fitted with high frequency single-beam and side-scan sonar instruments. A hovercraft can move seamlessly from land to water which allows for convenient and simple deployment. The sonar instruments are attached to a boat-shaped outrigger hull that can be raised and lowered automatically, enabling data collection in water as shallow as 10 cm. These data are used to extract seafloor characteristics in order to create detailed maps of the research area that include information such as sediment type, presence and extent of flora and fauna, and small-scale bathymetry.&lt;/p&gt;

scholarly journals Advances in Deep Learning-Based Medical Image Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiaoqing Liu ◽  
Kunlun Gao ◽  
Bo Liu ◽  
Chengwei Pan ◽  
Kongming Liang ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
Large Scale ◽  
Medical Image Analysis ◽  
Research Area ◽  
Clinical Applications ◽  
Learning Technologies ◽  
Small Scale ◽  
Great Success

Importance. With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active research area both in medical industry and academia. This paper reviewed the recent progress of deep learning research in medical image analysis and clinical applications. It also discussed the existing problems in the field and provided possible solutions and future directions. Highlights. This paper reviewed the advancement of convolutional neural network-based techniques in clinical applications. More specifically, state-of-the-art clinical applications include four major human body systems: the nervous system, the cardiovascular system, the digestive system, and the skeletal system. Overall, according to the best available evidence, deep learning models performed well in medical image analysis, but what cannot be ignored are the algorithms derived from small-scale medical datasets impeding the clinical applicability. Future direction could include federated learning, benchmark dataset collection, and utilizing domain subject knowledge as priors. Conclusion. Recent advanced deep learning technologies have achieved great success in medical image analysis with high accuracy, efficiency, stability, and scalability. Technological advancements that can alleviate the high demands on high-quality large-scale datasets could be one of the future developments in this area.

scholarly journals Identification of a New Species of the Medicinal Leeches in India using DNA Barcoding Technique - a Breakthrough

2021 ◽  
pp. 191-199
Author(s):  
Gaurav Phull ◽  
Mukund Dhule ◽  
Rekha Phull ◽  
Manisha Gupta
Keyword(s):  
Dna Barcoding ◽  
Large Scale ◽  
Research Area ◽  
Research Papers ◽  
Medicinal Value ◽  
Therapeutic Uses ◽  
Important Research Area ◽  
Medicinal Leeches ◽  
First Time ◽  
Species Being

Leech therapy has always been an important treatment modality in Ayurveda. Medicinal leeches are known for their extensive therapeutic uses. Identification of leech species is an important research area to understand the mechanism of therapeutic gains and exploring more probable benefits. Researchers have done a swell job in identifying various species of leeches in different parts of Asia including India. For therapeutic purpose however, the clinicians in India have not bothered much about the exact identification of leech species being used at different centres. There is dearth of research papers in this aspect. Thus a study was planned to identify the species of leeches being used at our centre. Materials and methods: DNA Barcoding technique was used where mitochondrial CO1 gene was amplified using LCO-HCO primers. Results: DNA sequencing of Leech sample closely resembled to Hirudinaria Bpling species, which is a breakthrough in existing knowledge of medicinal leeches in India, as this species was recently reported to be found in Thailand in 2012 for the first time. No previous documentation of its existence is available in our country. Conclusion: Further studies on large scale are required to explore their existence, morphological features and salivary contents in regard to their medicinal value. This will help in exploring any additional benefits to the existing knowledge of therapeutic uses of medicinal leeches. This study will pave the path to new avenues in therapeutic utility of leeches, as the newer species might have additional bioactive chemicals making them useful in wider variety of disorders.

Small-Scale Science to Large-Scale Profession

2000 ◽  
Vol 45 (4) ◽  
pp. 396-398
Author(s):  
Roger Smith
Keyword(s):  
Large Scale ◽  
Small Scale

Development of Physics Learning Module Based on Guided Inquiry with Light Wave Material in Class XI of Senior High School

2020 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Evi Rahmawati ◽  
Irnin Agustina Dwi Astuti ◽  
N Nurhayati
Keyword(s):  
Large Scale ◽  
Learning Strategy ◽  
Student Response ◽  
Average Score ◽  
Small Scale ◽  
Design Development ◽  
Teacher Response ◽  
Student Responses ◽  
Analysis Design ◽  
Scientific Skills

IPA Integrated is a place for students to study themselves and the surrounding environment applied in daily life. Integrated IPA Learning provides a direct experience to students through the use and development of scientific skills and attitudes. The importance of integrated IPA requires to pack learning well, integrated IPA integration with the preparation of modules combined with learning strategy can maximize the learning process in school. In SMP 209 Jakarta, the value of the integrated IPA is obtained from 34 students there are 10 students completed and 24 students are not complete because they get the value below the KKM of 68. This research is a development study with the development model of ADDIE (Analysis, Design, Development, Implementation, and Evaluation). The use of KPS-based integrated IPA modules (Science Process sSkills) on the theme of rainbow phenomenon obtained by media expert validation results with an average score of 84.38%, average material expert 82.18%, average linguist 75.37%. So the average of all aspects obtained by 80.55% is worth using and tested to students. The results of the teacher response obtained 88.69% value with excellent criteria. Student responses on a small scale acquired an average score of 85.19% with highly agreed criteria and on the large-scale student response gained a yield of 86.44% with very agreed criteria. So the module can be concluded receiving a good response by the teacher and students.

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