Analysis of Affordance Detection Methods for Real-World Robotic Manipulation

2019 ◽  
Author(s):  
Xavier Williams ◽  
Nihar R. Mahapatra
Keyword(s):  
Real World ◽  
Robotic Manipulation ◽  
Detection Methods

Detecting communities in networks using a Bayesian nonparametric method

2014 ◽  
Vol 28 (28) ◽  
pp. 1450199
Author(s):  
Shengze Hu ◽  
Zhenwen Wang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Real World ◽  
Detection Method ◽  
Generative Models ◽  
Detection Methods ◽  
Model Parameters ◽  
Nonparametric Method ◽  
Bayesian Nonparametric ◽  
New Community

In the real world, a large amount of systems can be described by networks where nodes represent entities and edges the interconnections between them. Community structure in networks is one of the interesting properties revealed in the study of networks. Many methods have been developed to extract communities from networks using the generative models which give the probability of generating networks based on some assumption about the communities. However, many generative models require setting the number of communities in the network. The methods based on such models are lack of practicality, because the number of communities is unknown before determining the communities. In this paper, the Bayesian nonparametric method is used to develop a new community detection method. First, a generative model is built to give the probability of generating the network and its communities. Next, the model parameters and the number of communities are calculated by fitting the model to the actual network. Finally, the communities in the network can be determined using the model parameters. In the experiments, we apply the proposed method to the synthetic and real-world networks, comparing with some other community detection methods. The experimental results show that the proposed method is efficient to detect communities in networks.

scholarly journals Fluorescence-Based Sensing of Pesticides Using Supramolecular Chemistry

2021 ◽  
Vol 9 ◽  
Author(s):  
Mindy Levine
Keyword(s):  
Public Health ◽  
Research And Development ◽  
Supramolecular Chemistry ◽  
Real World ◽  
Environmental Remediation ◽  
Detection Methods ◽  
Agricultural Sustainability ◽  
Future Research ◽  
Pesticide Detection

The detection of pesticides in real-world environments is a high priority for a broad range of applications, including in areas of public health, environmental remediation, and agricultural sustainability. While many methods for pesticide detection currently exist, the use of supramolecular fluorescence-based methods has significant practical advantages. Herein, we will review the use of fluorescence-based pesticide detection methods, with a particular focus on supramolecular chemistry-based methods. Illustrative examples that show how such methods have achieved success in real-world environments are also included, as are areas highlighted for future research and development.

Entropy and Surrogate Testing

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Real World ◽  
Time Series Data ◽  
Singular Spectrum Analysis ◽  
Detection Methods ◽  
Series Data ◽  
Lorenz Equations ◽  
Real World Data ◽  
Exogenous Shocks ◽  
Systems Of Odes ◽  
Periodic Dynamics

Reconstructing real-world system dynamics from time series data on a single variable is challenging because real-world data often exhibit a highly volatile and irregular appearance potentially driven by several diverse factors. NLTS methods help eliminate less likely drivers of dynamic irregularity. We set a benchmark for regular behavior by investigating how linear systems of ODEs are restricted to exponential and periodic dynamics, and illustrating how irregular behavior can arise if regular linear dynamics are corrupted with noise or shift over time (i.e., nonstationarity). We investigate how data can be pre-processed to control for the noise and nonstationarity potentially camouflaging nonlinear deterministic drivers of observed complexity. We can apply signal-detection methods, such as Singular Spectrum Analysis (SSA), to separate signal from noise in the data, and test the signal for nonstationarity potentially corrected with SSA. SSA measures signal strength which provides a useful initial indicator of whether we should continue searching for endogenous nonlinear drivers of complexity. We begin diagnosing deterministic structure in an isolated signal by attempting to reconstructed a shadow attractor. Finally, we use the classic Lorenz equations to illustrate how a deterministic nonlinear system of ODEs with at least three equations can generate observed irregular dynamics endogenously without aid of exogenous shocks or nonstationary dynamics.

scholarly journals NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 260 ◽  
Author(s):  
Bingyang Huang ◽  
Chaokun Wang ◽  
Binbin Wang
Keyword(s):  
Community Detection ◽  
Real World ◽  
State Of The Art ◽  
Label Propagation ◽  
Detection Methods ◽  
Overlapping Communities ◽  
Propagation Algorithm ◽  
Pruning Strategy ◽  
Node Similarity ◽  
Attributed Networks

With the enrichment of the entity information in the real world, many networks with attributed nodes are proposed and studied widely. Community detection in these attributed networks is an essential task that aims to find groups where the intra-nodes are much more densely connected than the inter-nodes. However, many existing community detection methods in attributed networks do not distinguish overlapping communities from non-overlapping communities when designing algorithms. In this paper, we propose a novel and accurate algorithm called Node-similarity-based Multi-Label Propagation Algorithm (NMLPA) for detecting overlapping communities in attributed networks. NMLPA first calculates the similarity between nodes and then propagates multiple labels based on the network structure and the node similarity. Moreover, NMLPA uses a pruning strategy to keep the number of labels per node within a suitable range. Extensive experiments conducted on both synthetic and real-world networks show that our new method significantly outperforms state-of-the-art methods.

scholarly journals Smart Black Box 2.0: Efficient High-Bandwidth Driving Data Collection Based on Video Anomalies

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 57
Author(s):  
Ryan Feng ◽  
Yu Yao ◽  
Ella Atkins
Keyword(s):  
Data Collection ◽  
Real World ◽  
Autonomous Vehicles ◽  
Ground Truth ◽  
Black Box ◽  
Video Data ◽  
Detection Methods ◽  
Real World Data ◽  
The Real ◽  
High Bandwidth

Autonomous vehicles require fleet-wide data collection for continuous algorithm development and validation. The smart black box (SBB) intelligent event data recorder has been proposed as a system for prioritized high-bandwidth data capture. This paper extends the SBB by applying anomaly detection and action detection methods for generalized event-of-interest (EOI) detection. An updated SBB pipeline is proposed for the real-time capture of driving video data. A video dataset is constructed to evaluate the SBB on real-world data for the first time. SBB performance is assessed by comparing the compression of normal and anomalous data and by comparing our prioritized data recording with an FIFO strategy. The results show that SBB data compression can increase the anomalous-to-normal memory ratio by ∼25%, while the prioritized recording strategy increases the anomalous-to-normal count ratio when compared to an FIFO strategy. We compare the real-world dataset SBB results to a baseline SBB given ground-truth anomaly labels and conclude that improved general EOI detection methods will greatly improve SBB performance.

scholarly journals Multiview Community Discovery Algorithm via Nonnegative Factorization Matrix in Heterogeneous Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Wang Tao ◽  
Liu Yang
Keyword(s):  
Community Structure ◽  
Heterogeneous Networks ◽  
Real World ◽  
Rapid Development ◽  
Complex Structure ◽  
Nonnegative Matrix ◽  
Bipartite Graphs ◽  
Semisupervised Learning ◽  
Detection Methods ◽  
Target Mode

With the rapid development of the Internet and communication technologies, a large number of multimode or multidimensional networks widely emerge in real-world applications. Traditional community detection methods usually focus on homogeneous networks and simply treat different modes of nodes and connections in the same way, thus ignoring the inherent complexity and diversity of heterogeneous networks. It is challenging to effectively integrate the multiple modes of network information to discover the hidden community structure underlying heterogeneous interactions. In our work, a joint nonnegative matrix factorization (Joint-NMF) algorithm is proposed to discover the complex structure in heterogeneous networks. Our method transforms the heterogeneous dataset into a series of bipartite graphs correlated. Taking inspiration from the multiview method, we extend the semisupervised learning from single graph to several bipartite graphs with multiple views. In this way, it provides mutual information between different bipartite graphs to realize the collaborative learning of different classifiers, thus comprehensively considers the internal structure of all bipartite graphs, and makes all the classifiers tend to reach a consensus on the clustering results of the target-mode nodes. The experimental results show that Joint-NMF algorithm is efficient and well-behaved in real-world heterogeneous networks and can better explore the community structure of multimode nodes in heterogeneous networks.

scholarly journals What delirium detection tools are used in routine clinical practice in the United Kingdom? Survey results from 91% of acute healthcare organisations

2021 ◽  
Author(s):  
Zoë Tieges ◽  
Jacqueline Lowrey ◽  
Alasdair M. J. MacLullich
Keyword(s):  
United Kingdom ◽  
Clinical Practice ◽  
Real World ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Assessment Tools ◽  
Clinical Population ◽  
Detection Methods ◽  
Delirium Assessment ◽  
The United Kingdom

Abstract Purpose Our aim was to collect information on delirium assessment processes and pathways in non-intensive care settings in the United Kingdom (UK). Methods We sent a Freedom of Information request to 169 UK National Health Service (NHS) hospitals, trusts and health boards (units) in July 2020 to obtain data on usage of delirium assessment tools in clinical practice and delirium pathways or guidelines. Results We received responses from 154/169 units (91% response rate). Of these, 146/154 (95%) units reported use of formal delirium assessment processes and 131/154 (85%) units had guidelines or pathways in place. The 4’A’s Test (4AT) was the most widely used tool, with 117/146 (80%) units reporting use. The Confusion Assessment Method was used in 65/146 (45%) units, and the Single Question to identify Delirium (SQiD) in 52/146 (36%) units. Conclusions Our findings show that the 4AT is the most commonly used tool in the UK, with 80% of units reporting use. This study adds to our knowledge of real-world uptake of delirium detection methods at scale. Future studies should evaluate real-world implementation of delirium assessment tools further via (1) tool completion rates and (2) rates of positive scores against the expected of prevalence delirium in the clinical population concerned.

scholarly journals Email Spoofing & Backlashes

2019 ◽  
Vol 8 (11) ◽  
pp. 1204-1209
Keyword(s):  
Best Practices ◽  
Real World ◽  
Mass Communication ◽  
Social Engineering ◽  
Anomalous Behavior ◽  
Detection Methods ◽  
Central Target ◽  
Phishing Attacks ◽  
The Social ◽  
The Impact

The email service is a core platform for Mass communication as a consequence of which, it becomes central Target of all the social engineering and phishing attacks. As a consequence, attackers can try to impersonate or fake a trusted identity to carry out highly sophisticated and deceptive phishing attacks via Email Spoofing. In this work, we analyze: (1) how different Email providers detect and deal with such attacks? (2) Existing protection techniques and what is its scope of effectiveness? (3) Under Which conditions do spoofed emails reach inbox and its potential consequences? (4) Best practices and Adaptability apart from existing methods to remain secure. We address this concern by considering the parameters of top 25 email services (Used by more than billions of users) and also real world experiments. The existing protocols, security layers and the restrictions based on detection methods. The scale of implications by allowing the forged emails to enter the inbox despite getting detected by layers of SPF, DKIM, DMARC and ARC. The extent of problems caused in different paradigms, and the potential of having just SMTP implemented without any additional security layers within the domains. The impact of Misleading UI for allowed spoofed emails by providers is also discussed briefly. We observe the impression of security when users are caught off guard in real world testing on domains (eg. Gmail, Hotmail, Yahoo mail, etc ) by simple platforms to spoof (eg. emkei.cz) apart from discussing the anomalous behavior of gmail as a response. We have conducted experiment to analyze behavior of top email domains against spoofed emails of various types

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