Real World Uniformity Measurement in Nonwoven Coverstock

Studies were carried out in a nonwoven roll goods plant to help eliminate subjective formation ratings. Tests were run on single layer samples with instruments measuring color, transmittance, haze and camera image gray scale values. High correlations are shown with air porosity, basis weight and formation ratings. Relationships to diaper glue bleed through, superabsorbent loss, and web tracking are reported. Some effects of process change are noted. Correlations critically depend upon measuring identical sample areas.

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Betweenness centrality for temporal multiplexes

Scientific Reports ◽

10.1038/s41598-021-84418-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Silvia Zaoli ◽

Piero Mazzarisi ◽

Fabrizio Lillo

Keyword(s):

Information Flow ◽

Real World ◽

Betweenness Centrality ◽

Temporal Structure ◽

Shortest Paths ◽

Single Layer ◽

Distance Metric ◽

Definition Of

AbstractBetweenness centrality quantifies the importance of a vertex for the information flow in a network. The standard betweenness centrality applies to static single-layer networks, but many real world networks are both dynamic and made of several layers. We propose a definition of betweenness centrality for temporal multiplexes. This definition accounts for the topological and temporal structure and for the duration of paths in the determination of the shortest paths. We propose an algorithm to compute the new metric using a mapping to a static graph. We apply the metric to a dataset of $$\sim 20$$ ∼ 20 k European flights and compare the results with those obtained with static or single-layer metrics. The differences in the airports rankings highlight the importance of considering the temporal multiplex structure and an appropriate distance metric.

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Logo Matching and Recognition Based on Context

Feature Dimension Reduction for Content-Based Image Identification - Advances in Multimedia and Interactive Technologies ◽

10.4018/978-1-5225-5775-3.ch009 ◽

2018 ◽

pp. 164-176

Author(s):

Tapan Kumar Das

Keyword(s):

Real World ◽

Scale Invariant Feature Transform ◽

Gray Scale ◽

Scale Invariant ◽

Invariant Feature ◽

Key Points ◽

Gray Scale Image ◽

Feature Transform ◽

Graphic Elements ◽

Scale Invariant Feature

Logos are graphic productions that recall some real-world objects or emphasize a name, simply display some abstract signs that have strong perceptual appeal. Color may have some relevance to assess the logo identity. Different logos may have a similar layout with slightly different spatial disposition of the graphic elements, localized differences in the orientation, size and shape, or differ by the presence/absence of one or few traits. In this chapter, the author uses ensemble-based framework to choose the best combination of preprocessing methods and candidate extractors. The proposed system has reference logos and test logos which are verified depending on some features like regions, pre-processing, key points. These features are extracted by using gray scale image by scale-invariant feature transform (SIFT) and Affine-SIFT (ASIFT) descriptor method. Pre-processing phase employs four different filters. Key points extraction is carried by SIFT and ASIFT algorithm. Key points are matched to recognize fake logo.

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Link prediction in real-world multiplex networks via layer reconstruction method

Royal Society Open Science ◽

10.1098/rsos.191928 ◽

2020 ◽

Vol 7 (7) ◽

pp. 191928

Author(s):

Amir Mahdi Abdolhosseini-Qomi ◽

Seyed Hossein Jafari ◽

Amirheckmat Taghizadeh ◽

Naser Yazdani ◽

Masoud Asadpour ◽

...

Keyword(s):

Real World ◽

Link Prediction ◽

Large Body ◽

Single Layer ◽

Structural Features ◽

Prediction Performance ◽

Reconstruction Method ◽

Multiplex Networks ◽

Technological Complex ◽

Different Types

Networks are invaluable tools to study real biological, social and technological complex systems in which connected elements form a purposeful phenomenon. A higher resolution image of these systems shows that the connection types do not confine to one but to a variety of types. Multiplex networks encode this complexity with a set of nodes which are connected in different layers via different types of links. A large body of research on link prediction problem is devoted to finding missing links in single-layer (simplex) networks. In recent years, the problem of link prediction in multiplex networks has gained the attention of researchers from different scientific communities. Although most of these studies suggest that prediction performance can be enhanced by using the information contained in different layers of the network, the exact source of this enhancement remains obscure. Here, it is shown that similarity w.r.t. structural features (eigenvectors) is a major source of enhancements for link prediction task in multiplex networks using the proposed layer reconstruction method and experiments on real-world multiplex networks from different disciplines. Moreover, we characterize how low values of similarity w.r.t. structural features result in cases where improving prediction performance is substantially hard.

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Learning embeddings for multiplex networks using triplet loss

Applied Network Science ◽

10.1007/s41109-019-0242-0 ◽

2019 ◽

Vol 4 (1) ◽

Author(s):

Seyedsaeed Hajiseyedjavadi ◽

Yu-Ru Lin ◽

Konstantinos Pelechrinis

Keyword(s):

Real World ◽

Online Social Network ◽

Single Layer ◽

Interaction Network ◽

Machine Learning Techniques ◽

Collaboration Network ◽

Multiplex Networks ◽

Multiplex Network ◽

Triplet Loss ◽

Low Dimensional

AbstractLearning low-dimensional representations of graphs has facilitated the use of traditional machine learning techniques to solving classic network analysis tasks such as link prediction, node classification, community detection, etc. However, to date, the vast majority of these learning tasks are focused on traditional single-layer/unimodal networks and largely ignore the case of multiplex networks. A multiplex network is a suitable structure to model multi-dimensional real-world complex systems. It consists of multiple layers where each layer represents a different relationship among the network nodes. In this work, we propose MUNEM, a novel approach for learning a low-dimensional representation of a multiplex network using a triplet loss objective function. In our approach, we preserve the global structure of each layer, while at the same time fusing knowledge among different layers during the learning process. We evaluate the effectiveness of our proposed method by testing and comparing on real-world multiplex networks from different domains, such as collaboration network, protein-protein interaction network, online social network. Finally, in order to deliberately examine the effect of our model’s parameters we conduct extensive experiments on synthetic multiplex networks.

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M-BiRank: Co-ranking developers and projects using multiple developer-project interactions in open source software community

10.21203/rs.3.rs-34629/v2 ◽

2020 ◽

Author(s):

Dengcheng Yan ◽

Bin Qi ◽

Yiwen Zhang ◽

Zhen Shao

Keyword(s):

Open Source ◽

Open Source Software ◽

Real World ◽

Single Layer ◽

Bipartite Network ◽

Degree Centrality ◽

Ranking Methods ◽

Ranking Model ◽

Multiple Interactions ◽

Collaborative Coding

Abstract Social collaborative coding is a popular trend in software development and such platforms as GitHub provides rich social and technical functionalities for developers to collaborate on open source projects through multiple interactions. Developers often follow popular developers and projects for learning, technical selection and collaboration. Thus identifying popular developers and projects is very meaningful. In this paper, we propose a multiplex bipartite network ranking model, M-BiRank, to co-rank developers and projects using multiple developer-project interactions. Firstly, multiple developer-project interactions such as commit, issue and watch is extracted and a multiplex developer-project bipartite network is constructed. Secondly, a random layer is selected from this multiplex bipartite network and initial ranking scores are calculated for developers and projects using BiRank. Finally, initial ranking scores diffuse to other layers and mutual reinforcement is taken into consideration to iteratively calculate ranking scores of developers and projects in different layers. Experiments on real world GitHub dataset show that M-BiRank outperforms degree centrality, traditional single layer ranking methods as well as multiplex ranking method.

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A Multi-Layer Network of the Colombian Sovereign Securities Market

Analyzing the Economics of Financial Market Infrastructures - Advances in Finance, Accounting, and Economics ◽

10.4018/978-1-4666-8745-5.ch007 ◽

2016 ◽

pp. 124-149 ◽

Cited By ~ 4

Author(s):

Carlos León ◽

Jhonatan Pérez ◽

Luc Renneboog

Keyword(s):

Financial Market ◽

Financial Institutions ◽

Real World ◽

Single Layer ◽

Securities Market ◽

Financial Networks ◽

Market Infrastructure ◽

Network Of Networks

This chapter examines the network of Colombian sovereign securities settlements. With data from the settlement market infrastructure we study financial institutions' transactions from three different trading and registering networks that we combine into a multi-layer network. Examining this network of networks enables us to confirm that (i) studying isolated single-layer trading and registering networks yields a misleading perspective on the relations between and risks induced by participating financial institutions; (ii) a multi-layer approach produces a connective structure consistent with most real-world networks (e.g. sparse, inhomogeneous, and clustered); and (iii) the multi-layer network is a multiplex that preserves the main connective features of its constituent layers due to positively correlated multiplexity. The results highlight the importance of mapping and understanding how financial institutions relate to each other across multiple financial environments, and the value of financial market infrastructures as sources of data for working on multi-layer financial networks.

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Terahertz Multilayer Thickness Measurements: Comparison of Optoelectronic Time and Frequency Domain Systems

Journal of Infrared Millimeter and Terahertz Waves ◽

10.1007/s10762-021-00831-5 ◽

2021 ◽

Author(s):

Lars Liebermeister ◽

Simon Nellen ◽

Robert B. Kohlhaas ◽

Sebastian Lauck ◽

Milan Deumer ◽

...

Keyword(s):

Standard Deviation ◽

Frequency Domain ◽

Time Domain ◽

Real World ◽

State Of The Art ◽

Single Layer ◽

Industrial Applications ◽

Starting Point ◽

Real World Applications ◽

Thickness Measurements

AbstractWe compare a state-of-the-art terahertz (THz) time domain spectroscopy (TDS) system and a novel optoelectronic frequency domain spectroscopy (FDS) system with respect to their performance in layer thickness measurements. We use equal sample sets, THz optics, and data evaluation methods for both spectrometers. On single-layer and multi-layer dielectric samples, we found a standard deviation of thickness measurements below 0.2 µm for TDS and below 0.5 µm for FDS. This factor of approx. two between the accuracy of both systems reproduces well for all samples. Although the TDS system achieves higher accuracy, FDS systems can be a competitive alternative for two reasons. First, the architecture of an FDS system is essentially simpler, and thus the price can be much lower compared to TDS. Second, an accuracy below 1 µm is sufficient for many real-world applications. Thus, this work may be a starting point for a comprehensive cross comparison of different terahertz systems developed for specific industrial applications.

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M-BiRank: co-ranking developers and projects using multiple developer-project interactions in open source software community

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01820-3 ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Dengcheng Yan ◽

Bin Qi ◽

Yiwen Zhang ◽

Zhen Shao

Keyword(s):

Open Source ◽

Open Source Software ◽

Real World ◽

Single Layer ◽

Bipartite Network ◽

Degree Centrality ◽

Ranking Methods ◽

Ranking Model ◽

Multiple Interactions ◽

Collaborative Coding

Abstract Social collaborative coding is a popular trend in software development, and such platforms as GitHub provide rich social and technical functionalities for developers to collaborate on open source projects through multiple interactions. Developers often follow popular developers and projects for learning, technical selection, and collaboration. Thus, identifying popular developers and projects is very meaningful. In this paper, we propose a multiplex bipartite network ranking model, M-BiRank, to co-rank developers and projects using multiple developer-project interactions. Firstly, multiple developer-project interactions such as commit, issue, and watch are extracted and a multiplex developer-project bipartite network is constructed. Secondly, a random layer is selected from this multiplex bipartite network and initial ranking scores are calculated for developers and projects using BiRank. Finally, initial ranking scores diffuse to other layers and mutual reinforcement is taken into consideration to iteratively calculate ranking scores of developers and projects in different layers. Experiments on real-world GitHub dataset show that M-BiRank outperforms degree centrality, traditional single layer ranking methods, and multiplex ranking method.

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