scholarly journals Pivot-based Maximal Biclique Enumeration

2020 ◽  
Author(s):  
Aman Abidi ◽  
Rui Zhou ◽  
Lu Chen ◽  
Chengfei Liu
Keyword(s):  
Real World ◽  
Search Space ◽  
Index Structure ◽  
Performance Study ◽  
Clique Enumeration ◽  
Wide Range ◽  
Real World Applications ◽  
Real World Datasets ◽  
Search Space Pruning ◽  
Extensive Performance

Enumerating maximal bicliques in a bipartite graph is an important problem in data mining, with innumerable real-world applications across different domains such as web community, bioinformatics, etc. Although substantial research has been conducted on this problem, surprisingly, we find that pivot-based search space pruning, which is quite effective in clique enumeration, has not been exploited in biclique scenario. Therefore, in this paper, we explore the pivot-based pruning for biclique enumeration. We propose an algorithm for implementing the pivot-based pruning, powered by an effective index structure Containment Directed Acyclic Graph (CDAG). Meanwhile, existing literature indicates contradictory findings on the order of vertex selection in biclique enumeration. As such, we re-examine the problem and suggest an offline ordering of vertices which expedites the pivot pruning. We conduct an extensive performance study using real-world datasets from a wide range of domains. The experimental results demonstrate that our algorithm is more scalable and outperforms all the existing algorithms across all datasets and can achieve a significant speedup against the previous algorithms.

scholarly journals A Practical Index Structure Supporting Fréchet Proximity Queries among Trajectories

2021 ◽  
Vol 7 (3) ◽  
pp. 1-33
Author(s):  
Joachim Gudmundsson ◽  
Michael Horton ◽  
John Pfeifer ◽  
Martin P. Seybold
Keyword(s):  
Nearest Neighbor ◽  
Search Space ◽  
Spatial Dimension ◽  
Index Structure ◽  
Trajectory Data ◽  
Efficiency And Effectiveness ◽  
Real World Datasets ◽  
Dynamic Tree ◽  
Search Space Pruning ◽  
Nearest Neighbor Queries

We present a scalable approach for range and k nearest neighbor queries under computationally expensive metrics, like the continuous Fréchet distance on trajectory data. Based on clustering for metric indexes, we obtain a dynamic tree structure whose size is linear in the number of trajectories, regardless of the trajectory’s individual sizes or the spatial dimension, which allows one to exploit low “intrinsic dimensionality” of datasets for effective search space pruning. Since the distance computation is expensive, generic metric indexing methods are rendered impractical. We present strategies that (i) improve on known upper and lower bound computations, (ii) build cluster trees without any or very few distance calls, and (iii) search using bounds for metric pruning, interval orderings for reduction, and randomized pivoting for reporting the final results. We analyze the efficiency and effectiveness of our methods with extensive experiments on diverse synthetic and real-world datasets. The results show improvement over state-of-the-art methods for exact queries, and even further speedups are achieved for queries that may return approximate results. Surprisingly, the majority of exact nearest-neighbor queries on real datasets are answered without any distance computations.

3D Technology – Additive Manufacturing Applications and Prospects (Vol. 24, No. 9, Full Issue)

2020 ◽  
Vol 24 (09) ◽  
Keyword(s):  
Additive Manufacturing ◽  
3D Printing ◽  
Environmental Impact ◽  
Real World ◽  
Vaccine Development ◽  
Health Screening ◽  
Wide Range ◽  
The World ◽  
Real World Applications ◽  
Manufacturing Applications

For the month of September 2020, APBN dives into the world of 3D printing and its wide range of real-world applications. Keeping our focus on the topic of the year, the COVID-19 pandemic, we explore the environmental impact of the global outbreak as well as gain insight to the top 5 vaccine platforms used in vaccine development. Discover more about technological advancements and how it is assisting innovation in geriatric health screening.

scholarly journals Particle Swarm Contour Search Algorithm

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 407 ◽  
Author(s):  
Dominik Weikert ◽  
Sebastian Mai ◽  
Sanaz Mostaghim
Keyword(s):  
Image Processing ◽  
Real World ◽  
State Of The Art ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Local Information ◽  
The State ◽  
Complete Knowledge ◽  
Real World Applications

In this article, we present a new algorithm called Particle Swarm Contour Search (PSCS)—a Particle Swarm Optimisation inspired algorithm to find object contours in 2D environments. Currently, most contour-finding algorithms are based on image processing and require a complete overview of the search space in which the contour is to be found. However, for real-world applications this would require a complete knowledge about the search space, which may not be always feasible or possible. The proposed algorithm removes this requirement and is only based on the local information of the particles to accurately identify a contour. Particles search for the contour of an object and then traverse alongside using their known information about positions in- and out-side of the object. Our experiments show that the proposed PSCS algorithm can deliver comparable results as the state-of-the-art.

Economics of Alliances: The Lessons for Collective Action

2001 ◽  
Vol 39 (3) ◽  
pp. 869-896 ◽  
Author(s):  
Todd Sandler ◽  
Keith Hartley
Keyword(s):  
Collective Action ◽  
Public Good ◽  
Real World ◽  
Pivotal Role ◽  
Burden Sharing ◽  
Empirical Knowledge ◽  
Wide Range ◽  
Real World Applications ◽  
Further Development

This essay provides an up-to-date summary of the findings of the literature on the economics of alliances. We show that the study of the economics of alliances has played a pivotal role in understanding and applying public good analysis to real-world applications. We establish that the manner in which alliances address burden sharing and allocative issues is related to strategic doctrines, weapon technology, perceived threats, and membership composition. Past contributions are evaluated, and areas needing further development are identified. The theoretical and empirical knowledge gained from the study of alliances is shown to be directly applicable to a wide range of international collectives.

scholarly journals Multimodal Linear Discriminant Analysis via Structural Sparsity

2017 ◽  
Author(s):  
Yu Zhang ◽  
Yuan Jiang
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Real World ◽  
Specific Class ◽  
Linear Discriminant ◽  
Class Separability ◽  
Dimensionality Reduction Technique ◽  
Real World Applications ◽  
Data Points ◽  
Real World Datasets

Linear discriminant analysis (LDA) is a widely used supervised dimensionality reduction technique. Even though the LDA method has many real-world applications, it has some limitations such as the single-modal problem that each class follows a normal distribution. To solve this problem, we propose a method called multimodal linear discriminant analysis (MLDA). By generalizing the between-class and within-class scatter matrices, the MLDA model can allow each data point to have its own class mean which is called the instance-specific class mean. Then in each class, data points which share the same or similar instance-specific class means are considered to form one cluster or modal. In order to learn the instance-specific class means, we use the ratio of the proposed generalized between-class scatter measure over the proposed generalized within-class scatter measure, which encourages the class separability, as a criterion. The observation that each class will have a limited number of clusters inspires us to use a structural sparse regularizor to control the number of unique instance-specific class means in each class. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed MLDA method.

scholarly journals A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability

Algorithms ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 17 ◽  
Author(s):  
Emmanuel Pintelas ◽  
Ioannis E. Livieris ◽  
Panagiotis Pintelas
Keyword(s):  
Machine Learning ◽  
Real World ◽  
High Performance ◽  
Black Box ◽  
Box Model ◽  
Proposed Model ◽  
Wide Range ◽  
Critical Issues ◽  
Key Factor ◽  
Real World Datasets

Machine learning has emerged as a key factor in many technological and scientific advances and applications. Much research has been devoted to developing high performance machine learning models, which are able to make very accurate predictions and decisions on a wide range of applications. Nevertheless, we still seek to understand and explain how these models work and make decisions. Explainability and interpretability in machine learning is a significant issue, since in most of real-world problems it is considered essential to understand and explain the model’s prediction mechanism in order to trust it and make decisions on critical issues. In this study, we developed a Grey-Box model based on semi-supervised methodology utilizing a self-training framework. The main objective of this work is the development of a both interpretable and accurate machine learning model, although this is a complex and challenging task. The proposed model was evaluated on a variety of real world datasets from the crucial application domains of education, finance and medicine. Our results demonstrate the efficiency of the proposed model performing comparable to a Black-Box and considerably outperforming single White-Box models, while at the same time remains as interpretable as a White-Box model.

scholarly journals A repository of real-world datasets for data-driven evolutionary multiobjective optimization

2019 ◽  
Vol 6 (1) ◽  
pp. 189-197 ◽  
Author(s):  
Cheng He ◽  
Ye Tian ◽  
Handing Wang ◽  
Yaochu Jin
Keyword(s):  
Multiobjective Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Data Driven ◽  
Benchmark Problems ◽  
Computational Costs ◽  
Evolutionary Multiobjective Optimization ◽  
Multiobjective Optimization Problems ◽  
Real World Applications ◽  
Real World Datasets

Abstract Many real-world optimization applications have more than one objective, which are modeled as multiobjective optimization problems. Generally, those complex objective functions are approximated by expensive simulations rather than cheap analytic functions, which have been formulated as data-driven multiobjective optimization problems. The high computational costs of those problems pose great challenges to existing evolutionary multiobjective optimization algorithms. Unfortunately, there have not been any benchmark problems reflecting those challenges yet. Therefore, we carefully select seven benchmark multiobjective optimization problems from real-world applications, aiming to promote the research on data-driven evolutionary multiobjective optimization by suggesting a set of benchmark problems extracted from various real-world optimization applications.

scholarly journals Eye centre localisation: an unsupervised modular approach

Sensor Review ◽  
2016 ◽  
Vol 36 (3) ◽  
pp. 277-286 ◽  
Author(s):  
Wenhao Zhang ◽  
Melvyn Lionel Smith ◽  
Lyndon Neal Smith ◽  
Abdul Rehman Farooq
Keyword(s):  
Real Time ◽  
Real World ◽  
Point Of View ◽  
Modular Approach ◽  
Content Type ◽  
Time Performance ◽  
Wide Range ◽  
Real World Applications ◽  
Coarse To Fine ◽  
Local Levels

Purpose This paper aims to introduce an unsupervised modular approach for eye centre localisation in images and videos following a coarse-to-fine, global-to-regional scheme. The design of the algorithm aims at excellent accuracy, robustness and real-time performance for use in real-world applications. Design/methodology/approach A modular approach has been designed that makes use of isophote and gradient features to estimate eye centre locations. This approach embraces two main modalities that progressively reduce global facial features to local levels for more precise inspections. A novel selective oriented gradient (SOG) filter has been specifically designed to remove strong gradients from eyebrows, eye corners and self-shadows, which sabotage most eye centre localisation methods. The proposed algorithm, tested on the BioID database, has shown superior accuracy. Findings The eye centre localisation algorithm has been compared with 11 other methods on the BioID database and six other methods on the GI4E database. The proposed algorithm has outperformed all the other algorithms in comparison in terms of localisation accuracy while exhibiting excellent real-time performance. This method is also inherently robust against head poses, partial eye occlusions and shadows. Originality/value The eye centre localisation method uses two mutually complementary modalities as a novel, fast, accurate and robust approach. In addition, other than assisting eye centre localisation, the SOG filter is able to resolve general tasks regarding the detection of curved shapes. From an applied point of view, the proposed method has great potentials in benefiting a wide range of real-world human-computer interaction (HCI) applications.

scholarly journals Boosting medical diagnostics by pooling independent judgments

2016 ◽  
Vol 113 (31) ◽  
pp. 8777-8782 ◽  
Author(s):  
Ralf H. J. M. Kurvers ◽  
Stefan M. Herzog ◽  
Ralph Hertwig ◽  
Jens Krause ◽  
Patricia A. Carney ◽  
...  
Keyword(s):  
Decision Making ◽  
Diagnostic Accuracy ◽  
Real World ◽  
Collective Intelligence ◽  
Medical Diagnostics ◽  
Wide Range ◽  
Key Factor ◽  
Systematic Effects ◽  
Real World Datasets ◽  
Skin Cancer Detection

Collective intelligence refers to the ability of groups to outperform individual decision makers when solving complex cognitive problems. Despite its potential to revolutionize decision making in a wide range of domains, including medical, economic, and political decision making, at present, little is known about the conditions underlying collective intelligence in real-world contexts. We here focus on two key areas of medical diagnostics, breast and skin cancer detection. Using a simulation study that draws on large real-world datasets, involving more than 140 doctors making more than 20,000 diagnoses, we investigate when combining the independent judgments of multiple doctors outperforms the best doctor in a group. We find that similarity in diagnostic accuracy is a key condition for collective intelligence: Aggregating the independent judgments of doctors outperforms the best doctor in a group whenever the diagnostic accuracy of doctors is relatively similar, but not when doctors’ diagnostic accuracy differs too much. This intriguingly simple result is highly robust and holds across different group sizes, performance levels of the best doctor, and collective intelligence rules. The enabling role of similarity, in turn, is explained by its systematic effects on the number of correct and incorrect decisions of the best doctor that are overruled by the collective. By identifying a key factor underlying collective intelligence in two important real-world contexts, our findings pave the way for innovative and more effective approaches to complex real-world decision making, and to the scientific analyses of those approaches.

scholarly journals Towards plug-and-play visual graph query interfaces

2021 ◽  
Vol 14 (11) ◽  
pp. 1979-1991
Author(s):  
Zifeng Yuan ◽  
Huey Eng Chua ◽  
Sourav S Bhowmick ◽  
Zekun Ye ◽  
Wook-Shin Han ◽  
...  
Keyword(s):  
Real World ◽  
Domain Knowledge ◽  
Experimental Studies ◽  
Plug And Play ◽  
Large Networks ◽  
Graph Query ◽  
Query Interfaces ◽  
Wide Range ◽  
Real World Datasets ◽  
Visual Graph

Canned patterns ( i.e. , small subgraph patterns) in visual graph query interfaces (a.k.a GUI) facilitate efficient query formulation by enabling pattern-at-a-time construction mode. However, existing GUIS for querying large networks either do not expose any canned patterns or if they do then they are typically selected manually based on domain knowledge. Unfortunately, manual generation of canned patterns is not only labor intensive but may also lack diversity for supporting efficient visual formulation of a wide range of subgraph queries. In this paper, we present a novel, generic, and extensible framework called TATTOO that takes a data-driven approach to automatically select canned patterns for a GUI from large networks. Specifically, it first decomposes the underlying network into truss-infested and truss-oblivious regions. Then candidate canned patterns capturing different real-world query topologies are generated from these regions. Canned patterns based on a user-specified plug are then selected for the GUI from these candidates by maximizing coverage and diversity , and by minimizing the cognitive load of the pattern set. Experimental studies with real-world datasets demonstrate the benefits of TATTOO. Importantly, this work takes a concrete step towards realizing plug-and-play visual graph query interfaces for large networks.

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