scholarly journals Balancing Relevance and Diversity in Online Bipartite Matching via Submodularity

2019 ◽  
Vol 33 ◽  
pp. 1877-1884 ◽  
Author(s):  
John P. Dickerson ◽  
Karthik Abinav Sankararaman ◽  
Aravind Srinivasan ◽  
Pan Xu
Keyword(s):  
Real World ◽  
The Other ◽  
Objective Functions ◽  
Bipartite Matching ◽  
Matching Problems ◽  
Special Cases ◽  
Coverage Function ◽  
Synthetic Datasets ◽  
Novel Algorithms ◽  
Real World Problems

In bipartite matching problems, vertices on one side of a bipartite graph are paired with those on the other. In its online variant, one side of the graph is available offline, while the vertices on the other side arrive online. When a vertex arrives, an irrevocable and immediate decision should be made by the algorithm; either match it to an available vertex or drop it. Examples of such problems include matching workers to firms, advertisers to keywords, organs to patients, and so on. Much of the literature focuses on maximizing the total relevance—modeled via total weight—of the matching. However, in many real-world problems, it is also important to consider contributions of diversity: hiring a diverse pool of candidates, displaying a relevant but diverse set of ads, and so on. In this paper, we propose the Online Submodular Bipartite Matching (OSBM) problem, where the goal is to maximize a submodular function f over the set of matched edges. This objective is general enough to capture the notion of both diversity (e.g., a weighted coverage function) and relevance (e.g., the traditional linear function)—as well as many other natural objective functions occurring in practice (e.g., limited total budget in advertising settings). We propose novel algorithms that have provable guarantees and are essentially optimal when restricted to various special cases. We also run experiments on real-world and synthetic datasets to validate our algorithms.

Empirical Comparison of Various Discretization Procedures

1998 ◽  
Vol 12 (07) ◽  
pp. 1017-1032 ◽  
Author(s):  
Petr Berka ◽  
Ivan Bruha
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Machine Learning Algorithm ◽  
Empirical Comparison ◽  
Numerical Attributes ◽  
Real World Problems ◽  
Discretization Procedure

The genuine symbolic machine learning (ML) algorithms are capable of processing symbolic, categorial data only. However, real-world problems, e.g. in medicine or finance, involve both symbolic and numerical attributes. Therefore, there is an important issue of ML to discretize (categorize) numerical attributes. There exist quite a few discretization procedures in the ML field. This paper describes two newer algorithms for categorization (discretization) of numerical attributes. The first one is implemented in the KEX (Knowledge EXplorer) as its preprocessing procedure. Its idea is to discretize the numerical attributes in such a way that the resulting categorization corresponds to KEX knowledge acquisition algorithm. Since the categorization for KEX is done "off-line" before using the KEX machine learning algorithm, it can be used as a preprocessing step for other machine learning algorithms, too. The other discretization procedure is implemented in CN4, a large extension of the well-known CN2 machine learning algorithm. The range of numerical attributes is divided into intervals that may form a complex generated by the algorithm as a part of the class description. Experimental results show a comparison of performance of KEX and CN4 on some well-known ML databases. To make the comparison more exhibitory, we also used the discretization procedure of the MLC++ library. Other ML algorithms such as ID3 and C4.5 were run under our experiments, too. Then, the results are compared and discussed.

Artificial intelligence and operations research: challenges and opportunities in planning and scheduling

2000 ◽  
Vol 15 (1) ◽  
pp. 1-10 ◽  
Author(s):  
CARLA P. GOMES
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Operations Research ◽  
Expressive Power ◽  
Combinatorial Problems ◽  
The Other ◽  
Planning And Scheduling ◽  
Functional Programming Languages ◽  
Other Hand ◽  
Real World Problems

Both the Artificial Intelligence (AI) and the Operations Research (OR) communities are interested in developing techniques for solving hard combinatorial problems, in particular in the domain of planning and scheduling. AI approaches encompass a rich collection of knowledge representation formalisms for dealing with a wide variety of real-world problems. Some examples are constraint programming representations, logical formalisms, declarative and functional programming languages such as Prolog and Lisp, Bayesian models, rule-based formalism, etc. The downside of such rich representations is that in general they lead to intractable problems, and we therefore often cannot use such formalisms for handling realistic size problems. OR, on the other hand, has focused on more tractable representations, such as linear programming formulations. OR-based techniques have demonstrated the ability to identify optimal and locally optimal solutions for well-defined problem spaces. In general, however, OR solutions are restricted to rigid models with limited expressive power. AI techniques, on the other hand, provide richer and more flexible representations of real-world problems, supporting efficient constraint-based reasoning mechanisms as well as mixed initiative frameworks, which allow the human expertise to be in the loop. The challenge lies in providing representations that are expressive enough to describe real-world problems and at the same time guaranteeing good and fast solutions.

Idea Generation Through Hackathon Event in Emergencies and Disasters, with Emphasis on Managing Flash Flood Disaster

2021 ◽  
pp. 1-5
Author(s):  
Sanaz Monsef ◽  
Shaghayegh Haghjooy Javanmard ◽  
Mostafa Amini-Rarani ◽  
Mohammad H. Yarmohammadian ◽  
Youseph Yazdi ◽  
...  
Keyword(s):  
Real World ◽  
Business Models ◽  
Flash Flood ◽  
Idea Generation ◽  
Flash Floods ◽  
Flood Disaster ◽  
The Other ◽  
Future Events ◽  
Real World Problems

Abstract Objective: This study was intended to demonstrate the applicability of the hackathon in idea generation for managing emergencies and disasters with a particular focus on flash floods. Methods: A 4-day hackathon event was held, having 60 students, 9 mentors and 6 judges gathered to explore different ideas, and to solve problems of Iran flooding from mid-March to April, 2019. Of these, 10 teams with 6 students were accordingly formed to brainstorm and discuss the idea, while 9 mentors offered advice and guided them to manage their ideas. Then, all teams focused on designing their business models. Finally, the hackathon teams finalized their lean canvas and presented their ideas to the judging panel and the other participants. Results: A total of 10 ideas were presented, and based on the knowledge and experience of the judges, 3 ideas that were more practical and useful were selected. Conclusions: As participants in a hackathon identify and present real-world problems, while ensuring that the prototype solutions address the end-user’s needs, it could be used to drive innovation, generate ideas, promote change in emergencies and disasters, and can increase our preparedness for future events. It helps us to develop tools and applications to better respond to these events.

scholarly journals A Mutual-Evaluation Genetic Algorithm for Numerical and Routing Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Chih-Hao Lin ◽  
Jiun-De He
Keyword(s):  
Genetic Algorithm ◽  
Real World ◽  
Function Analysis ◽  
Wide Spectrum ◽  
Solution Space ◽  
Objective Functions ◽  
Routing Problems ◽  
Local Optima ◽  
Mutual Evaluation ◽  
Real World Problems

Many real-world problems can be formulated as numerical optimization with certain objective functions. However, these objective functions often contain numerous local optima, which could trap an algorithm from moving toward the desired global solution. To improve the search efficiency of traditional genetic algorithms, this paper presents a mutual-evaluation genetic algorithm (MEGA). A novel mutual-evaluation approach is employed so that the merit of selected genes in a chromosome can be determined by comparing the fitness changes before and after interchanging with those in the mating chromosome. According to the determined genome merit, a therapy crossover can generate effective schemata to explore the solution space efficiently. The computational experiments for twelve numerical problems show that the MEGA can find near optimal solutions in all test benchmarks and achieve solutions with higher accuracy than those obtained by eight existing algorithms. This study also uses the MEGA to find optimal flow-allocation strategies for multipath-routing problems. Experiments on quality-of-service routing scenarios show that the MEGA can deal with these constrained routing problems effectively and efficiently. Therefore, the MEGA not only can reduce the effort of function analysis but also can deal with a wide spectrum of real-world problems.

scholarly journals AN OBJECTIVE MULTI-CRITERIA APPROACH TO OPTIMIZATION USING MOORA METHOD AND INTERVAL GREY NUMBERS

2012 ◽  
Vol 18 (2) ◽  
pp. 331-363 ◽  
Author(s):  
Dragisa Stanujkic ◽  
Nedeljko Magdalinovic ◽  
Rodoljub Jovanovic ◽  
Sanja Stojanovic
Keyword(s):  
Decision Making ◽  
Real World ◽  
Operational Research ◽  
The Other ◽  
Previous Period ◽  
Moora Method ◽  
Real World Problems

Many real-world problems are complex and/or related to the manifestation of some form of uncertainty and/or prediction. Therefore the use of extended MCDM methods is more appropriate than the use of the other classic decision making methods. These methods are improved by the use of a form of fuzzy or interval grey numbers. In the field of operational research, during the previous period, numerous MCDM methods were formed, but one newly proposed, the MOORA method, is very specific and yet has no extension. Therefore, in this paper we combine concept of interval grey numbers and MOORA method in order to propose extended MOORA method which will be more appropriate to solve many complex real-world problems.

scholarly journals Nature-Inspired Algorithms in Real-World Optimization Problems

MENDEL ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 7-14 ◽  
Author(s):  
Petr Bujok ◽  
Josef Tvrdik ◽  
Radka Polakova
Keyword(s):  
Differential Evolution ◽  
Real World ◽  
Optimization Problems ◽  
Random Search ◽  
The Other ◽  
Test Problems ◽  
Strong Argument ◽  
Nature Inspired Algorithms ◽  
New Algorithms ◽  
Real World Problems

Eight popular nature inspired algorithms are compared with the blind random search and three advanced adaptive variants of differential evolution (DE) on real-world problems benchmark collected for CEC 2011 algorithms competition. The results show the good performance of the adaptive DE variants and their superiority over the other algorithms in the test problems. Some of the nature-inspired algorithms perform even worse that the blind random search in some problems. This is a strong argument for recommendation for application, where well-verified algorithm successful in competitions should be preferred instead of developing some new algorithms.

scholarly journals The Unteachable in Pursuit of the Unreadable?

2006 ◽  
Vol 1 (2) ◽  
pp. 51 ◽  
Author(s):  
Andrew Booth
Keyword(s):  
Problem Solving ◽  
Systematic Reviews ◽  
Real World ◽  
Self Efficacy ◽  
The Other ◽  
Evidence Based ◽  
Real World Problems

This commentary highlights two overwhelming barriers to EBLIP - one at the consumption end and the other at the production end of the evidence chain, namely that librarians are ‘unteachable’ and systematic reviews are ‘unreadable’. The author identifies two possible solutions to overcome these barriers; equipping librarians with self-efficacy and concentrating efforts on the investigation of real world problems through the production of Route maps for Evidence based problem Solving (“RESolve”). He proposes that these approaches will help in making librarians teachable and evidence syntheses readable.

scholarly journals Developing an Optimal Class of Generic Sixteenth-Order Simple-Root Finders and Investigating Their Dynamics

Mathematics ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 8 ◽  
Author(s):  
Young Hee Geum ◽  
Young Ik Kim ◽  
Beny Neta
Keyword(s):  
Global Convergence ◽  
Real World ◽  
Simple Root ◽  
Weight Functions ◽  
Root Finding ◽  
Special Cases ◽  
Attraction Basins ◽  
Visual Description ◽  
Future Work ◽  
Real World Problems

Developed here are sixteenth-order simple-root-finding optimal methods with generic weight functions. Their numerical and dynamical aspects are investigated with the establishment of a main theorem describing the desired optimal convergence. Special cases with polynomial and rational weight functions have been extensively studied for applications to real-world problems. A number of computational experiments clearly support the underlying theory on the local convergence of the proposed methods. In addition, to investigate the relevant global convergence, we focus on the dynamics of the developed methods, as well as other known methods through the visual description of attraction basins. Finally, we summarized the results, discussion, conclusion, and future work.

scholarly journals BORDER CROSSINGS: USE OF LINGUISTIC STUDIES ACROSS SUBJECT DISCIPLINES

2019 ◽  
Vol 5 (2) ◽  
pp. 49
Author(s):  
Maya Khemlani David ◽  
Aliyyah Nuha Faiqah Azman Firdaus ◽  
Syed Abdul Manan
Keyword(s):  
Real World ◽  
Applied Linguistics ◽  
The Other ◽  
Border Crossings ◽  
Health Law ◽  
Indirect Impact ◽  
In Trans ◽  
Almost All ◽  
And Linguistics ◽  
Real World Problems

Cross-disciplinary research, involving scholars of multiple disciplines, has attracted much attention from universities recently. This type of study extends beyond simple collaboration in integrating data, methodologies, perspectives and concepts and engages with real world problems, especially as global complexities have undermined the�underlying ideology of countability and singularity of various disciplines founded on antiquated notions of territorialization.�Since most disciplines are transferred through language and linguistics sciences like socio-linguistics, applied-linguistics and psycho-linguistics,�an interrogation of received discourses on language study�has direct and indirect impact on almost all the other disciplines and can be used to enhance language related studies in different ways.�This paper shall define cross-disciplinary research and provide an overview of how applied linguistics and professional studies interrelate, focusing on the fact that research across disciplines must yield output that advances and benefits society, while allowing for complex and nuanced assessments allowed by the porous borders of different disciplines. This paper shares the kind of cross-disciplinary research which marries linguistics, languages and communication with other disciplines (for example, studies based on socio-linguistics and health, law, business or industry) to show how knowledge achieved from such research can result in trans-disciplinary recombination and expertise in other professional domains.

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