Learning to weight similarity measures with Siamese networks: a case study on optimum-path forest

2022 ◽  
pp. 155-173
Author(s):  
Gustavo H. de Rosa ◽  
João Paulo Papa
Keyword(s):  
Similarity Measures ◽  
Optimum Path ◽  
Siamese Networks

scholarly journals Multilingual author matching across different academic databases: a case study on KAKEN, DBLP, and PubMed

2021 ◽  
Vol 126 (3) ◽  
pp. 2311-2327
Author(s):  
Yuto Chikazawa ◽  
Marie Katsurai ◽  
Ikki Ohmukai
Keyword(s):  
Database Management ◽  
Similarity Measures ◽  
Practical Approach ◽  
Native Languages ◽  
Source Database ◽  
Academic Publications ◽  
Academic Records ◽  
Stable Performance ◽  
Target Database

AbstractResearchers often use their native languages to present and exchange ideas. To construct an individual author’s complete profile, a list of their English and non-English academic publications must be constructed. This paper presents a practical approach for multilingual author matching across different academic databases. Our approach automatically links the academic records of a target database to a researcher identifier of a source database. First, we extracted a comprehensive set of records in the target database, whose author names were identical to the researcher names in the source database. Then, we calculated multiple author similarity measures, which can be adopted in certain entity pairs from different language databases. Finally, we aggregated the measures to output an improved score that indicates the likelihood of each record as being the researcher’s work. Our method was found to be easy to implement, and its performance was evaluated in real database management settings. Experiments were conducted using DBLP and PubMed as the target English databases. As the Japanese database, KAKEN was the source for identifying researcher information. The results demonstrated each similarity measure’s performance, from which we observed that the score aggregation achieved stable performance. Our method can lessen human efforts to associate various scholarly contributions.

Advanced Similarity Measures Using Word Embeddings and Siamese Networks in CBR

2019 ◽  
pp. 449-462 ◽  
Author(s):  
Kareem Amin ◽  
George Lancaster ◽  
Stelios Kapetanakis ◽  
Klaus-Dieter Althoff ◽  
Andreas Dengel ◽  
...  
Keyword(s):  
Similarity Measures ◽  
Word Embeddings ◽  
Siamese Networks

scholarly journals Construction of Quality Indicators Based on Pre-Established Goals: Application to a Colombian Public University

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1075
Author(s):  
Vicente Liern ◽  
Sandra E. Parada-Rico ◽  
Olga Blasco-Blasco
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Gender Inequality ◽  
Multiple Criteria Decision Making ◽  
Similarity Measures ◽  
Public University ◽  
Strategic Decisions ◽  
Economic Disparity

This study creates indicators of adequacy and excellence based on multiple-criteria decision-making (MCDM) methods and fuzzy logic. The calculation of indicators presents two main difficulties: The nature of the data (numerical, interval, and linguistic values are mixed) and the objective of each criterion (which does not have to reach either the maximum or the minimum). A method is proposed, based on similarity measures with predetermined ideals, that is capable of overcoming these difficulties to provide easy-to-interpret information about the quality of the alternatives. To illustrate the usefulness of this proposed method, it has been applied to data collected from students across nine semesters at the Bucaramanga campus of the Industrial University of Santander in Colombia. This case study demonstrates that the proposed method can facilitate strategic decisions at an institution and open the way for the establishment of action policies regarding gender inequality and economic disparity, among other things.

scholarly journals A Cloudware Architecture for Collaboration of Multiple AGVs in Indoor Logistics: Case Study in Fabric Manufacturing Enterprises

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2023
Author(s):  
Fatih Okumuş ◽  
Emrah Dönmez ◽  
Adnan Fatih Kocamaz
Keyword(s):  
Collision Avoidance ◽  
Task Allocation ◽  
Simulation Software ◽  
Working Environment ◽  
Automated Guided Vehicles ◽  
Cloud System ◽  
Problem Model ◽  
Logistics Systems ◽  
Optimum Path

In Industry 4.0 compatible workshops, the demand for Automated Guided Vehicles (AGVs) used in indoor logistics systems has increased remarkably. In these indoor logistics systems, it may be necessary to execute multiple transport tasks simultaneously using multiple AGVs. However, some challenges require special solutions for AGVs to be used in industrial autonomous transportation. These challenges can be addressed under four main headings: positioning, optimum path planning, collision avoidance and optimum task allocation. The solutions produced for these challenges may require special studies that vary depending on the type of tasks and the working environment in which AGVs are used. This study focuses on the problem of automated indoor logistics carried out in the simultaneous production of textile finishing enterprises. In the study, a centralized cloud system that enables multiple AGVs to work in collaboration has been developed. The finishing enterprise of a denim manufacturing factory was handled as a case study and modelling of mapping-planning processes was carried out using the developed cloud system. In the cloud system, RestFul APIs, for mapping the environment, and WebSocket methods, to track the locations of AGVs, have been developed. A collaboration module in harmony with the working model has been developed for AGVs to be used for fabric transportation. The collaboration module consists of task definition, battery management-optimization, selection of the most suitable batch trolleys (provides mobility of fabrics for the finishing mills), optimum task distribution and collision avoidance stages. In the collaboration module, all the finishing processes until the product arrives the delivery point are defined as tasks. A task allocation algorithm has been developed for the optimum performance of these tasks. The multi-fitness function that optimizes the total path of the AGVs, the elapsed time and the energy spent while performing the tasks have been determined. An assignment matrix based on K nearest neighbor (k-NN) and permutation possibilities was created for the optimal task allocation, and the most appropriate row was selected according to the optimal path totals of each row in the matrix. The D* Lite algorithm has been used to calculate the optimum path between AGVs and goals by avoiding static obstacles. By developing simulation software, the problem model was adapted and the operation of the cloud system was tested. Simulation results showed that the developed cloud system was successfully implemented. Although the developed cloud system has been applied as a case study in fabric finishing workshops with a complex structure, it can be used in different sectors as its logistic processes are similar.

scholarly journals Path optimization for robots in a constrained workspace

2019 ◽  
Vol 8 (2) ◽  
pp. 89
Author(s):  
Doaa Mahmood Badr ◽  
Abbas Fadhal Mahdi
Keyword(s):  
Optimal Path ◽  
Joint Space ◽  
Task Space ◽  
Dimensional Synthesis ◽  
A Algorithm ◽  
Cartesian Space ◽  
Optimum Path ◽  
Stepper Motors ◽  
Path Planner

In this work, the classical A* algorithm serves as path planner to generate the optimum path that would avoid collisions and take the start, collisions, and goal as an input and give the optimal path as an output. The work was done in a static environment, so the coordinates of the obstacles are predefined for the planner. The obtained path is just a sequence of points in space, and this path may be considered later the task space and the first step for another sequential operation like mapping from Cartesian space to joint space, topology optimization, dimensional synthesis, etc. The case study was Lab-Volt 5150 manipulator; it is an accurate educational five degree of freedom 5DOF stationary robot driven by five stepper motors.

scholarly journals Characterizing molecular flexibility by combining lRMSD measures

2018 ◽  
Author(s):  
F. Cazals ◽  
R. Tetley
Keyword(s):  
Conformational Changes ◽  
Structural Alignment ◽  
Similarity Measures ◽  
Rigid Motion ◽  
Class Ii ◽  
Structural Bioinformatics ◽  
Structural Motifs ◽  
Collective Coordinates ◽  
Quaternary Structures

AbstractThe root mean square deviation (RMSD) and the least RMSD are two widely used similarity measures in structural bioinformatics. Yet, they stem from global comparisons, possibly obliterating locally conserved motifs. We correct these limitations with the so-called combined RMSD, which mixes independent lRMSD measures, each computed with its own rigid motion. The combined RMSD can be used to compare (quaternary) structures based on motifs defined from the sequence (domains, SSE), or to compare structures based on structural motifs yielded by local structural alignment methods.We illustrate the benefits of combined RMSD over the usual RMSD on three problems, namely (i) the analysis of conformational changes based on combined RMSD of rigid structural motifs (case study: a class II fusion protein), (ii) the calculation of structural phylogenies (case study: class II fusion proteins), and (iii) the assignment of quaternary structures for hemoglobin. Using these, we argue that the combined RMSD is a tool a choice to perform positive and negative discrimination of degree of freedom, with applications to the design of move sets and collective coordinates.Combined RMSD are available within the Structural Bioinformatics Library (http://sbl.inria.fr).

scholarly journals A Social–Aware Recommender System Based on User’s Personal Smart Devices

2020 ◽  
Vol 9 (9) ◽  
pp. 519 ◽  
Author(s):  
Soroush Ojagh ◽  
Mohammad Reza Malek ◽  
Sara Saeedi
Keyword(s):  
Social Interactions ◽  
Collaborative Filtering ◽  
Clustering Algorithm ◽  
Contextual Information ◽  
Similarity Measures ◽  
Cold Start ◽  
Smart Devices ◽  
User Similarity ◽  
User Clustering

Providing recommendations in cold start situations is one of the most challenging problems for collaborative filtering based recommender systems (RSs). Although user social context information has largely contributed to the cold start problem, most of the RSs still suffer from the lack of initial social links for newcomers. For this study, we are going to address this issue using a proposed user similarity detection engine (USDE). Utilizing users’ personal smart devices enables the proposed USDE to automatically extract real-world social interactions between users. Moreover, the proposed USDE uses user clustering algorithm that includes contextual information for identifying similar users based on their profiles. The dynamically updated contextual information for the user profiles helps with user similarity clustering and provides more personalized recommendations. The proposed RS is evaluated using movie recommendations as a case study. The results show that the proposed RS can improve the accuracy and personalization level of recommendations as compared to two other widely applied collaborative filtering RSs. In addition, the performance of the USDE is evaluated in different scenarios. The conducted experimental results on USDE show that the proposed USDE outperforms widely applied similarity measures in cold start and data sparsity situations.

scholarly journals Rank-Similarity Measures for Comparing Gene Prioritizations: A Case Study in Autism

2020 ◽  
Author(s):  
Concettina Guerra ◽  
Sarang Joshi ◽  
Yinquan Lu ◽  
Francesco Palini ◽  
Umberto Ferraro Petrillo ◽  
...  
Keyword(s):  
Similarity Measures
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