Pattern Recognition on Vehicle Number Plates Using a Fast Match Algorithm

Computer Vision was the fast developing apps in the world, it is make people make a lot of new algorithm. Before we can use in out app, we need to test the algorithm to make sure how effective and optimal the algorithm to solve every case we given. A lot of traffic system has implemented computer vision, they need fast and can work in every condition, because every vehicle who pass needs to be recognized. In this research Fast Match algorithm was chosen because they can solve some test and make a lot of image have a similarity with the template. It makes accuracy of the data can be achieved with this algorithm. For example on of the sample was have a SAD point for 0.5 and Overlap Error for 0.5 and can run in standard computer just for a couple second. It makes the template and the original image has a little similarity.

Match Algorithms for Scientific Names in FlorItaly, the Portal to the Flora of Italy

Scientific names are not part of everyday language in any modern country, and their input as strings in a query system can be easily associated with typographical errors. While globally unique identifiers univocally address a taxon name, they can hardly be used for querying a database manually. Thus, matching algorithms are often used to overcome misspelled names in query systems in several data repositories worldwide. In order to improve users’ experience in the use of FlorItaly, the Portal to the Flora of Italy, a near match algorithm to resolve misspelled scientific names has been integrated in the query systems. In addition, a novel tool in FlorItaly, capable of rapidly aligning any list of names to the nomenclatural backbone provided by the national checklists, has been developed. This manuscript aims at describing the potential of these new tools.

Creating enriched training sets of eligible studies for large systematic reviews: the utility of PubMed's Best Match algorithm

Introduction Solutions like crowd screening and machine learning can assist systematic reviewers with heavy screening burdens but require training sets containing a mix of eligible and ineligible studies. This study explores using PubMed's Best Match algorithm to create small training sets containing at least five relevant studies. Methods Six systematic reviews were examined retrospectively. MEDLINE searches were converted and run in PubMed. The ranking of included studies was studied under both Best Match and Most Recent sort conditions. Results Retrieval sizes for the systematic reviews ranged from 151 to 5,406 records and the numbers of relevant records ranged from 8 to 763. The median ranking of relevant records was higher in Best Match for all six reviews, when compared with Most Recent sort. Best Match placed a total of thirty relevant records in the first fifty, at least one for each systematic review. Most Recent sorting placed only ten relevant records in the first fifty. Best Match sorting outperformed Most Recent in all cases and placed five or more relevant records in the first fifty in three of six cases. Discussion Using a predetermined set size such as fifty may not provide enough true positives for an effective systematic review training set. However, screening PubMed records ranked by Best Match and continuing until the desired number of true positives are identified is efficient and effective. Conclusions The Best Match sort in PubMed improves the ranking and increases the proportion of relevant records in the first fifty records relative to sorting by recency.

Optimized Distributed Subgraph Matching Algorithm Based on Partition Replication

At present, with the explosive growth of data scale, subgraph matching for massive graph data is difficult to satisfy with efficiency. Meanwhile, the graph index used in existing subgraph matching algorithm is difficult to update and maintain when facing dynamic graphs. We propose a distributed subgraph matching algorithm based on Partition Replica (noted as PR-Match) to process the partition and storage of large-scale data graphs. The PR-Match algorithm first splits the query graph into sub-queries, then assigns the sub-query to each node for sub-graph matching, and finally merges the matching results. In the PR-Match algorithm, we propose a heuristic rule based on prediction cost to select the optimal merging plan, which greatly reduces the cost of merging. In order to accelerate the matching speed of the sub-query graph, a vertex code based on the vertex neighbor label signature is proposed, which greatly reduces the search space for the subquery. As the vertex code is based on the increment, the problem that the feature-based graph index is difficult to maintain in the face of the dynamic graph is solved. An abundance of experiments on real and synthetic datasets demonstrate the high efficiency and strong scalability of the PR-Match algorithm when handling large-scale data graphs.

Misunderstanding the Match: Do Students Create Rank Lists Based on True Preferences?

Introduction: The “stable marriage” algorithm underlying the National Residency Match Program (NRMP) has been shown to create optimal outcomes when students submit true preference lists. Previous research has shown students may allow external information to affect their rank lists. The objective of this study was to determine whether medical students consistently make rank lists that reflect their true preferences. Methods: A voluntary online survey was sent to third-year students at a single midwestern medical school. Students were given hypothetical scenarios that either should or should not affect their true residency preferences and rated the importance of six factors to their final rank list. The survey was edited by a group of education scholars and revised based on feedback from a pilot with current postgraduate year 1 residents. Results: Of 175 students surveyed, 140 (80%) responded; 63% (88/140) reported that their “perceived competitiveness” would influence their rank list at least a “moderate amount. Of 135 students, 31 (23%) moved a program lower on their list if they learned they were ranked “low” by that program, while 6% (8/135) of respondents moved a program higher if they learned they were ranked “at the top of the list.” Participants responded similarly (κ = 0.71) when presented with scenarios asking what they would do vs what a classmate should do. Conclusion: Students’ hypothetical rank lists did not consistently match their true residency preferences. These results may stem from a misunderstanding of the Match algorithm. Medical schools should consider augmenting explicit education related to the NRMP Match algorithm to ensure optimal outcomes for students.