Transparency in Complex Computational Systems

Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s..

European technical guidance document for the flexible scope accreditation of laboratories quantifying GMOs

The aim of this guidance document is to facilitate harmonised flexible scope accreditation within Europe, according to ISO/IEC 17025:2005 related to quantitative testing of genetically modified organisms (GMOs) by quantitative real-time polymerase chain reaction (qPCR) for GM events authorised in the EU or which are in the authorisation process. This document gives guidance to and is intended for laboratories that are considering to acquire a flexible scope of accreditation according to ISO/IEC 17025. At the same time it aims to provide information for assessors involved in the accreditation process of these laboratories. This guidance document has been written by members of the Task Force (TF) Flexible scope accreditation, which has been initiated by European Commission, Joint Research Centre, Institute for Reference Materials and Measurements (EC JRC-IRMM, Geel, BE). After an extensive commenting phase it has been submitted to the European co-operation for Accreditation (EA) in February 2013 for consideration as an EA guidance document.JRC.D.2-Standards for Innovation and sustainable Developmen

Molecular and morphological phylogenetics of the digitate-tubered clade within subtribe Orchidinae s.s. (Orchidaceae: Orchideae)

The digitate-tubered clade (Dactylorhiza s.l. plus Gymnadenia s.l.) within subtribe Orchidinae is an important element of the North-temperate orchid flora and has become a model system for studying the genetic and epigenetic consequences of organism-wide ploidy change. Here, we integrate morphological phylogenetics with Sanger sequencing of nrITS and the plastid region trnL-F in order to explore phylogenetic relationships and phenotypic character evolution within the clade. The resulting morphological phylogenies are strongly incongruent with the molecular phylogenies, instead reconstructing through parsimony the genus-level boundaries recognised by traditional 20th Century taxonomy. They raise fresh doubts concerning whether Pseudorchis is sister to Platanthera or to Dactylorhiza plus Gymnadenia. Constraining the morphological matrix to the topology derived from ITS sequences increased tree length by 20%, adding considerably to the already exceptional level of phenotypic homoplasy. Both molecular and morphological trees agree that D. viridis and D. iberica are the earliest- diverging species within Dactylorhiza (emphasising the redundancy of the former genus Coeloglossum). Morphology and ITS both suggest that the former genus Nigritella is nested within (and thus part of) Gymnadenia, the Pyrenean endemic 'N.' gabasiana apparently forming a molecular bridge between the two radically contrasting core phenotypes. Comparatively short subtending molecular branches plus widespread (though sporadic) hybridisation indicate that Dactylorhiza and Gymnadenia approximate the minimum level of molecular divergence acceptable in sister genera. They share similar tuber morphologies and base chromosome numbers, and both genera are unusually prone to polyploid speciation. Another prominent feature of multiple speciation events within Gymnadenia is floral paedomorphosis. The 'traditional' morphological and candidate-gene approaches to phylogeny reconstruction are critically appraised.Peer reviewedFinal Published versio

Development of a heuristic methodology for designing measurement networks for precise metal accounting

This thesis investigates the development of a heuristic based methodology for designing measurement networks with application to the precise accounting of metal flows in mineral beneficiation operations. The term 'measurement network' is used to refer to the 'system of sampling and weight measurement equipment' from which process measurements are routinely collected. Metal accounting is defined as the estimation of saleable metal in the mine and subsequent process streams over a defined time period. One of the greatest challenges facing metal accounting is 'uncertainty' that is caused by random errors, and sometimes gross errors, that obtain in process measurements. While gross errors can be eliminated through correct measurement practices, random errors are an inherent property of measured data and they can only be minimised. Two types of rules for designing measurement networks were considered. The first type of rules referred to as 'expert heuristics' consists of (i) Code of Practice Guidelines from the AMIRA P754 Code, and (ii) prevailing accounting practices from the mineral and metallurgical processing industry which were obtained through a questionnaire survey campaign. It was hypothesised that experts in the industry design measurement networks using rules or guidelines that ensure requisite quality in metal accounting. The second set of rules was derived from the symbolic manipulation of the general steady-state linear data reconciliation solution as well as from an intensive numerical study on the variance reduction response of measurements after data reconciliation conducted in this study. These were referred to as 'mathematical heuristics' and are based on the general principle of variance reduction through data reconciliation. It was hypothesised that data reconciliation can be used to target variance reduction for selected measurements by exploiting characteristics of entire measurement networks as well as individual measurement characteristics

A Fake Profile Detection Model Using Multistage Stacked Ensemble Classification

Fake profile identification on social media platforms is essential for preserving a reliable online community. Previous studies have primarily used conventional classifiers for fake account identification on social networking sites, neglecting feature selection and class balancing to enhance performance. This study introduces a novel multistage stacked ensemble classification model to enhance fake profile detection accuracy, especially in imbalanced datasets. The model comprises three phases: feature selection, base learning, and meta-learning for classification. The novelty of the work lies in utilizing chi-squared feature-class association-based feature selection, combining stacked ensemble and cost-sensitive learning. The research findings indicate that the proposed model significantly enhances fake profile detection efficiency. Employing cost-sensitive learning enhances accuracy on the Facebook, Instagram, and Twitter spam datasets with 95%, 98.20%, and 81% precision, outperforming conventional and advanced classifiers. It is demonstrated that the proposed model has the potential to enhance the security and reliability of online social networks, compared with existing models

Handwritten Devanagari numeral recognition

Optical character recognition (OCR) plays a very vital role in today’s modern world. OCR can be useful for solving many complex problems and thus making human’s job easier. In OCR we give a scanned digital image or handwritten text as the input to the system. OCR can be used in postal department for sorting of the mails and in other offices. Much work has been done for English alphabets but now a day’s Indian script is an active area of interest for the researchers. Devanagari is on such Indian script. Research is going on for the recognition of alphabets but much less concentration is given on numerals. Here an attempt was made for the recognition of Devanagari numerals. The main part of any OCR system is the feature extraction part because more the features extracted more is the accuracy. Here two methods were used for the process of feature extraction. One of the method was moment based method. There are many moment based methods but we have preferred the Tchebichef moment. Tchebichef moment was preferred because of its better image representation capability. The second method was based on the contour curvature. Contour is a very important boundary feature used for finding similarity between shapes. After the process of feature extraction, the extracted feature has to be classified and for the same Artificial Neural Network (ANN) was used. There are many classifier but we preferred ANN because it is easy to handle and less error prone and apart from that its accuracy is much higher compared to other classifier. The classification was done individually with the two extracted features and finally the features were cascaded to increase the accuracy

Population-based JPEG Image Compression: Problem Re-Formulation

The JPEG standard is widely used in different image processing applications. One of the main components of the JPEG standard is the quantisation table (QT) since it plays a vital role in the image properties such as image quality and file size. In recent years, several efforts based on population-based metaheuristic (PBMH) algorithms have been performed to find the proper QT(s) for a specific image, although they do not take into consideration the user's opinion. Take an android developer as an example, who prefers a small-size image, while the optimisation process results in a high-quality image, leading to a huge file size. Another pitfall of the current works is a lack of comprehensive coverage, meaning that the QT(s) can not provide all possible combinations of file size and quality. Therefore, this paper aims to propose three distinct contributions. First, to include the user's opinion in the compression process, the file size of the output image can be controlled by a user in advance. Second, to tackle the lack of comprehensive coverage, we suggest a novel representation. Our proposed representation can not only provide more comprehensive coverage but also find the proper value for the quality factor for a specific image without any background knowledge. Both changes in representation and objective function are independent of the search strategies and can be used with any type of population-based metaheuristic (PBMH) algorithm. Therefore, as the third contribution, we also provide a comprehensive benchmark on 22 state-of-the-art and recently-introduced PBMH algorithms on our new formulation of JPEG image compression. Our extensive experiments on different benchmark images and in terms of different criteria show that our novel formulation for JPEG image compression can work effectively.Comment: 39 pages, this paper is submitted to the related journa

2019 IMSAloquium: Student Inquiry and Research Program and IMSA Internship Program

Welcome to IMSAloquium 2019! This is IMSA’s 32nd year of leading in educational innovation, the 31st year of the IMSA Student Inquiry and Research (SIR) Program, and the first year of the newly imagined IMSA Internship Program.https://digitalcommons.imsa.edu/archives_sir/1029/thumbnail.jp

2018 IMSAloquium, Student Investigation Showcase

This is IMSA\u27s 31st year of leading in educational innovation and the 30th year of the Student Inquiry and Research Program (SIR)! ... These studies have all happened during the past year in a variety of laboratories, real or virtual, on and off campus. Students were asked to not only learn a great deal about complex topics, but to contribute to them in meaningful ways. The presentations you hear today reflect the various stages of their work on a myriad of projects.https://digitalcommons.imsa.edu/archives_sir/1028/thumbnail.jp