Detection and characterization of singly deuterated silylene, SiHD, via optical spectroscopy

abstract: Singly deuterated silylene has been detected and characterized in the gas-phase using high-resolution, two-dimensional, optical spectroscopy. Rotationally resolved lines in the 0[0 over 0][˜ over X][superscript 1]A′ → [˜ over A][superscript 1]A′′000X˜1A′→A˜1A″ band are assigned to both c-type perpendicular transition and additional parallel, axis-switching induced bands. The extracted rotational constants were combined with those for SiH[subscript 2] and SiD[subscript 2] to determine an improved equilibrium bond length, r[subscript SiH], and bond angle, θ, of 1.5137 ± 0.0003 Å and 92.04° ± 0.05°, and 1.4853 ± 0.0005 Å and 122.48° ± 0.08° for the [˜ over X][superscript 1]A′(0, 0, 0) and [˜ over A][superscript 1]A″(0, 0, 0) state respectively. The dispersed fluorescence consists of a long progression in the [˜ over A][superscript 1]A″(0,0,0) → [˜ over X][superscript 1]A′(0, ν[subscript 2], 0) emission which was analyzed to produce vibrational parameters. A strong quantum level dependence of the rotationally resolved radiative decay curves is analyzed.This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. The following article appeared in The Journal of Chemical Physics and may be found at http://aip.scitation.org/doi/10.1063/1.4954702

Content Detection in Handwritten Documents

abstract: Handwritten documents have gained popularity in various domains including education and business. A key task in analyzing a complex document is to distinguish between various content types such as text, math, graphics, tables and so on. For example, one such aspect could be a region on the document with a mathematical expression; in this case, the label would be math. This differentiation facilitates the performance of specific recognition tasks depending on the content type. We hypothesize that the recognition accuracy of the subsequent tasks such as textual, math, and shape recognition will increase, further leading to a better analysis of the document. Content detection on handwritten documents assigns a particular class to a homogeneous portion of the document. To complete this task, a set of handwritten solutions was digitally collected from middle school students located in two different geographical regions in 2017 and 2018. This research discusses the methods to collect, pre-process and detect content type in the collected handwritten documents. A total of 4049 documents were extracted in the form of image, and json format; and were labelled using an object labelling software with tags being text, math, diagram, cross out, table, graph, tick mark, arrow, and doodle. The labelled images were fed to the Tensorflow’s object detection API to learn a neural network model. We show our results from two neural networks models, Faster Region-based Convolutional Neural Network (Faster R-CNN) and Single Shot detection model (SSD).Dissertation/ThesisMasters Thesis Computer Science 201

Detection of Capillary-Mediated Energy Fields on a Grain Boundary Groove: Solid–Liquid Interface Perturbations

abstract: Grain boundary grooves are common features on polycrystalline solid–liquid interfaces. Their local microstructure can be closely approximated as a “variational” groove, the theoretical profile for which is analyzed here for its Gibbs–Thomson thermo-potential distribution. The distribution of thermo-potentials for a variational groove exhibits gradients tangential to the solid–liquid interface. Energy fluxes stimulated by capillary-mediated tangential gradients are divergent and thus capable of redistributing energy on real or simulated grain boundary grooves. Moreover, the importance of such capillary-mediated energy fields on interfaces is their influence on stability and pattern formation dynamics. The capillary-mediated field expected to be present on a stationary grain boundary groove is verified quantitatively using the multiphase-field approach. Simulation and post-processing measurements fully corroborate the presence and intensity distribution of interfacial cooling, proving that thermodynamically-consistent numerical models already support, without any modification, capillary perturbation fields, the existence of which is currently overlooked in formulations of sharp interface dynamic models

Estimating labels from label proportions

Consider the following problem: given sets of unlabeled observations, each set with known label proportions, predict the labels of another set of observations, also with known label proportions. This problem appears in areas like e-commerce, spam filtering and improper content detection. We present consistent estimators which can reconstruct the correct labels with high probability in a uniform convergence sense. Experiments show that our method works well in practice.

Design of Moisture Content Detection System

AbstractIn this paper, a method for measuring the moisture content of grain was presented based on single chip microcomputer and capacitive sensor. The working principle of measuring moisture content is introduced and a concentric cylinder type of capacitive sensor is designed, the signal processing circuits of system are described in details. System is tested in practice and discussions are made on the various factors affecting the capacitive measuring of grain moisture based on the practical experiments, experiment results showed that the system has high measuring accuracy and good controlling capacity

Recalibrating classifiers for interpretable abusive content detection

Dataset and code for the paper, 'Recalibrating classifiers for interpretable abusive content detection' by Vidgen et al. (2020) -- to appear at the NLP + CSS workshop at EMNLP 2020. We provide: 1,000 annotated tweets, sampled using the Davidson classifier with 20 0.05 increments (50 from each) from a dataset of tweets directed against MPs in the UK 2017 General Election 1,000 annotated tweets, sampled using the Perspective classifier with 20 0.05 increments (50 from each) from a dataset of tweets directed against MPs in the UK 2017 General Election Code for recalibration in R and STAN. Annotation guidelines for both datasets. Paper abstract We investigate the use of machine learning classifiers for detecting online abuse in empirical research. We show that uncalibrated classifiers (i.e. where the 'raw' scores are used) align poorly with human evaluations. This limits their use to understand the dynamics, patterns and prevalence of online abuse. We examine two widely used classifiers (created by Perspective and Davidson et al.) on a dataset of tweets directed against candidates in the UK's 2017 general election. A Bayesian approach is presented to recalibrate the raw scores from the classifiers, using probabilistic programming and newly annotated data. We argue that interpretability evaluation and recalibration is integral to the application of abusive content classifiers

Intestinal content detection in capsule endoscopy using robust features

This work covers two aspects. First, it generally compares and summarizes the similarities and differences of state of the art feature detector and descriptor and second it presents a novel approach of detecting intestinal content (in particular bubbles) in capsule endoscopy images. Feature detectors and descriptors providing invariance to change of perspective, scale, signal-noise-ratio and lighting conditions are important and interesting topics in current research and the number of possible applications seems to be numberless. After analysing a selection of in the literature presented approaches, this work investigates in their suitability for applications information extraction in capsule endoscopy images. Eventually, a very good performing detector of intestinal content in capsule endoscopy images is presented. A accurate detection of intestinal content is crucial for all kinds of machine learning approaches and other analysis on capsule endoscopy studies because they occlude the field of view of the capsule camera and therefore those frames need to be excluded from analysis. As a so called "byproduct" of this investigation a graphical user interface supported Feature Analysis Tool is presented to execute and compare the discussed feature detectors and descriptor on arbitrary images, with configurable parameters and visualized their output. As well the presented bubble classifier is part of this tool and if a ground truth is available (or can also be generated using this tool) a detailed visualization of the validation result will be performed.Nota: Aquest document conté originàriament altre material i/o programari només consultable a la Biblioteca de Ciència i Tecnologia

Fake Content Detection in the Information Exponential Spreading Era

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementRecent years brought an information access democratization, allowing people to access a huge amount of information and the ability to share it, in a way that it can easily reach millions of people in a very short time. This allows to have right and wrong uses of this capabilities, that in some cases can be used to spread malicious content to achieve some sort of goal. Several studies have been made regarding text mining and sentiment analysis, aiming to spot fake information and avoid misinformation spreading. The trustworthiness and veracity of the information that is accessible to people is getting increasingly important, and in some cases critical, and can be seen has a huge challenge for the current digital era. This problem might be addressed with the help of science and technology. One question that we can do to ourselves is: How do we guarantee that there is a correct use of information, and that people can trust in the veracity of it? Using mathematics and statistics, combined with machine learning classification and predictive algorithms, using the current computation power of information systems, can help minimize the problem, or at least spot the potential fake information. One suggests developing a research work that aims to reach a model for the prediction of a given text content is trustworthy. The results were promising reaching a predicting model with good performance

Plagiarism Detection Avoidance Methods and Countermeasures

Plagiarism is a major problem that educators face in the information age. Today\u27s plagiarist has a near limitless supply of well-written articles via the internet. Due to the scale of the problem, detecting plagiarism has now become the domain of the computer scientist rather than the educator. With the use of computers, documents can be conveniently scanned into a plagiarism detection system that references public web pages, academic journals, and even previous students\u27 papers, acting as an all-seeing eye. However, plagiarists can overcome these digital content detection systems with the use of clever masking and substitutions techniques. These systems cost universities tens of thousands of dollars, and also infringe upon intellectual property ownership rights without the informed consent of individual students. In this work, we examine the efficacy of commercial plagiarism detection systems when used against some selected masking techniques, and then present a simple countermeasure to combat the aforementioned detection avoidance technique