Types Of Annotations Research Articles

Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity

BackgroundAdaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover mutations that confer phenotypic functions of interest. However, the task of finding and understanding all beneficial mutations of an ALE experiment remains an open challenge for the field. To provide for better results than traditional methods of ALE mutation analysis, this work applied enrichment methods to mutations described by a multiscale annotation framework and a consolidated set of ALE experiment conditions. A total of 25,321 unique genome annotations from various sources were leveraged to describe multiple scales of mutated features in a set of 35 Escherichia coli based ALE experiments. These experiments totalled 208 independent evolutions and 2641 mutations. Additionally, mutated features were statistically associated across a total of 43 unique experimental conditions to aid in deconvoluting mutation selection pressures.ResultsIdentifying potentially beneficial, or key, mutations was enhanced by seeking coding and non-coding genome features significantly enriched by mutations across multiple ALE replicates and scales of genome annotations. The median proportion of ALE experiment key mutations increased from 62%, with only small coding and non-coding features, to 71% with larger aggregate features. Understanding key mutations was enhanced by considering the functions of broader annotation types and the significantly associated conditions for key mutated features. The approaches developed here were used to find and characterize novel key mutations in two ALE experiments: one previously unpublished with Escherichia coli grown on glycerol as a carbon source and one previously published with Escherichia coli tolerized to high concentrations of L-serine.ConclusionsThe emergent adaptive strategies represented by sets of ALE mutations became more clear upon observing the aggregation of mutated features across small to large scale genome annotations. The clarification of mutation selection pressures among the many experimental conditions also helped bring these strategies to light. This work demonstrates how multiscale genome annotation frameworks and data-driven methods can help better characterize ALE mutations, and thus help elucidate the genotype-to-phenotype relationship of the studied organism.

DicomAnnotator: a Configurable Open-Source Software Program for Efficient DICOM Image Annotation.

Modern, supervised machine learning approaches to medical image classification, image segmentation, and object detection usually require many annotated images. As manual annotation is usually labor-intensive and time-consuming, a well-designed software program can aid and expedite the annotation process. Ideally, this program should be configurable for various annotation tasks, enable efficient placement of several types of annotations on an image or a region of an image, attribute annotations to individual annotators, and be able to display Digital Imaging and Communications in Medicine (DICOM)-formatted images. No current open-source software program fulfills these requirements. To fill this gap, we developed DicomAnnotator, a configurable open-source software program for DICOM image annotation. This program fulfills the above requirements and provides user-friendly features to aid the annotation process. In this paper, we present the design and implementation of DicomAnnotator. Using spine image annotation as a test case, our evaluation showed that annotators with various backgrounds can use DicomAnnotator to annotate DICOM images efficiently. DicomAnnotator is freely available at https://github.com/UW-CLEAR-Center/DICOM-Annotator under the GPLv3 license.

The digital common read: Creating a space for authentic engagement with social annotation

This paper describes a new approach to the common read at the University of New Haven, USA. Faculty and students choose a text in the public domain, place it on the web, and ask incoming first-year honors students to annotate the text collaboratively using Hypothesis. The choice of text, placement on the web, and editorial introduction can all affect rates of participation and the type of annotations that students choose to share. This method is a low-cost way of creating space for a social intellectual experience prior to arriving on campus.

Key Elements for a Doctoral Annotated Bibliography

The purpose of this article is to present an overview of why annotated bibliographies are needed in higher education, especially in doctoral education programs. The necessity for emerging scholars to build an annotated bibliography is paramount to the development of their research study. However, there are numerous types of annotated bibliographies. This article will discuss the different types of annotations and which format best facilitates the development of a dissertation.

A new fine-grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction.

PremiseHerbarium specimens represent an outstanding source of material with which to study plant phenological changes in response to climate change. The fine‐scale phenological annotation of such specimens is nevertheless highly time consuming and requires substantial human investment and expertise, which are difficult to rapidly mobilize.MethodsWe trained and evaluated new deep learning models to automate the detection, segmentation, and classification of four reproductive structures of Streptanthus tortuosus (flower buds, flowers, immature fruits, and mature fruits). We used a training data set of 21 digitized herbarium sheets for which the position and outlines of 1036 reproductive structures were annotated manually. We adjusted the hyperparameters of a mask R‐CNN (regional convolutional neural network) to this specific task and evaluated the resulting trained models for their ability to count reproductive structures and estimate their size.ResultsThe main outcome of our study is that the performance of detection and segmentation can vary significantly with: (i) the type of annotations used for training, (ii) the type of reproductive structures, and (iii) the size of the reproductive structures. In the case of Streptanthus tortuosus, the method can provide quite accurate estimates (77.9% of cases) of the number of reproductive structures, which is better estimated for flowers than for immature fruits and buds. The size estimation results are also encouraging, showing a difference of only a few millimeters between the predicted and actual sizes of buds and flowers.DiscussionThis method has great potential for automating the analysis of reproductive structures in high‐resolution images of herbarium sheets. Deeper investigations regarding the taxonomic scalability of this approach and its potential improvement will be conducted in future work.

무게중심을 활용한 모션 생성 기술

When a character’s pose changes, its center of mass(COM) also changes. The change of COM has distinctive patterns corresponding to various motion types like walking, running or sitting. Thus the motion type can be predicted by using COM movement. We propose a motion generator that uses character’s center of mass information. This generator can generate various motions without annotated action type labels. Thus dataset for training and running can be generated full-automatically. Our neural network model takes the motion history of the character and its center of mass information as inputs and generates a full-body pose for the current frame, and is trained using simple Convolutional Neural Network(CNN) that performs 1D convolution to deal with time-series motion data.

Model elements identification using neural networks: a comprehensive study

Modeling of natural language requirements, especially for a large system, can take a significant amount of effort and time. Many automated model-driven approaches partially address this problem. However, the application of state-of-the-art neural network architectures to automated model element identification tasks has not been studied. In this paper, we perform an empirical study on automatic model elements identification for component state transition models from use case documents. We analyzed four different neural network architectures: feed forward neural network, convolutional neural network, recurrent neural network (RNN) with long short-term memory, and RNN with gated recurrent unit (GRU), and the trade-offs among them using six use case documents. We analyzed the effect of factors such as types of splitting, types of predictions, types of designs, and types of annotations on performance of neural networks. The results of neural networks on the test and unseen data showed that RNN with GRU is the most effective neural network architecture. However, the factors that result in effective predictions of neural networks are dependent on the type of the model element.

Looking behind the scenes: an analysis of dialogue lists for Ocean's Eleven

ABSTRACT In this article we consider the process involved in creating foreign language versions of feature films by describing and analysing paratextual elements in the dialogue list for the audiovisual source text Ocean's Eleven. We aim to assess the potential translational impact of this accompanying material on the final product and to offer insights into the complex mediation process that lies at the heart of the linguistic transfer in audiovisual translation. Our analysis initially starts out from Scene 12, the common thread focused on in the volume, but widens to consider the strategic role of paratextual elements such as translator's notes in the translation of dialogues in the whole film. The approach adopted is descriptive-qualitative as it aims to first disclose the most typical and frequent annotation types and highlight the semantic, pragmatic, and narrative aims they pursue in each case, and then to relate them with the options adopted in the dubbed dialogues. More than offering conclusive results, the study aims at opening up research into the process of translation in the context of audiovisual communication, by means of integrating descriptive analyses of the translated product and an informed consideration of professional practices that are part and parcel of the post-production process, focusing in particular on paratextual materials for the use of translators.

Automated quantification and architectural pattern detection of hepatic fibrosis in NAFLD

Accurate detection and quantification of hepatic fibrosis remain essential for assessing the severity of non-alcoholic fatty liver disease (NAFLD) and its response to therapy in clinical practice and research studies. Our aim was to develop an integrated artificial intelligence-based automated tool to detect and quantify hepatic fibrosis and assess its architectural pattern in NAFLD liver biopsies. Digital images of the trichrome-stained slides of liver biopsies from patients with NAFLD and different severity of fibrosis were used. Two expert liver pathologists semi-quantitatively assessed the severity of fibrosis in these biopsies and using a web applet provided a total of 987 annotations of different fibrosis types for developing, training and testing supervised machine learning models to detect fibrosis. The collagen proportionate area (CPA) was measured and correlated with each of the pathologists semi-quantitative fibrosis scores. Models were created and tested to detect each of six potential fibrosis patterns. There was good to excellent correlation between CPA and the pathologist score of fibrosis stage. The coefficient of determination (R2) of automated CPA with the pathologist stages ranged from 0.60 to 0.86. There was considerable overlap in the calculated CPA across different fibrosis stages. For identification of fibrosis patterns, the models areas under the receiver operator curve were 78.6% for detection of periportal fibrosis, 83.3% for pericellular fibrosis, 86.4% for portal fibrosis and >90% for detection of normal fibrosis, bridging fibrosis, and presence of nodule/cirrhosis. In conclusion, an integrated automated tool could accurately quantify hepatic fibrosis and determine its architectural patterns in NAFLD liver biopsies.

The Open Images Dataset V4

We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding an initial design bias. Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship annotations involving 57 classes. For object detection in particular, we provide 15x more bounding boxes than the next largest datasets (15.4M boxes on 1.9M images). The images often show complex scenes with several objects (8 annotated objects per image on average). We annotated visual relationships between them, which support visual relationship detection, an emerging task that requires structured reasoning. We provide in-depth comprehensive statistics about the dataset, we validate the quality of the annotations, we study how the performance of several modern models evolves with increasing amounts of training data, and we demonstrate two applications made possible by having unified annotations of multiple types coexisting in the same images. We hope that the scale, quality, and variety of Open Images V4 will foster further research and innovation even beyond the areas of image classification, object detection, and visual relationship detection.

Review of the Studies on Multimedia Annotation in China Over the Past Two Decades

This paper reviews the studies on multimedia annotation from the following perspectives: theoretical construct on multimedia annotation, the empirical research on different effectiveness of types of multimedia annotation on the reading/listening comprehension and vocabulary acquisition, which aims to help the overseas scholars to know the present situation and the direction for the future research on multimedia annotation in China. The source data is mainly from the articles on multimedia annotation published in CNKI from Jan. 2000 to Jan. 2020 (www.cnki.net), and the situation is as follow: (1) most domestic scholars introduce and verify the multimedia annotation theories through empirical researches; (2) the researchers focus more on the effects of the types of multimedia annotation on the reading comprehension and the vocabulary acquisition compared to the effects on listening comprehension, and it could be classified into three effects: positive, negative and no effect, but the factors causing the above effects have not been systematically studied or summarized; (3) most scholars mainly take intermediate and advanced language level learners as the research subjects, the lower level learners should be also considered; (4) in the research of the types of the multimedia annotation, the theme of the research materials in reading/listening comprehension would be a new prospective which has been rarely studied compared to other types. To sum up, more researches should be done to investigate the effects of different types of multimedia annotation to the reading/listening comprehension and vocabulary acquisition, which should cover wider range of subjects and consider different types of experiment materials. And the author believes that it will enhance the EFL learning and teaching in the future.

An annotated type catalogue of amphibians and reptiles collected by Nikolay A. Zarudny in Iran and Middle Asia.

A complete catalogue is provided for the type specimens of amphibians and reptiles collected by Nikolay A. Zarudny and stored mostly in the herpetological collection of the Zoological Institute, Russian Academy of Sciences, St. Petersburg, Russia (ZISP), as of August 2018. The collection contains 270 type specimens, representing 51 taxa (species and/or subspecies) of one family of turtles, one amphisbaenia family, five lizard families and four snake families from 74 type localities. As a result of studying Zarudny's collections, mainly from Iran, was the description by A. M. Nikolsky of two new genera, Microgecko and Diplometopon, and 42 species and subspecies (varieties). Twenty-two of these taxa are regarded currently as valid.

Influential factors of working adults' perceptions of mobile-assisted vocabulary learning with multimedia annotations

With the development of technology-enhanced language learning, there have been increasingly more studies on mobile-assisted vocabulary learning with multimedia annotations. However, little research has been conducted to explore working adults' use of multimedia annotations in authentic mobile learning environments, investigate their perceptions of diverse types of multimedia annotations, or discuss the factors that may influence their perceptions of multimedia annotations. To fill in these research gaps, the researchers of this project developed a multimedia-annotation-enhanced vocabulary learning app, iWORDS, and invited 24 working adults in Hong Kong to learn vocabulary using this app. After experiencing mobile-assisted vocabulary learning with four types of multimedia annotations (i.e., the textual, the pictorial, the text-plus-GIF and the text-plus-video annotations), the participants were interviewed about their perceptions and preferences. The results indicated that working adults used textual annotations most frequently but showed strongest preference for text-plus-GIF annotations. Four factors, content quality, attractiveness, efficiency and cognitive stimuli, played important roles in influencing working adults' preferences and perceptions of multimedia annotations. Based on the research results, we proposed some suggestions for the development, selection and usage of multimedia annotations in mobile-assisted vocabulary learning.

Influential factors of working adults' perceptions of mobile-assisted vocabulary learning with multimedia annotations

With the development of technology-enhanced language learning, there have been increasingly more studies on mobile-assisted vocabulary learning with multimedia annotations. However, little research has been conducted to explore working adults' use of multimedia annotations in authentic mobile learning environments, investigate their perceptions of diverse types of multimedia annotations, or discuss the factors that may influence their perceptions of multimedia annotations. To fill in these research gaps, the researchers of this project developed a multimedia-annotation-enhanced vocabulary learning app, iWORDS, and invited 24 working adults in Hong Kong to learn vocabulary using this app. After experiencing mobile-assisted vocabulary learning with four types of multimedia annotations (i.e., the textual, the pictorial, the text-plus-GIF and the text-plus-video annotations), the participants were interviewed about their perceptions and preferences. The results indicated that working adults used textual annotations most frequently but showed strongest preference for text-plus-GIF annotations. Four factors, content quality, attractiveness, efficiency and cognitive stimuli, played important roles in influencing working adults' preferences and perceptions of multimedia annotations. Based on the research results, we proposed some suggestions for the development, selection and usage of multimedia annotations in mobile-assisted vocabulary learning.

Лингводидактические свойства корпусных технологий

For the last two decades, corpus technologies, understood as a combination of means and methods of processing and analyzing data of electronic linguistic corpora, as a type of information and communication technology, have attracted great interest of researchers and teachers of foreign languages.We explain the concepts of corpus linguistics, corpus technology, linguistic corpus, concordance. The methods of studying case technologies, which are an annotation, abstraction, and analysis, are considered. The advantages of linguistic corpora are given. The history of the emergence and development of linguistic electronic cases from the pre-digital to digital period is described. Minimum requirements for the corpus of texts are presented. They include representativeness, known volume of the corpus, electronic form, annotation and balance. We consider the typology of linguistic corpora. According to the language of the texts in corpora, there are monolingual and multilingual corpora, which in turn are divided into mixed and parallel ones. According to language data, there are written, oral and mixed corpora. Corpora can be annotated and non-annotated. There are three types of annotation: linguistic, metatextual, and extralinguistic. According to the parameter of representation of the language material of a corpus, there are fragmented and non-fragmented ones. According to the type of access, they are classified as open and restricted. According to the genre representation, linguistic corpora are diverse. The size of a corpus should distinguish between representative, illustrative and monitoring types of corpora. The didactic properties of corpus technologies in the field of teaching a foreign language are studied. The division of the linguodidactic properties of case technologies into mandatory and optional is proposed.

Operational Semantics of Annotated Reflex Programs

Reflex is a process-oriented language that provides a design of easy-to-maintain control software for programmable logic controllers. The language has been successfully used in a several reliability critical control systems, e. g. control software for a silicon single crystal growth furnace and electronic equipment control system. Currently, the main goal of the Reflex language project is to develop formal verification methods for Reflex programs in order to guarantee increased reliability of the software created on its basis. The paper presents the formal operational semantics of Reflex programs extended by annotations describing the formal specification of software requirements as a necessary basis for the application of such methods. A brief overview of the Reflex language is given and a simple example of its use – a control program for a hand dryer – is provided. The concepts of environment and variables shared with the environment are defined that allows to disengage from specific input/output ports. Types of annotations that specify restrictions on the values of the variables at program launch, restrictions on the environment (in particular, on the control object), invariants of the control cycle, pre- and postconditions of external functions used in Reflex programs are defined. Annotated Reflex also uses standard annotations assume, assert and havoc. The operational semantics of the annotated Reflex programs uses the global clock as well as the local clocks of separate processes, the time of which is measured in the number of iterations of the control cycle, to simulate time constraints on the execution of processes at certain states. It stores a complete history of changes of the values of shared variables for a more precise description of the time properties of the program and its environment. Semantics takes into account the infinity of the program execution cycle, the logic of process transition management from state to state and the interaction of processes with each other and with the environment. Extending the formal operational semantics of the Reflex language to annotations simplifies the proof of the correctness of the transformation approach to deductive verification of Reflex programs developed by the authors, transforming an annotated Reflex program to an annotated program in a very limited subset of the C language, by reducing a complex proof of preserving the truth of program requirements during the transformation to a simpler proof of equivalence of the original and the resulting annotated programs with respect to their operational semantics.

A collaborative augmented reality annotation tool for the inspection of prefabricated buildings

The inspection of prefabricated buildings involves different stages and tasks such as the collection of measurements, the visual inspection of components and the written annotation of defects. Traditionally, inspectors have documented the process, the kind of defects and the proposed correction measures in paper format, hindering the collaboration with other experts (either simultaneously or asynchronously) and the collection of other types of annotations (e.g. images, 3D elements). In this paper, we present an AR tool designed to aid inspectors during this process. The tool has many benefits, as it allows simultaneously performing a collaborative inspection, taking multitype and geolocated annotations, their monitoring and edition, and performing in situ augmented visualizations. The quantitative and qualitative user evaluation carried out with our tool in a real environment (including usability and satisfaction evaluations) shows the relevance that such a technology might bring to the field and prove that our tool is usable and fulfils most of the inspectors’ expectations.

Text mining brain imaging reports

BackgroundWith the improvements to text mining technology and the availability of large unstructured Electronic Healthcare Records (EHR) datasets, it is now possible to extract structured information from raw text contained within EHR at reasonably high accuracy. We describe a text mining system for classifying radiologists’ reports of CT and MRI brain scans, assigning labels indicating occurrence and type of stroke, as well as other observations. Our system, the Edinburgh Information Extraction for Radiology reports (EdIE-R) system, which we describe here, was developed and tested on a collection of radiology reports.The work reported in this paper is based on 1168 radiology reports from the Edinburgh Stroke Study (ESS), a hospital-based register of stroke and transient ischaemic attack patients. We manually created annotations for this data in parallel with developing the rule-based EdIE-R system to identify phenotype information related to stroke in radiology reports. This process was iterative and domain expert feedback was considered at each iteration to adapt and tune the EdIE-R text mining system which identifies entities, negation and relations between entities in each report and determines report-level labels (phenotypes).ResultsThe inter-annotator agreement (IAA) for all types of annotations is high at 96.96 for entities, 96.46 for negation, 95.84 for relations and 94.02 for labels. The equivalent system scores on the blind test set are equally high at 95.49 for entities, 94.41 for negation, 98.27 for relations and 96.39 for labels for the first annotator and 96.86, 96.01, 96.53 and 92.61, respectively for the second annotator.ConclusionAutomated reading of such EHR data at such high levels of accuracies opens up avenues for population health monitoring and audit, and can provide a resource for epidemiological studies. We are in the process of validating EdIE-R in separate larger cohorts in NHS England and Scotland. The manually annotated ESS corpus will be available for research purposes on application.

Semantic Tag Categories in Corpus Linguistics: Experience and Examination

Two types of semantic annotation: facet and pointer are discussed in the article;where it is noted that if the facet method requires consecutive interpretation of a unit, the genealogy method requires the group, group, and field to be defined. The first method is from word to word, second method to meaning. This shows the application of the word -> meaning, meaning -> word principle in both the expert's process of searching and the interface that is reflected in the interface. Priority of such features as lexical-grammatical, lexical-thematic, methodological assessment characteristic, derivative characterization as a result of observation of existing online corps; The constants and operator-classifier tags are also defined as permanent labels are the main points of the research.

A web-based collaborative reading annotation system with gamification mechanisms to improve reading performance

This study presents a web-based collaborative reading annotation system (WCRAS) with gamification mechanisms to motivate students' annotations behaviors and promote students' reading comprehension performance. The research participants were 55 fifth grade students from two classes of an elementary school in northeastern Taiwan. A quasi-experimental design was adopted to evaluate the effects of the experimental and control groups respectively using WCRAS with and without gamification mechanisms to support digital reading on students' annotation behaviors, collaborative interaction relationship, reading comprehension performance, and immersion experience. The results showed that the experimental group made significantly more annotations across almost all types of reading annotations and response annotations and had a significantly higher degree of immersive experience and social interaction than the control group. However, the difference in reading comprehension performance between the two groups has not been found. In addition, although the experimental group provided more amounts of high-quality annotations, the group did not generate more quantity of annotations related to reading comprehension aspects assessed by PIRLS. These research findings indicate that a gap between the promotion of the quantity and quality of participation and learning outcomes exists. This study is significant to address the need that designs the gamification mechanisms to encourage high-quality annotations that can effectively enhance students’ reading comprehension. To better associate game features and learning performance in the WCRAS with gamification mechanisms, future studies are suggested to promote both annotation quality and connection between reading achievement and gamification mechanisms.