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set as bio at bottom:[Insert “Resources” text box at bottom of second page. 2-column box, per template. This will be a standard insertion.]As the production of many global goods has shifted to Chin...

Develop and pilot test a simulator that presents ten commonly encountered representative clinical contexts for trainees to learn basic electroencephalogram (EEG) interpretation skills. We created an interactive web-based training simulator that allows self-paced, asynchronous learning and assessment of basic EEG interpretation skills. The simulator uses the information retrieval process via a free-response text box to enhance learning. Ten scenarios were created that present dynamic (scrolling) EEG tracings resembling the clinical setting, followed by questions with free-text answers. The answer was checked against an accepted word/phrase list. The simulator has been used by 76 trainees in total. We report pilot study results from the University of Florida’s neurology residents (N = 24). Total percent correct for each scenario and average percent correct for all scenarios were calculated and correlated with most recent In-training Examination (ITE) and United States Medical License Examination (USMLE) scores. Neurology residents’ mean percent correct scenario scores ranged from 27.1–86.0% with an average scenario score of 61.2% ± 7.7. We showed a moderately strong correlation r = 0.49 between the ITE and the average scenario score. We developed an online interactive EEG interpretation simulator to review basic EEG content and assess interpretation skills using an active retrieval approach. The pilot study showed a moderately strong correlation r = 0.49 between the ITE and the average scenario score. Since the ITE is a measure of clinical practice, this is evidence that the simulator can provide self-directed instruction and shows promise as a tool for assessment of EEG knowledge.

In today’s world, Modern organizations deal with terabytes of text, such as email, that often plays a significant role in their day to day operations. The user has to face a task of identifying useful information from these data which is difficult and it requires some amount of time. One possible means is to use text summarization. Text summarization is the process of identifying the most valued/meaningful information in a document and compressing that information into a shorter version preserving its overall meaning. Sentiment analysis is about determining the text given by the user whether it is Positive, Negative or Neutral. We used Gensim Algorithm for generating text summary. This algorithm automatically summarizes the given text, by extracting one or more important sentences from the text. This project is about text summarization which includes sentiment analysis. In UI, a text box will be displayed, which is used to take the input text from the user which need to be summarize. Then it will pre-process the text and show the summarized content. It will be taking input as URL of an article and it is going to provide Title, Author, Publication Date, Sentimental analysis, Keywords, URL of an Article.

Objective The study sought to describe patient-entered supplemental information on symptomatic adverse events (AEs) in cancer clinical research reported via a National Cancer Institute software system and examine the feasibility of mapping these entries to established terminologies. Materials and Methods Patients in 3 multicenter trials electronically completed surveys during cancer treatment. Each survey included a prespecified subset of items from the National Cancer Institute’s Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). Upon completion of the survey items, patients could add supplemental symptomatic AE information in a free text box. As patients typed into the box, structured dropdown terms could be selected from the PRO-CTCAE item library or Medical Dictionary for Regulatory Activities (MedDRA), or patients could type unstructured free text for submission. Results Data were pooled from 1760 participants (48% women; 78% White) who completed 8892 surveys, of which 2387 (26.8%) included supplemental symptomatic AE information. Overall, 1024 (58%) patients entered supplemental information at least once, with an average of 2.3 per patient per study. This encompassed 1474 of 8892 (16.6%) dropdowns and 913 of 8892 (10.3%) unstructured free text entries. One-third of the unstructured free text entries (32%) could be mapped post hoc to a PRO-CTCAE term and 68% to a MedDRA term. Discussion Participants frequently added supplemental information beyond study-specific survey items. Almost half selected a structured dropdown term, although many opted to submit unstructured free text entries. Most free text entries could be mapped post hoc to PRO-CTCAE or MedDRA terms, suggesting opportunities to enhance the system to perform real-time mapping for AE reporting. Conclusions Patient reporting of symptomatic AEs using a text box functionality with mapping to existing terminologies is both feasible and informative.

With modern rapid and emerging development trend of Internet technology, great number of data are accumulated in the society, which leads to a larger data scale. With the target to discover the potential value of general data, it has been usually necessary to flexibly apply various data mining algorithms based on the actual situation and scenarios. The implementation of data mining algorithm is faced with great challenges in terms of execution efficiency, algorithm parallelization and platform usability. Different from horizontal text, multi-directional text and curved text, the surface text of surface object has some geometric features. In the detection phase, the text box label of surface text needs more vertex coordinates to express its spatial information, which makes it difficult to label large-scale samples. In the recognition stage, it is more difficult to recognize the surface text of the surface object than the regular text on the plane. At present, several universities ignore the importance to the construction of political education micro platform, and fail to develop a scientific micro platform according to the actual needs and interests of students. Based on this situation, this paper applies the data mining models and text recognition algorithm to study innovative smart political education platform.

Nowadays, there is a great challenge to make information technology transparent so the end-user can have a good user experience. We are proposing a Natural Language Interface Model in which the user introduces the phrase through a text box in the local natural language, preferably in the Portuguese language. The system will read these words and separate them into a list of tokens, which will be analyzed, and if unnecessary words are found, they will be removed. The system will make a combination of the user’s request. In the end, it will arrive until the generation of SQL Query and basics MongoDB Query as well ready to be executed directly in the Database. The model has an average accuracy of 70.27%. A morphological analysis will understand the meaning of the user’s sentence ending in a friendly interface.

© Copyright © 2020 Armstrong, Bridge, Oakden, Reynolds, Wang, Panzone, Rivera, Kause, Ffoulkes, Krawczyk, Miller and Serjeant. There is a food knowledge disconnect between the food research community, and the general population. Food experts know detailed information about foods, but there is a lack of understanding about what citizens know. Citizen science has been used successfully in various fields however, is virtually absent from food research. This pilot study explores the efficacy of Zooniverse citizen science platform as a method of food citizen research, and assesses the impact of response method, food type, portion size and weight information, on estimates of carbon footprint and energy content. Estimates of the carbon footprint (grams of carbon dioxide equivalent) and energy content (Kcal) of 10 foods were measured in a 3 (Between: Response Method; multiple-choice, slider, text box) × 3 (Within: portion size; small, medium, large) × 2 (Between: weight information; present, absent) observational survey. Citizens (N = ~516, unique IP addresses) were recruited via informal email networks. Kruskal–Wallis and Chi-square analyses compared citizen estimations with validated values, and assessed the impact of the variables on estimations. The majority of carbon footprint and energy content estimates were inaccurate, with citizens typically overestimating values. We observe an unexpected correlation between carbon footprint and energy content estimates. Portion size impacts perceptions, with estimations increasing with larger portion sizes. Weight information impacts perceptions, with estimations of carbon footprint being lower, and estimates of energy content being higher when weight information is present. Input method significantly affects observed values, estimates of carbon footprint and energy content estimates were lowest using the text box, followed by the slider tool, highest estimates were given using multiple choice. Citizens are unable to accurately estimate the carbon footprint and energy content of foods, though citizens may possess understanding of the hierarchy of values. These compelling findings highlight the need for consumer education, with a focus on carbon footprint, to precede interventions intended to move consumers toward more sustainable and healthy diets. We have demonstrated that citizen science can be used to measure food carbon footprint and energy content perceptions, and the slider tool offers greater sensitivity of estimation and functionality than other input methods. This work was funded by STFC Food Network+ pilot funding (ST/P003079/1), and STFC twenty-first century challenge funding (ST/T001410/1) Piloting Zooniverse for food, health, and sustainability citizen science. CR was supported from the HEFCE Catalyst-funded N8 AgriFood Resilience Programme and matched funding from the N8 group of Universities. Additional funding was provided by Research England via the project Food based citizen science in UK as a policy tool.

The purpose of this review was to summarize the algorithms and methodologies of text-mining and demonstrate the main objective of text-mining on radiology reports in health care facilities which consider as a common source of medical information. And how it affects radiologist performance and plays a big role in clinical practice workflow and decisions making in critical situations and time-consuming. In case the radiologist widely used a narrative- text box in their reporting and sometimes there is a big need to know very specific and critical information about the patients’ current status and to provide with accurate diagnosis then take the appropriate action as soon as possible. However, here it becomes the need to utilize information technology and the effort was directed to find ways to merge data science with the health care field to solve such a problem. We follow the systematic review methodology conducted by Ahmad Alaiad .et al study completed after 29 quantitative and systematic related articles were searched using relevant database then extract and discuss the text-mining processes and provide overall picture about such new innovation and how the IT now days play a valuable role in health problem solving and make the clinical practice more effective and efficient and improve quality of care by improving clinical decision making process. We develop a research taxonomy that summarizes the most of algorithms and methodologies of existing research, we identify the major future questions, limitations and gaps.

This study aims to determine the initial competency description of the functional training participants of the young teacher class XIII 2018. The research method used is descriptive method with a research instrument in the form of a closed questionnaire given to all training participants totaling 30 people, taking place on 9 October 2018. The distribution of questionnaires was conducted at the beginning of learning because it aims to find out the initial competency of the training participants before learning begins. The questions asked in this closed questionnaire are questions that must be answered by the training participants with answers "YES" or "NO". This closed questionnaire was distributed to the class group of training participants using Google form. From 30 training participants who sent back the questionnaire to 25 people, this number was the basis for subsequent data processing. The data obtained are processed simply in the form of a percentage for each aspect of the question. The results showed that the initial competency of the 2018 Young Teachers Functional Training participants for the ten aspects for the ten aspects had the lowest average "YES" for the aspect of navigation in PowerPoint (27.00%), which was more detailed for question number 40 namely about skills in using menus and navigation (76%), following the next two that have not been mastered namely the skills to use hyperlinks (72%) and presentations with slide masters (72%). Whereas for the aspect that has the highest average percentage of "YES" is knowing PowerPoint (85.32%), the next downturn is working with text in PowerPoint {more in detail to question number 13 about writing text in the text box (84%)}, and techniques for preparing presentations {in more detail to question number 12 about reducing existing slides (80%)}. The data obtained can be used as material to organize strategies in learning. Keywords: Pre Competences, Training, Multimedia

There are many times when it is necessary to establish a connection to a database and execute SQL commands against that database from the code behind of a page. For example, we may require the end user to click a Display button to display the contact information of his or her organization’s customers in a non-data-bound control like a multiline text box. Or we may require the end user to enter his or her login credentials and click a Login button to gain access to their application’s functionality. In both of these scenarios, we would need a page that interacts with a database from the button Click event handler method in the page’s code behind.

[Insert “Resources” text box at bottom of second page. 2-column box, per template. This will be a standard insertion.]The theme of IRI’s 2018 Annual Conference, held in May in Atlanta, was Breaking...

Consumer payments elicited on slider scales can be systematically different from those elicited through text boxes because of the end point assimilation effect. When people use text boxes to make payments, they evaluate monetary values relative to the starting point of the response range. In contrast, when people use slider scales, they evaluate monetary values relative to the starting point as well as the end point of the response range. Consequently, payments elicited on slider scales tend to be assimilated toward the end point of the response range. This slider scale end point assimilation effect varies for ascending and descending payment formats. For ascending payment formats (e.g., eBay bids), slider scales elicit higher payments than text boxes. But for descending payment formats (e.g., Priceline bids), slider scales elicit lower payments than text boxes. This research not only documents how slider scales alter consumer payments, but also explains how the mental number line affects financial decisions.

In this article, we propose a new approach to segmentation-free word spotting that is based on the combination of three different contributions. Firstly, inspired by the success of bounding box proposal algorithms in object recognition, we propose a scheme to generate a set of word-independent text box proposals. For that, we generate a set of atomic bounding boxes based on simple connected component analysis that are combined using a set of spatial constraints in order to generate the final set of text box proposals. Secondly, an attribute representation based on the Pyramidal Histogram of Characters (PHOC) is encoded in an integral image and used to efficiently evaluate text box proposals for retrieval. Thirdly, we also propose an indexing scheme for fast retrieval based on character n-grams. For the generation of the index a similar attribute space based on a Pyramidal Histogram of Character N-grams (PHON) is used. All attribute models are learned using linear SVMs over the Fisher Vector representation of the word images along with the PHOC or PHON labels of the corresponding words. We show the performance of the proposed approach in both tasks of query-by-string and query-by-example in standard single- and multi-writer data sets, reporting state-of-the-art results.

This research is to design a curriculum for the students who will be dealing with the image processing techniques, and at the same time, network security has been considered as well. The goal of this curriculum is to prepare the students attending higher education systems or the novice network engineers for a fundamental knowledge in network systems and required skills. As such a basic design has been common in other curriculums, this study will also add another critical issue to its contents; the problems associated with network security which is becoming more serious every year. The curriculum consists of 6-hour courses during which students can learn about some of the basic network configurations while experiencing typical operating processes where Java language skill is required. The introductory-level graphic processing techniques are provided in the curriculum through several case studies and some specific network attacking methods and their outcomes will be introduced and discussed. In each course, the communication protocols, available services and problem solving methods are described and explained and the students' level of understanding and final achievements will be measured based on the assignments given to them. For instance, in imaging process, the students are to classify applied protocols and services on the basis of individual definitions and port-numbers assigned, followed by image capturing and transferring of captured image to the text box for an image printing task. Meanwhile, the network security courses involve analyses of packet traffics which will allow the students to locate the host or origin of malicious attacks exploiting malignant packets to sever communications or shut down entire systems. In the future extended study the author will extend the courses to provide in-depth knowledges while transforming the curriculum to make it more adaptable to non-IT or non-science majors.

Try to imagine that a figure, a table or an explanatory box in your main manuscript gets cited, in addition to citations to the main paper. Some scientists would no doubt be ecstatic at this unrealistic opportunity of gathering additional citations. This paper highlights a case in which a text box (Unger and Couzin in Science 312(5770):40---41, 2006. doi:10.1126/science.312.5770.40) within a larger paper (Couzin and Unger in Science 312(5770):38---43, 2006. doi:10.1126/science.312.5770.38), as well as the paper itself, are both cited, 33 and 8 times, respectively, according to Clarivate Analytics' (formerly Thomson Reuters) Web of Science. Both papers were published in AAAS' Science. This paper explores details of these citations and shows how four papers between 2007 and 2015 have cited both papers, including the text box. The argument is put forward that citation of least divisible units of a paper, in this case, a text box, are unfair citation practices, and since they refer to the citation of a part of the same paper, the term "nested self-citation" has been coined. Given the attention given in recent times to citation manipulation, citation rings and inappropriate citations, the risks of nested self-citations, including the skewing of citation counts, and of not correcting potentially misleading information, need to be explored.

To date, offline handwriting paragraph recognition systems either separately crop text line images and recognize them or perform implicit line segmentation by integrating complicated multi-dimensional long short-term memory (MDLSTM) with an attention mechanism. The former abovementioned approachs could lead to sub-optimal performances while the latter is very time-consuming. In this paper, a fast end-to-end system, called adversarial feature enhancing network (AFEN), is proposed for offline handwritten paragraph recognition. The proposed AFEN system comprises five components: a shared feature extractor for robust feature learning, a text detection branch for text box proposal; RoIRotate for oriented feature region extraction, an adversarial feature learning network for joint feature learning of text detection and recognition branch, and a text recognition branch for text transcription. Experiments on two popular handwritten paragraph recognition benchmarks, namely IAM and Rimes are used to verify the efficacy of the proposed AFEN system. The proposed approach yields impressive results compared to previously proposed systems in the literature.

Scene text detection methods based on direct regression are always efficient and accuracy. However, the previous methods also have obvious shortcomings. The central text pixels always constrained the text box in the process of direct regression, which made the predicted results inaccurate. To solve this problem, we propose a novel Location Sensitive Regression algorithm for multi-oriented scene Text Detection (LSRTD), which assigns different weights to text pixels according to their distance to text box vertexes. At the same time, we apply Focal Loss to pixel-wise text/non-text classification task, the number of false positive cases in results is decreased because of Focal Loss balances the loss weights of easy and hard samples during training. Our method LSRTD is validated on benchmark datasets. The F-measure of ICDAR2015 is 0.84 on single-scale testing.

Table detection and extraction has been studied in the context of documents like reports, where tables are clearly outlined and stand out from the document structure visually. We study this topic in a rather more challenging domain of layout-heavy business documents, particularly invoices. Invoices present the novel challenges of tables being often without outlines - either in the form of borders or surrounding text flow - with ragged columns and widely varying data content. We will also show, that we can extract specific information from structurally different tables or table-like structures with one model. We present a comprehensive representation of a page using graph over word boxes, positional embeddings, trainable textual features and rephrase the table detection as a text box labeling problem. We will work on our newly presented dataset of pro forma invoices, invoices and debit note documents using this representation and propose multiple baselines to solve this labeling problem. We then propose a novel neural network model that achieves strong, practical results on the presented dataset and analyze the model performance and effects of graph convolutions and self-attention in detail., Changed from previous version based on icdar2019 feedback to include 6 pages, 2 figures. Slightly changed paper name and abstract to be less misleading. Corrected grammar and shortened content heavily, corrected misleading information and readability. Currently in review for icdar2019-wml subconference/workshop

Automatic license plate recognition (ALPR) is a process of identification of the license plate from an image taken at any signal stop or parking station. The Identified information or the number plate can be used for keeping a check on signal stops or for monetary settlements at parking lots or toll stations. It can also be used around the world for law and administrative works like checking a vehicle if it is registered or not. It also finds its use in safety supervision system. The ALPR can be used upon various types of pictures like color images, black and white images and etc. ALPR may fail a number of times if the quality of the picture is not up to the mark. So, ALPR depend a lot in the image quality, and hence the challenges in its real-life application. It may be used both indoors and outdoors but it must be able to process the images both quickly and successfully in all sorts of environments, be it daytime or night time, sunny or rainy. Our proposed method mainly has three modules: 1) Detection of license plate 2) Segmentation of Characters 3) Text Box Generation. The number plates sometimes may be unclear for the cameras. It may sometimes have dirt and scratches on it and thus increasing the complexities of the ALPR system. Today nationwide ALPR has been adopted by several law and governmental agencies for its efficiency and usefulness. Public safety agencies are using the ALPR system to gather and store relevant information about people and to keep a check on the vehicular activities. It is becoming a significant tool for the Law Enforcement Agencies.

The paper aims at designing of two stage cascaded ultra-wideband (UWB) low noise amplifier (LNA) by using negative image amplifier technique. The objective of this article is to show the performance improvement using negative image amplifier technique and realization of negative valued lumped elements into microstrip line geometry. The innovative technique to realize the negative lumped elements are carried out by using Richard’s Transformation and transmission line calculation. The AWR microwave office tool is used to obtain characteristics of UWB LNA design with hybrid microwave integrated circuit (HMIC) technology. The 2-stage cascaded LNA design using negative image amplifier technique achieves average gain of 23dB gain and low noise figure of less than 2dB with return loss less than - 8dB for UWB 3-10GHz. The Proper bias circuit is extracted using DC characteristics of transistor at biasing point 2V, 20mA and discussed in detail with LNA layout. The negative image matching technique is applied for both input and output matching network. This work will be useful for all low power UWB wireless receiver applications.

One of the most common, versatile, and useful ways to implement Dynamic SQL is when performing complex searches. Consider your favorite web sites and the search functionality provided in each. For some, the search may be simple: You go to the single text box in the top-right corner, enter some text, and get your results. Other searches—such as searching for a hotel, flight, or car rental—can involve dozens (or more) of optional parameters. Dynamic SQL can allow you to pare down your search queries in order to only process what is needed. In addition, you can greatly customize the search, as well as the data returned. You can even analyze the input to determine the correct course of action, based on its structure.

The objective of this paper is to gain more evidence regarding how the design of the rating scales and open-ended questions influence data quality in Web surveys of MOOC (Massive Open Online Course) students. We present the results of four full-factorial randomized experiments that investigate the impact of the following factors: 1) order of response options; 2) user interface for rating questions 3) layout of question's options; and 4) size of answer boxes in open-ended questions. We found that responses to scalar questions with ascending (from negative to positive) or descending (from positive to negative) order of response options do not differ substantially. The use of the radio button format allows a reduction in the percentage of respondents who choose the "Don't know" option and makes responding to questions less challenging in comparison with slider and text box interfaces. There are no significant differences in the answers of respondents who completed questionnaires with a vertical or horizontal orientation of the questions' options. In addition, respondents who answer the questions with larger answer boxes are more likely to write longer comments. The order of options does not affect responses to matrix table questions.Employing a radio button format allows reducing the number of "Don't know" answers.Larger text boxes tend to result in longer answers in a native language survey.

The use of contexts --side information associated to information tasks-- has been one of the most important dimensions for the improvement of Information Retrieval tasks, helping to clarify the information needs of the users which usually start from a few keywords in a text box. Particularly, the social context has been leveraged in search and personalization since the inception of the Social Web, but even today we find new scenarios of information filtering, search, recommendation and personalization where the use of social signals can produce a steep improvement. In addition, the action of searching has become a social process on the Web, making traditional assumptions of relevance obsolete and requiring new paradigms for matching the most useful resources that solve information needs. This escenario has motivated us for organizing the Social Personalization and Search (SPS) workshop, a forum aimed at sharing and discussing research that leverage social data for improving classic personalization models for information access and to revisiting search from individual phenomena to a collaborative process.

Eighty-five years ago, the journal Science published "The Romance of the Next Decimal Place" by F.K. Richtmyer. The essay cites specific historical examples to show how improved measurement accuracy (the next decimal place) has driven scientific discovery in many areas, and predicts that this will continue at an increasing pace. As his title suggests, improving measurement accuracy can be an exciting quest. Richtmyer also suggested that science progresses in a two-step process quoted in "A Two-Step Progression of Science" (Text Box 1).

Web image character recognition has important application value in the commercial field and text detection is the basis of extracting this text information. Among many text detection algorithms, EAST (efficient and accurate scene text detector) algorithm has a good performance in natural scenes, but it is ordinary in web pictures. Based on this, aiming at the problem that the number of channels in the QUAD output layer of EAST algorithm is redundant, which leads to the low accuracy of web image text detection, it is modified as the distance from the point marked as a positive sample to the four vertices of the text box where it is located instead of the coordinate offset to the vertices. The improvement reduces from the original 8 channels to 4 channels, which restricts the optimization direction of the model, and then by setting a new loss function in order to improve the loss value of the edge area of the text box and the pixels in the image that are difficult to detect, the model is more suitable for the text detection of the web image. The experimental results show that the improved EAST algorithm significantly improves the accuracy of text detection in web images.

Cortana is one of the new features introduced by Microsoft in its latest version of desktop operating systems, i.e., Windows 10. The feature is identified by “Ask me anything” text box at the Start Menu and can be used for a number of tasks such as setting up reminders based on time, place, and person; searching stuff on local device or web; sending emails and texts; and more. The feature keeps track of reminders when and where they got finalized, as a result, evidentiary artifacts related to reminders are recorded in a back-end database. The forensic examination of Cortana has been largely unexplored in literature as the platform is relatively new. This paper seeks to determine the databases created by Cortana, their format, and the type of information recorded in these databases. As a part of this paper, six custom Python scripts have been developed for decoding and exporting data to aid forensic investigators. Furthermore, several experiments are conducted to extract information related to reminders such as created and last updated timestamps of a reminder, type of reminder, when a reminder got finalized, and where it got finalized. Finally, forensic usefulness of information stored in a Cortana database is demonstrated in terms of a location timeline constructed over a period of time.

Text detection in natural scene images is an important and popular task in the computer vision community. Due to slanted characters and blurred images in natural environments, it is a challenging task under active research. In this paper, we propose a Focal Text Detection Network (FTDN), which could be trained well without abundant data. FTDN is able to segment text region and simultaneously regress text box at pixel-level. Specifically, combined with focal loss, our method can balance positive/negative and easy/hard samples to achieve better performance. Compared with previous methods, FTDN achieves better performance in terms of text detection accuracy in natural scene. It outperforms the state-of-the-art methods on the standard ICDAR 2015 dataset with 80.9% F-measure.

In this paper, we propose to reconsider the myth of the paperless office and we explore a new user experience, the augmented document, in order to digitize a document, extract information (like scientific network) in order to find similar content. This framework exploits image processing tools to segment the document and facilitate the manipulation of its structure. Then, OCR is performed to enable textual edition: copy/paste from other sources, correct mistakes, change text box shapes. Moreover, in order to help the user enriching the document, the system is able to propose similar content (like papers from the same researcher, documents from the same topic …). This all-in-one framework was tested on many different devices like interactive table, HP Sprout, or Microsoft Surface, and all the actions can be performed with basic gestures without requiring technical expertise.

The caption of Fig. 6 and the main text contained an error in the chemical formula of the “(MozW1−z)0.85Ta0.10(TiZr)0.05” alloy; it has now been corrected to “(Mo1−zWz)0.85Ta0.10(TiZr)0.05”. Figure 5 also contained an erroneous text box, which has now been removed. This has now been corrected in both the PDF and HTML version of this article.

Previous deep learning based state-of-the-art scene text detection methods can be roughly classified into two categories. The first category treats scene text as a type of general objects and follows general object detection paradigm to localize scene text by regressing the text box locations, but troubled by the arbitrary-orientation and large aspect ratios of scene text. The second one segments text regions directly, but mostly needs complex post processing. In this paper, we present a method that combines the ideas of the two types of methods while avoiding their shortcomings. We propose to detect scene text by localizing corner points of text bounding boxes and segmenting text regions in relative positions. In inference stage, candidate boxes are generated by sampling and grouping corner points, which are further scored by segmentation maps and suppressed by NMS. Compared with previous methods, our method can handle long oriented text naturally and doesn't need complex post processing. The experiments on ICDAR2013, ICDAR2015, MSRA-TD500, MLT and COCO-Text demonstrate that the proposed algorithm achieves better or comparable results in both accuracy and efficiency. Based on VGG16, it achieves an F-measure of 84.3% on ICDAR2015 and 81.5% on MSRA-TD500., Comment: To appear in CVPR2018

The use of voice-activated virtual assistants (VAs) to execute tasks, request information, or entertain oneself on the smartphone is on the rise. However, insufficient feedback on the states of VAs may impair the interaction flow. We propose to use nonverbal emotional expressions to indicate a VA's conversational states and promote user engagement. We introduce three emotional expression designs of VA, iconic facial expressions, a text box with body movements, and emotional voice waveforms. Our user study results show that a VA with an expressive face or text body movements can evoke stronger user engagement than the one with voice waveforms.

This informal essay attempts to summarise Pat Werhane’s performance at the conference celebrating her work and offer some insights into the light-handed carefulness that characterizes the continuing project of her work – her practice - in the field of Business Ethics. Appreciating the complex scope of the work to which this volume is a tribute, Pat presented her summary in front of a contrastingly simple organizational slide. At the center of the slide there is a text box containing the words “the linguistic turn”. Radiating from this idea are a dozen or so distinct areas of Business Ethics to which Pat has contributed, including her ongoing areas of interest. At the foot of the slide was an animated graphic of an endless pile of turtles, one on top of the next. In this essay I shall explore some possible origins and significances for this characteristically modest scheme. How might this linguistic turn, that Pat has identified for us as a unifying thread, arise? What may we understand of its significance among her otherwise eclectic interests?

The web provides a rich, open-domain environment with textual, structural, and spatial properties. We propose a new task for grounding language in this environment: given a natural language command (e.g., "click on the second article"), choose the correct element on the web page (e.g., a hyperlink or text box). We collected a dataset of over 50,000 commands that capture various phenomena such as functional references (e.g. "find who made this site"), relational reasoning (e.g. "article by john"), and visual reasoning (e.g. "top-most article"). We also implemented and analyzed three baseline models that capture different phenomena present in the dataset., Comment: EMNLP 2018

This article compares two methods of employing novice Web workers to author descriptions of science, technology, engineering, and mathematics images to make them accessible to individuals with visual and print-reading disabilities. The goal is to identify methods of creating image descriptions that are inexpensive, effective, and follow established accessibility guidelines. The first method explicitly presented the guidelines to the worker, then the worker constructed the image description in an empty text box and table. The second method queried the worker for image information and then used responses to construct a template-based description according to established guidelines. The descriptions generated through queried image description (QID) were more likely to include information on the image category, title, caption, and units. They were also more similar to one another, based on Jaccard distances of q-grams, indicating that their word usage and structure were more standardized. Last, the workers preferred describing images using QID and found the task easier. Therefore, explicit instruction on image-description guidelines is not sufficient to produce quality image descriptions when using novice Web workers. Instead, it is better to provide information about images, then generate descriptions from responses using templates.

Personalized Web Image search is the one searching for the particular images of User intention on the Web. For searching images, a user might provide query terms like keyword, image file, or click on few image file, and therefore the system can determine the images similar to the query. The similarity used for search criteria could be Meta tags, color distribution in images, region/shapes attributes, etc. Web-scale image search engines namely Google and Bing searches for images are relying on the surrounding text features. It is highly cumbersome and complicated for the web-scale based image search engines to interpret users search intention only by querying of keywords. This leads to the incorporation of noise and high ambiguity in the search results which are extremely unfit in the context of the users. It's also a necessary mandate for using visual information for solving the problem of ambiguity in the text-based image retrieval scenario. In the case of Google search, search text box will auto complete while user is typing similar added keywords. This method will differ from user intention while searching. So to avoid this kind of faults, it is important to use visual information in order to solve the uncertainty in text-based image retrieval. To retrieve exact matching, and acquire user‟s intention we can allow them text query with extended or related images as a suggestion. We have proposed an innovative Web image search approach. It only needs the user to click on one query image with minimal effort and images from a pool fetched by text-based search are re-ranked based on both visual and textual contents.

The importance of formative feedback in higher education institutions is out of doubt, however, reports on poor feedback from students and a high cost in producing quality feedback from teachers demand a different line of action. This article proposes the use of Open Education Resources as suggested content for markers in order to enhance their provided feedback. An evaluation was performed using a computer supported marking tool which connect the mark activity with the OER to encourage the marker to produce formative feedback while she is marking exams. Control and experimental groups of markers were arranged, with the former using only a text box to include commentaries as feedback and the latter the OER connected search tool. Both groups marked the same set of exams after a training session on quality feedback. Results showed that the use of OER in the interface to provide feedback made markers generate more and longer feedback than those in the control group.

Create a system that allows you to queue messages and show them one at a time. Draw the messages as text, above a rectangle that only shows when a message does.

Imagine a life without search engines. Every time you need an answer, you automatically go to sites like Google and Bing. Over last decade or so, search has come a long way. Gone are the days when search results were restricted to picking data from a database. With the increasing amount of data getting generated every second and new algorithms getting invented, search has moved beyond the text box. Search has moved from the era of postback to partial postbacks to even now auto-suggest. Search has also moved from just giving textual results and links to images and now videos, news, and contextual search. Search has also moved from reactive to proactive. Search has also moved from generalized search to a personalized experience. Google has certainly provided a new dimension to the entire search engine. From knowing the weather for the coming weekend to a cricket score to the latest news, videos, or anything else you want to know, your first stop has been Google for the last decade or so.

Computer agents can significantly impact the manner in which people perceive information and make choices. This study examined the effectiveness of an anthropomorphized computer agent (a Polar Bear) versus a simple text box at two levels of reliability on participants’ receptiveness to information about climate change and subsequent environmentally friendly behavior. Participants ( N=110) answered questions about climate change while receiving the assistance of one of four computer agents comprising either an anthropomorphic agent (Polar Bear) or text box at 90% or 60% reliability. Results revealed that dependence on the anthropomorphic Polar Bear agent did not significantly differ from dependence on information conveyed via the Text Box agent; however, accuracy on the environmental quiz was greater for participants assisted by the Polar Bear agent under 90% reliability but lower than those assisted by the Text Box under 60% reliability. This study demonstrates that in some contexts the decision aiding advantages of anthropomorphic characters are selective and may be dependent largely on the agent’s demonstrated reliability at helping users reach their end goal.

This article examines the impacts of capturing correspondence metadata through an exhaustive discussion of how details such as the sender and recipient of a letter, their respective addresses, and the date of writing can be entered in an intuitive and accurate fashion. The focus is on the development of fundamental input mechanisms, which can be reused for specific metadata items. Our discussion results in the implementation of a proof-of-concept application, which shows that only a single text box is needed for each metadata item, without violating any of the self-imposed goals and requirements.

In the current study, we assessed whether undergraduate electronic gaming machine (EGM) gamblers would be more apt to set a time limit and spend less time gambling when asked to consider setting an explicit time limit prior to their gambling session. To this end, participants (N = 43) were randomly assigned to a time limit pop-up condition or control condition, both of which involved gambling on an EGM in a virtual reality (VR) casino. In the time limit pop-up condition, participants were asked (via pop-up message) to consider setting a time limit on play and entering that limit in an available text box prior to commencing play. In the no time limit pop-up condition, participants engaged in play immediately upon accessing the EGM in the VR casino (i.e. they were not exposed to a time limit pop-up message). As predicted, participants who were explicitly asked to consider setting a time limit on their EGM play were significantly more likely to do so and spent less time gambling than those who were not give...

This study examined the effect of the presence of an artificial individual in a purchase environment on purchase intention for products with fair-trade labels among Japanese consumers. By manipulating the presence of an artificial individual, we assessed consumers' intentions to purchase fair-trade products under two different experimental unmanned purchase environments: the agent condition, in which task instructions were given by a female clerk-like computer graphic agent throughout the task (N=118), and the control condition in which task instructions were given through a text box (N=106). Results demonstrated that participants under the agent condition valuated fair trade higher than those under the control condition, although participants in both the agent and the control conditions were instructed that their responses would remain anonymous. These findings suggest that the implication of the presence of an artificial individual, such as a clerk-like agent in an unmanned purchase environment, enhances the ethicality of purchase intentions as with manned purchase environments.

The debating activities of the public sphere stimulated the development of pre-modern Europe. So far, such activities had failed to do same for Nigeria – a country in dire need of development. This paper investigates why public discourse in Nigeria has so far failed to develop Nigeria. To do the investigation, I purposively selected eight textual exemplars from stories which three Nigerian newspapers wrote when they covered the 2005 National Political Reform Conference in Nigeria. I presented the selected textual exemplars on a text box and then subjected the texts to interpretation using the method of critical discourse analysis. When I triangulated my interpretation with comments I got when I interviewed the journalists behind the interpreted texts, I found reasons why the mass media, as presently organized, cannot constitute an appropriate arena for development-boosting public discourse in Nigeria. On this light, I suggested some re-adaptation in a way that could motivate intellectuals into fashioning credible arenas for development discourse activities.

Using cues (attention directing cues) to guide students through using multimedia learning environments is vital as it reduces visual search and thus reducing the cognitive load on students' working memory. In some cases these visual cues are used in the form of text box to guide students' attention and to provide critical information at the same time. These text cues may impose heavy cognitive load on learners, which cause working memory overload and, therefore, hinder learning. One of the reasons for this working memory overload is the split attention effect that occurs when learners need to mentally integrate two related sources of information at the same time in order for the learning materials to be understood. The current empirical study was performed to investigate the effects of different text cues that vary only in the opacity level of the text box (opaque, semi-transparent, transparent) on reducing split attention. A mental effort scale and a task performance test were administrated to 119 participants. A quantitative analysis was conducted and results from it showed that students from the semi-transparent group spent less cognitive effort learning the materials than students in both the transparent and the opaque groups. Moreover there was a positive significant correlation between the mental effort spent in learning the materials and the students' performance.

This poster presents an innovative user-support method for structured data entry that resulted from working with a large patient registry reporting group in a leading hospital, tasked with efficient electronic medical record discretization. It is a method of using syntactic constraints on various fields during web-forms coding, to auto-select the fields where values belong, this allows coders to spend more time looking at the clinical narrative documentation and less time attending to the mouse, the browser, and field-selection on the data entry pages. The method is to meta-program a "floating text box" for intermediate, semi-structured notes, with behavior specific to the choices on that page. It bridges the gap between abbreviations, which are easy, and datamining natural language text, which is hard. The ideas can be generalized to auto-filling forms and semi-autonomous forms filling beyond the medical domain.

The labelling effect describes cases in which a verbal label is affixed to a stimulus and then exerts its distorting influence in subsequent judgment or recall of that stimulus (cf. the anchoring effect in Chapter 12, the misinformation effect in Chapter 22, and the hindsight bias in Chapter 23). Text box 20.1 lists a number of examples demonstrating labelling effects in different domains. This list already suggests that labelling effects may be rather widespread in our lives. More examples, from more applied domains, are given in Text box 20.2.

Objectives: In the modern era of web services, the web site registration process plays a vital role for authorized access. The process can be easily cracked by the hackers through their false registration. To overcome this problem, we introduce a Variant Random Order (VRO) in text-graphics color CAPTCHA approach for enhancing web security. Methods: In our proposed approach, the user has to enter all the characters based on the random number generation model, where the random number is to be displayed under the corresponding CAPTCHA characters. Based on the sorted order (generally ascending) of the random number, the user has to select the CAPTCHA characters one by one and to fill in the given empty text box. After typing all the characters from a given CAPTCHA, then the user can sign and enter into the required web area. If any mistake made in the entry process, a new CAPTCHA will be generated for the same login process. Consequently, we say that the process will be unbreakable by any Robot and in turn the website access is completely restricted. The number of CAPTCHA characters along with a random number is to be varied for every attempt. Also, each and every character in the CAPTCHA is to be represented with different colors, which in turn the pixel intensity can be varied for the same character representation. Findings: Applications: This type of techniques helps the proposed system to display the CAPTCHA with additional security in major application areas such as health services, military services and banking services