Miracle-dz/UniFAR-FTUs huggingface dataset:
Are L2 learners pressed for time?: Retrieval of grammatical gender information in L2 lexical access

Even at high levels of proficiency, grammatical gender poses a persistent challenge to adult learners of a second language. While a considerable body of research has investigated L2 use and processing of grammatical gender, it remains unclear what the source of this difficulty is. Evidence does, however, suggest that it is not a knowledge deficit. Studies using offline comprehension tasks that probe knowledge of grammatical gender have, for example, repeatedly found that advanced L2 learners perform at ceiling (Alarcón, 2011; Grüter et al., 2012; McCarthy, 2008; Montrul et al., 2008; White et al., 2004). This has been taken as evidence that late L2 learners acquire the mental representations for grammatical gender information. Further evidence has come from research demonstrating online sensitivity to gender agreement. Self-paced reading and eye-tracking studies have shown that, like native speakers, late L2 learners spend more time reading a post-nominal adjective in Spanish that does not agree in gender with its preceding noun compared to adjectives that do agree (Foote, 2011; Keating, 2009; Sagarra & Herschensohn, 2010). Event-related potential (ERP) studies have further found that L2 learners can show native-like P600 responses to gender agreement violations (Alemán Bañon et al., 2014; Foucart & Frenck-Mestre, 2011, 2012; Gillon Dowens et al., 2010, 2011; Tokowicz & MacWhinney, 2005). Together, these findings that advanced adult L2 learners show ceiling performance in offline tasks as well as online sensitivity to gender agreement violations suggest that the source of their difficulty in using grammatical gender online does not exclusively stem from a knowledge deficit. An alternative possibility is that L2 difficulty with grammatical gender has a processing-based locus. Indeed, there is substantial research to suggest that this is the case. Using eye-tracking while reading, Keating (2009) found that
Toward a parallel and cascading model of the writing system: A review of research on writing processes coordination

Efficient coordination of the different writing processes is central to producing good-quality texts, and is a fundamental component of writing skill. In this article, I propose a general theoretical framework for considering how writing processes are coordinated, in which writing processes are concurrently activated with more or less overlap between processes depending on their working memory demands, and with the flow of information cascading from central to peripheral levels of processing. To support this view, I review studies that investigated effects of handwriting skills on concurrent activation of higher order processes, and research on word production that explored how information cascades between levels of processing in the writing system. I argue that a parallel and cascading model makes it possible to combine different findings in a common integrated framework and thus constitutes a heuristic for further understanding coordination of the different levels of processing involved in writing.
Multimedia Glosses and Their Effect on L2 Text Comprehension and Vocabulary Learning

The present study investigates the effects that different types of multimedia glosses, namely textual, pictorial, and textual + pictorial, have on text comprehension and vocabulary learning when the goal is exclusively comprehension of a computerized text. This study is based on the theoretical framework of attention, which maintains that attention is critical in the acquisition process of an L2 (Robinson, 1995; Schmidt, 1995, 2001; Tomlin and Villa, 1994). Ninety-four participants read a text under one of four gloss conditions while asked to think aloud. This study investigated whether any of the conditions promoted noticing and whether this noticing led to better comprehension of the text and learning of the target vocabulary words. Reading comprehension, recognition, and production measures were utilized in a pre-post test design. Results of quantitative and qualitative analyses of the data gathered showed first that all multimedia gloss groups noticed and recognized significantly more of the target words than the control group. Second, no significant differences were found among any of the groups in production of the target vocabulary items. Finally, regarding comprehension, results showed that the combination gloss group significantly outperformed all other groups. These results confirm that the multimedia glosses under investigation have a different effect on comprehension and vocabulary learning respectively.
GALATEAS D2W: A Multi-lingual Disambiguation to Wikipedia Web Service

ABSTRACT The motivation for entity extraction within a digital culturalcollection is the enrichment potential of such a tool – usefulin this context for such tasks as metadata generation andquery log analysis. The use of Disambiguation to Wikipediaas our particular entity extraction tool is motivated by itsgeneralisable nature and its suitability to noisy text. Theparticular methodolgy we use does not avail of speciﬁc nat-ural language tools and therefore can be applied to otherlanguages with minimal adaptation. This has allowed us todevelop a multi-lingual Disambiguation to Wikipedia toolwhich we have deployed as a web service for the use of thecommunity. Categories and Subject Descriptors I.2 [Artiﬁcial Intelligence]: Natural Language Process-ing—Text analysis General Terms Algorithms,Languages, Experimentation Keywords Disambiguation to Wikipedia, Entity recognition 1. INTRODUCTION Information Retrieval within the digital cultural heritagecontext must contend with often inately “noisy” resources:poor spelling and punctuation, obsolete word forms and ab-breviated forms. This can be the case in both text-basedresources and in the metadata of image-based resources.This provides an often unsurmountable challenge to tradi-tional natural language processing techniques, e.g., tradi-tional Named Entity Recognition (ner). However, the en-
Multidisciplinary Design Optimization of a Quiet Propeller

Designing a quiet and efficient propeller is a demanding task since these two goals often lead to contradictory design trends. The task becomes even more complicated when additional constraints are introduced. The paper presents a Multidisciplinary Design Optimization (MDO) process for designing a quiet propeller under various constraints. The acoustic model of the propeller is presented in detail, including its validation. The new process is used in order to design a quiet propeller for an electric Mini-UAV. This design is subjected to power, structural and side constraints. The design variables include: blade geometry, blade cone angle, propeller radius, number of blades, and rotational speed. It is shown that the motor characteristics have an important influence on the optimization and therefore it is important to take them into account during the design process. The influence of the various parameters on the design and propeller’s characteristics are presented and discussed in detail.
The development of the knowledge of orthographic redundancy of experienced and inexperienced nonnative adult readers of French

2. When an image on the film is obliterated with a round black mark, it is an indication o f either blurred copy because o f movem ent during exposure, duplicate copy, or copyrighted materials tha t should n o t have been filmed For blurred pages, a good image o f the page can be found in the adjacent frame. If copyrighted materials were deleted, a target note will appear listing the pages in the adjacent frame.
How Mobile Learning Can Change Education

Frequent technological changes have started the unstoppable changes in the development of human society. Unlimited access to the information is surely the first and the basic result of these changes. The next change, not less important, happened in the transfer and the presentation of the information and the knowledge. Development of mobile technologies enabled a new step out by making the users fully mobile, resulting in obliterating the time and space limits. These devices enable the unlimited access to the information, knowledge data bases, and multimedia contents and most importantly to the communication beyond time and space. Is today’s elementary school ready for the changes influenced by the mobile technologies? How can today’s teacher respond to the challenges of mobile technologies in their classroom?
The Use of Computer-Based Simulation to Aid Comprehension and Incidental Vocabulary Learning

One of the main issues in language learning is to find ways to enable learners to interact with the language input in an involved task. Given that computer-based simulation allows learners to interact with visual modes, this article examines how the interaction of students with an online video simulation affects their second language video comprehension and incidental vocabulary learning. Forty-three Arab adult learners were randomly assigned to either an experimental group or a control group. Students in the experimental group (players) participated in a virtual knee surgery simulation by dragging various surgery devices, which were shown in the clip, over the knee of a patient. Those in the control group (viewers) viewed the same video in YouTube and were requested to watch and listen to the video surgery. After the treatments, the students took comprehension and vocabulary recognition tests (image association with words). Results show that the players significantly outperformed the viewers in these two tests. The pedagogical implications of this study suggest that interactivity in language input is crucial in facilitating second language acquisition.
Clinical roles of soluble ST2 for the outcomes of cardiac valve operations

Soluble ST2 is a novel cardiac biomarker that reflects cardiac injury and the response to treatment. Previous studies showed that ST2 has less confounding and more specific than NT-proBNP. ACCF/AHA has proposed 2013 Guidelines for the Management of Heart Failure that includes ST2 in the recommendations. In the research proposal, we will assess the clinical role of serum ST2 level in cardiac non-coronary valve surgical patients.
The Development of an Assessment for Learning Model for Elementary Classroom

Assessment for Learning for elementary classroom is a concept that aims to stimulate self learning and development among the student via assessment model. This research aimed to 1) develop an Assessment for Learning Model for elementary classroom and 2) test and assess the outcome of our model. The authors utilized interviewing, questionnaire and assessment form as tools for data gathering. The data was analyzed by statistical methods, namely, mean and standard deviation. Lastly, we performed the t-test Dependent to test our hypothesis and we’ve found that the Assessment for Learning Model for elementary classroom comprises of 2 phase and 6 steps. The result comprises two stages. The planning stage is composed of (1) define assessment objectives and what students need to achieve (2) define performance benchmark (3) design learning methodology and assessment method in accord with learning objectives. The teaching and feedback stage is composed of (1) integrate assessment with teaching/learning activities (2) Feedback after learning unit (3) Feedback result is used to improve primary school student's performance. The result of using the designed assessment model reveals that students have improved their learning achievement, self-regulation, and self-efficacy at statistically significant level of . 05.
Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration

Objective The advent of Electronic Medical Records (EMR) with large electronic imaging databases along with advances in deep neural networks with machine learning has provided a unique opportunity to achieve milestones in automated image analysis. Optical coherence tomography (OCT) is the most commonly obtained imaging modality in ophthalmology and represents a dense and rich dataset when combined with labels derived from the EMR. We sought to determine if deep learning could be utilized to distinguish normal OCT images from images from patients with Age-related Macular Degeneration (AMD). Design EMR and OCT database study Subjects Normal and AMD patients who had a macular OCT. Methods Automated extraction of an OCT imaging database was performed and linked to clinical endpoints from the EMR. OCT macula scans were obtained by Heidelberg Spectralis, and each OCT scan was linked to EMR clinical endpoints extracted from EPIC. The central 11 images were selected from each OCT scan of two cohorts of patients: normal and AMD. Cross-validation was performed using a random subset of patients. Receiver operator curves (ROC) were constructed at an independent image level, macular OCT level, and patient level. Main outcome measure Area under the ROC. Results Of a recent extraction of 2.6 million OCT images linked to clinical datapoints from the EMR, 52,690 normal macular OCT images and 48,312 AMD macular OCT images were selected. A deep neural network was trained to categorize images as either normal or AMD. At the image level, we achieved an area under the ROC of 92.78% with an accuracy of 87.63%. At the macula level, we achieved an area under the ROC of 93.83% with an accuracy of 88.98%. At a patient level, we achieved an area under the ROC of 97.45% with an accuracy of 93.45%. Peak sensitivity and specificity with optimal cutoffs were 92.64% and 93.69% respectively. Conclusions Deep learning techniques achieve high accuracy and is effective as a new image classification technique. These findings have important implications in utilizing OCT in automated screening and the development of computer aided diagnosis tools in the future.
Frequency domain approach to closed-loop identification based on output inter-sampling scheme

A novel frequency domain closed-loop identification algorithm is proposed using the output inter-sampling scheme. It is illustrated that the inter-sampled plant output has cyclo-stationary property that can be used to eliminate the correlation between the plant input and output noise even though no persistently exciting test signal is available. It can be applied for the unstable plant identification problem, and can be extended to the situation where the output disturbance noise is colored. An experiment of magnetic suspension system is performed to demonstrate the effectiveness of the proposed approach.
Deep Learning and Its Applications to Signal and Information Processing

INTRODUCTION TO DEEP LEARNING Many traditional machine learning and signal processing techniques exploit shallow architectures, which contain a single layer of nonlinear feature transformation. Examples of shallow architectures are conventional hidden Markov models (HMMs), linear or nonlinear dynamical systems, conditional random fields (CRFs), maximum entropy (MaxEnt) models, support vector machines (SVMs), kernel regression, and multilayer perceptron (MLP) with a single hidden layer. A property common to these shallow learning models is the simple architecture that consists of only one layer responsible for transforming the raw input signals or features into a problem-specific feature space, which may be unobservable. Take the example of a support vector machine. It is a shallow linear separation model with one feature transformation layer when kernel trick is used, and with zero feature transformation layer when kernel trick is not used. Human information processing
Placing pauses in read spoken Spanish: a model and an algorithm

The purpose of this work is to describe the appearance and location of typographically unmarked pauses in any Spanish text to be read. An experiment is designed to derive pause location from natural speech: results show that Intonation Group length constraints guide the appearance of pauses, which are placed depending on syntactic information. Then, a rule-based algorithm is developed to automatically place pauses whose performance is tested by means of qualitative tests. The evaluation shows that the system adequately places pauses in read texts, since it predicts 81% of orthographically unmarked pauses; when pauses associated to punctuation signs are included, the percentage of correct prediction increases to 92%.
A high Q RF CMOS differential active inductor

This paper presents the design of a differential CMOS active inductor for use in applications around 1-1.7 GHz. The architecture is based on a gyrator-C topology, where tuneable compensation techniques are applied to reduce phase error due to additional device parasitics, thus extending the useable frequency range of the inductor closer to the f/sub t/ of the technology. The inductance value and quality factor are also independently tuneable, and simulation results using device parameters from the AMS 0.8 /spl mu/m CMOS process demonstrate operation at 1.5 GHz with a Q of over 300. The implementation of a tuneable bandpass filter using the inductor is also demonstrated.
Fast centre- surround contrast modification

A new algorithm for fast contrast modification of standard dynamic range (SDR) images (8 bits/ channel) is presented. Its thrust is to enhance the contrast in the under-/over-exposed regions of SDR images, caused by the low dynamic range of the capturing device. It is motivated by the attributes of the shunting centre - surround cells of the human visual system. The main advantage of the proposed algorithm is its O(N ) complexity which results in very fast execution, even when executed on a conventional personal computer (0.2 s/frame for a 640 � 480 pixel resolution on a 3 GHz Pentium 4). Thus, it moderately increases the computational burden if it is used as a pre-processing stage for other image processing algorithms. The proposed method is compared with other established algorithms, which can enhance the contrast in the under-/over-exposed regions of SDR images: the multi-scale Retinex with colour rendition, the McCann Retinex (McCann99), the rational mapping function and the automatic colour equalisation. The results obtained by this comparison indicate that the proposed algorithm exhibits at least comparable results in contrast modification tasks to the other algorithms, in significantly reduced execution times.
Speaker normalization for speech recognition

A codeword-dependent neural network (CDNN) is presented for the study of speaker adaptation. The CDNN is used as a nonlinear mapping function to transform speech data between two speakers. The mapping function is characterized by a number of important properties. First, the assembly of mapping functions enhances overall mapping quality. Second, multiple input vectors are used simultaneously in the transformation. This not only makes full use of dynamic information but also alleviates possible errors in the supervision data. Finally, the mapping function is derived from training data, with the quality dependent on the available amount of training data. Based on speaker-dependent models, performance evaluation showed that speaker normalization significantly reduced the error rate from 41.9% to 5.0%. <<ETX>>
Increasing Distributed IT&C Application Security

The development of distributed IT & C applications â€“ DIA is presented alongside their main characteristics and the actors involved in activities through-out their lifecycle are identified in the before-mentioned scope. Aspects pertaining security risks, as well as methods of enhancing security, are detailed by DIA architectural features. The analysis includes risk elements, vulnerabilities, means of enhancing the behavior of the system, as well as a hierarchical feature dependency model based on a qualitative assessment of DIA security features, obtained through an inquiry in the common means of protection used by Romanian professionals, as well as their prioritization in the context of limited resources. A graph-based model of feature interactions is built. The last section deals with the ways of improving risk detection methods, as derived from the answers and features presented.
Accounting Automation and Insight at the Speed of Thought

ABSTRACT: Modern organizations are inundated with data, and they often struggle to organize it in an efficient and effective manner in order to get the most value from the data. The context of this case is, thus, situated in current business practice. Students are given large data files that were extracted from an enterprise system. They must use Microsoft Access and Excel to summarize and organize the data to create a dynamic profit and loss statement. Basic skills in Excel and general accounting knowledge are assumed, while Access knowledge is not assumed. The Teaching Notes provide solutions and are organized to allow instructors to provide minimal guidance or fully annotated directions.
Application of Hyperbolastic Growth and Hypertabastic Survival Methods in Biomedical and Cancer Research

Submit Manuscript | http://medcraveonline.com processes of angiogenesis, tumor invasion, and metastasis [1]. The precision and flexibility of these hyperbolastic models have been tested against classical models such as Gompertz, logistic, and Weibull models, and the results of these tests have always been satisfactory. As reported in our PLOS-ONE manuscript [2], we analyzed the cervical cancer mortality of Whites and Blacks in the US using a type II longitudinal hyperbolastic mixedeffects model. In regard to embryonic and adult stem cell proliferation, the hyperbolastic models performed better than classical models. In a recent report by the scientists at the University of Pittsburgh Live Cell Imaging Laboratory [5], the hyperbolastic model was cited as a flexible model for analyzing stem cell proliferation.