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67 results for “Language Development”
Improving the Developer Experience with a Low-Code ProcessModelling Language: Companion site
<p>This companion site contains additional data to complement the paper:</p> <p><em><strong>Henriques, H., Lourenço, H., Amaral, V., and Goulão, M. (2018). Improving the developer experience with a low-code process </strong></em><em><strong>modelling</strong></em><em><strong> language. In ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018), Copenhagen, Denmark. ACM. https://doi.org/10.1145/3239372.3239387</strong></em></p> <p><strong>Abstract</strong></p> <p><strong>Context</strong><strong>: </strong>The OutSystems Platform is a development environment composed of several DSLs, used to specify, quickly build and validate web and mobile applications. The DSLs allow users to model different perspectives such as interfaces and data models, define custom business logic and construct process models.</p> <p><strong>Problem</strong><strong>: </strong>TheDSL for process modelling (Business Process Technology (BPT)), has a low adoption rate and is perceived as having usability problems hampering its adoption. This is problematic given the language maintenance costs.</p> <p><strong>Method:</strong> We used a combination of interviews, a critical review of BPT using the “Physics of Notation” and empirical evaluations of BPT using the System Usability Scale (SUS)and the NASA Task Load indeX (TLX), to develop a new version ofBPT, taking these inputs and Outsystems’ engineers culture into account.</p> <p><strong>Results: </strong>Evaluations conducted with 25 professional soft-ware engineers showed an increase of the semantic transparency on the new version, from 31% to 69%, an increase in the correctness of responses, from 51% to 89%, an increase in the SUS score, from 42.25 to 64.78, and a decrease of the TLX score, from 36.50 to 20.78. These differences were statistically significant.</p> <p><strong>Conclusions:</strong> These results suggest the new version of BPT significantly improved the developer experience of the previous version. The end users background with OutSystems had a relevant impact on the final concrete syntax choices and achieved usability indicators.</p> <p> </p> <p><strong>Contents</strong></p> <p>This companion site provides a permanent link for additional data to the supported paper.</p> <p>This repository includes:</p> <ul> <li>Surveys and Questionnaires used in the evaluation reported in the paper <ul> <li>Survey on OutSystems BPT notations (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/survey.pdf">survey.pdf</a>)</li> <li>Prototype Symbol Set Questionnaire (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/PrototypeSymbolSetQuestionnaire.pdf">PrototypeSymbolSetQuestionnaire.pdf</a>)</li> <li>Original BPT Evaluation (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/languages.png">languages.png</a>)</li> <li>Usability Evaluation (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/sus.png">sus.png</a>)</li> <li>Cognitive Effort Evaluation (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/tlx.png">tlx.png</a>)</li> <li>Testing environment screenshot (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/Testing%20Environment%20Screenshot.png">Testing Environment Screenshot</a>)</li> </ul> </li> <li>Statistics <ul> <li>SUS and NASA TLX <ul> <li>Descriptive statistics (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/SUSTLXDescriptiveStats.pdf">SUSTLXDescriptiveStats.pdf</a>)</li> <li>Normality tests (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/SUSTLXNormalityTests.pdf">SUSTLXNormality.pdf</a>)</li> <li>Correlation test (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/SUSTLXCorrelation.pdf">SUSTLXCorrelation.pdf</a>)</li> <li>Scatterplot (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/SUSTLXScatterPlot.pdf">SUSTLXScatterplot.pdf</a>)</li> </ul> </li> </ul> </li> </ul> <p> </p>
Common Genetic Variants in FOXP2 are Not Associated with Individual Differences in Language Development
<p>Three data sets used in” Common Genetic Variants <em>in FOXP2</em> Are Not Associated with Individual Differences in Language Development” are provided. The discovery data set was comprised of 834 children who were members of a Longitudinal sample and children who were members of a School sample. Both samples are contained in the Iowa data set. The Iowa data set contains a quantitative variable LCOMP that represents a composite z-score representing oral language ability. The data set also identifies which sample the children belonged to and the allele calls for 13 tag SNPs located across <em>FOXP2</em>. A second data file, ELVS, contains data from a separate sample of children who were used to test for replication of inconsistent evidence of an association between language and the SNP rs1916988. The ELVS file provides a composite oral language score scaled in standard score units (mean=100, SD=15) and the genotype calls for the SNP rs1916988.</p>
Figure 1. The Structure of the Automatic Translate Voice to Sign Language Animation System-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>All technologies of voice recognition, speaker identification and verification, each has its<br> own advantages and disadvantages and may requires different treatments and techniques. The<br> choice of which technology to use is application-specific. At the highest level, all voice recognition<br> systems contain two main modules: feature extraction and feature matching. Feature extraction is<br> the process that extracts a small amount of data from the voice signal that can later be used to<br> represent each word. Feature matching involves the actual procedure to identify the unknown word<br> by comparing extracted features from his/her voice input with the ones from a set of known words.<br> A wide range of possibilities exist for parametrically representing the speech signal for the<br> voice recognition task, such as Linear Prediction Coding (LPC), RASTA-PLP and Mel-Frequency<br> Cepstrum Coefficients (MFCC).</p>
Results and log of LLM-KG-Bench runs described in article "Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering", Meyer et al. 2023
<p>Results and logs of <a href="https://github.com/AKSW/LLM-KG-Bench">LLM-KG-Bench</a> runs described in article "Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering", Meyer et al., to appear in <a href="https://2023-eu.semantics.cc/page/accepted_posters">SEMANTICS 2023 poster track</a> proceedings.</p>
Timely Dictionary Development: In-person and Virtual Rapid Word Collection for Endangered Indigenous Languages
<p>Timely Dictionary Development: In-person and virtual Rapid Word Collection for endangered Indigenous Languages</p> <p>Dorothea Hoffmann, Wilhelm Meya, Abbie Hantgan-Sonko & Elliot Thornton</p> <p>Presented 5 October 2022 at the Berlin-Brandenburg Academy of Sciences and Humanities Where Do We Need to Go From Here? Language Documentation and Archiving in the International Decade of Indigenous Languages</p>
DCASE 2024 Task 9: Language-Queried Audio Source Separation | Development Set
<p><strong>== Description == </strong></p> <p>The development set is composed of audio samples from FSD50K [1] and Clotho v2 [2] datasets. FSD50K contains over 51k audio clips (~100 hours) manually labeled using 200 classes drawn from the AudioSet Ontology. For each audio clip in the FSD50K dataset, we generated one automatic caption for each audio clip by prompting ChatGPT (GPT-4) with its sound event tags. All audio files should be converted to mono 16 kHz audio for training LASS models. </p> <p>Clotho v2: <a href="../records/4783391">https://zenodo.org/records/4783391</a></p> <p>FSD50K: <a href="../records/4060432">https://zenodo.org/records/4060432</a></p> <p>Automatic captions generated for FSD50K:</p> <ul> <li>fsd50k_dev_auto_caption.json</li> <li>fsd50k_eval_auto_caption.json</li> </ul> <p>Prompt for generating captions:</p> <blockquote> <p>I will give you a number of lists containing sound events. Please write an one-sentence audio caption to describe these sounds.</p> <p>Make sure you are using grammatical subject-verb-object sentences. Directly describe the sounds and avoid using the word “heard”. Please don't describe the temporal order of these sound events. The caption should be less than 20 words.</p> </blockquote> <p>In addition to the development set, participants are free to use any external data (including private data) but are not allowed to use audio in Freesound uploaded between April and October 2023. Participants must specify all external resources utilized in their submission in the technical report.</p> <p><strong>== References ==</strong></p> <p>[1] Fonseca E, Favory X, Pons J, et al. FSD50k: an open dataset of human-labeled sound events. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 30: 829-852.</p> <p>[2] Drossos K, Lipping S, Virtanen T. Clotho: An audio captioning dataset. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2020: 736-740.</p> <p><strong>== Contact ==</strong></p> <p>Xubo Liu, xubo.liu@surrey.ac.uk</p>
Early, typical, and late talkers: an exploratory study on predictors of language development in the first two years of life
<p>Background: The consensus in scientific literature is that each child undergoes a unique linguistic development path, albeit with shared developmental stages. Some children excel or lag behind their peers in language skills. Consequently, a key challenge in language acquisition research is pinpointing factors influencing individual differences in language development.</p> <p>Methods: We observed children longitudinally from 3 to 24 months of life to explore early predictors of vocabulary size. Based on the productive vocabulary size of children at 24 months, 30 children met our sample selection criteria: 10 late talkers and 10 early talkers, and we compared them with 10 typical talkers. We evaluated interactive behaviors at 3, 6, 9 and 12 months, considering vocal production, gaze at mother's face, and gestural production during mother-child interactions, and we considered mothers' report of children's actions and gestures and receptive-vocabulary size at 15 and 18 months.</p> <p>Results: Results indicated early precursors of language outcome at 24 months identifiable as early as 3 months in vocal productions, 6 months for gaze at mother's face and 12 months for gestural productions.</p> <p>Conclusions: Our research highlights both theoretical and practical implications. Theoretically, identifying the early indicators of belonging to the group of late or early talkers underscores the significant role of this developmental period for future studies. On a practical note, our findings emphasize the crucial need for early investigations to identify predictors of vocabulary development before the typical age at which lexical delay is identified.</p>
When the Dragons Defeat the Knight: Basilisk an Architectural Pattern for Platform and Language Independent Development
<p>Experimental dataset and the scripts needed to run the experiment described in the paper:</p> <p><strong> When the Dragons Defeat the Knight</strong>: <br> Basilisk an Architectural Pattern for Platform and Language Independent Development<br><em> Francesco Bertolotti, Walter Cazzola, Dario Ostuni</em> and <em>Carlo Castoldi</em></p> <p>Published in Journal of Systems and Software, 215, September 2024. Elsevier.</p>
Supplementary Material for "Investigating Software Development Teams Members' Perceptions of Data Privacy in the Use of Large Language Models (LLMs)"
<h3>ABSTRACT<strong>: </strong></h3> <p><strong>Context</strong>: Large Language Models (LLMs) have revolutionized natural language generation and understanding. However, they raise significant data privacy concerns, especially when sensitive data is processed and stored by third parties. <br><strong>Goal</strong>: This paper investigates the perception of software development teams members regarding data privacy when using LLMs in their professional activities. Additionally, we examine the challenges faced and the practices adopted by these practitioners. <br><strong>Method</strong>: We conducted a survey with 78 ICT practitioners from five regions of the country. <br><strong>Results</strong>: Software development teams members have basic knowledge about data privacy and LGPD, but most have never received formal training on LLMs and possess only basic knowledge about them. Their main concerns include the leakage of sensitive data and the misuse of personal data. To mitigate risks, they avoid using sensitive data and implement anonymization techniques. The primary challenges practitioners face are ensuring transparency in the use of LLMs and minimizing data collection. Software development teams members consider current legislation inadequate for protecting data privacy in the context of LLM use. <br><strong>Conclusions</strong>: The results reveal a need to improve knowledge and practices related to data privacy in the context of LLM use. According to software development teams members, organizations need to invest in training, develop new tools, and adopt more robust policies to protect user data privacy. They advocate for a multifaceted approach that combines education, technology, and regulation to ensure the safe and responsible use of LLMs.</p>
Sustainable Development Goals (SDGs) in English as a Foreign Language (EFL)
<p>The qualitative mixed-method intervention study, grounded in content analysis and comparative methodology, reveals that incorporating SDGs into teacher training programmes is key since there is a significant direct impact on society. The objective was firstly to create an SDG-based didactic proposal including inquiry-based learning as its pedagogical approach for developing critical thinking. Secondly, to study its effect on participants regarding raising awareness of SDGs and their projection to society as future teachers.</p>
Working on Rapid Language Development in Toddlers
ClinicalTrials.gov study NCT01975922. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Developing and Testing a Spanish-Language Intervention to Reduce Cancer-Related Sleep Disturbance
ClinicalTrials.gov study NCT04101526. IPD Sharing: NO. Countries: 2. Publications: 2.
Early, typical, and late talkers: an exploratory study on predictors of language development in the first two years of life
Open the record for dataset details and reuse information.
Development of compositionality through interactive learning of language and action of robots
Open the record for dataset details and reuse information.
Evaluating Large Language Models in Summarizing Developer Chat Conversations: A Linguistic Perspective
<p>This is a replication package that includes:</p> <ul> <li>GoldenSet: contains the best summaries by participants for each conversation and the corresponding LLM generated summaries)</li> <li>LinguisticAnalysis_HumanGenerated: linguistic analysis such as speech tags, entities, etc. for summaries created by the Mturk participants (golden set)</li> <li>LinguisticAnalysis_LLMGenerated: linguistic analysis such as speech tags, entities etc. for summaries generated by the large language models</li> </ul> <p> </p>
Baseline EEG in the first year of life: insights into the development of autism spectrum disorder and language impairments
<p>The dataset includes power values obtained from baseline EEG recorded in infants. Specifically, for each infant the Power Spectral Density (PSD) was estimated for epochs of 30 s (at least 2 epochs) using the Welch’s method (Hamming window of 10s with 50% overlap) and successively log transformed as follow: log PSD = log10(1+PSD). The power in the following bands was computed: delta (2-4 Hz), theta (4-6 Hz), low-alpha (6-9-Hz), high-alpha (9-13 Hz), beta (13-30 Hz) and gamma (30-45 Hz). Two clusters of 6 frontal electrodes were selected (frontal left cluster and frontal right cluster) and power values were then averaged across electrodes within each cluster. Thus, the database contains for each subjects and frequency band two values of power one related to the frontal left cluster and the other one related to frontal right cluster.</p> <p>Each raw in the database corresponds to a subject and each column to a different variable.</p> <p>Column A, Subject code;</p> <p>Column B, Group: 0 = typically developing (TD) infant; 1 = infant higher likelihood of developing language and learning impairments (HL-LLI); 2 = infant at higher likelihood of developing autism spectrum disorder (HL-ASD);</p> <p>Column C, Diagnosis_group: 0 = children from the original TD group (without familiarity for neither ASD nor LLI) who at 3 years of age scored above the 30th percentile in the parent-administered Language Development Survey (LDS) (Rescorla and Alley, 2001); 1 = children diagnosed with Developmental Language Disorders/Developmental Dyslexia (DLD/DD); 2 = children diagnosed with ASD;</p> <p>Column D, Time_point: 1 = data related to the EEG recording performed at six months of age; 2 = data related to the EEG recording performed at twelve months of age;</p> <p>Columns from E to P: power values obtained in the frontal left (F_sx) and right (F_dx) clusters for each frequency band.</p> <p>All details about subjects, groups, data acquisition and preprocessing are available in the following manuscript: Piazza, C., Dondena, C., Riboldi, E. M., Riva, V., & Cantiani, C. (2023). Baseline EEG in the first year of life: Preliminary insights into the development of autism spectrum disorder and language impairments. <em>iScience</em>, <em>26</em>(7).</p> <p>Matlab .m files related to the preprocessing steps and the power value extraction are also available.</p>
Promoting Early Language Development Through Parent-child Book Reading in Costa Rica
ClinicalTrials.gov study NCT04978766. IPD Sharing: YES. Countries: 1. Publications: 3.
Language Development Deficits and Early Interactive Music Intervention
ClinicalTrials.gov study NCT06261307. IPD Sharing: NO. Countries: 1. Publications: 3.
Development and Validation of a Digital Optotype for Near Vision in Greek Language.
ClinicalTrials.gov study NCT04242836. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Language Development in Cochlear Implant Children
ClinicalTrials.gov study NCT04595552. IPD Sharing: Not stated. Countries: 1. Publications: 3.
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Allen Brain Atlas
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.