Acquisition of Pig Latin: a case study

ABSTRACTA boy's acquisition of Pig Latin was monitored throughout the year preceding first grade. Abilities underlying this game include the identification of words, deletion of the first syllabic onset (i.e. prevocalic consonants) of each word, blending of this onset and the suffix [e1] onto the word's end, and short-term memory for speech units. Performance improved over time as the underlying abilities developed. Meanwhile, various informative errors were made. Throughout most of the study, onsets that were correctly removed from a word's beginning were often added to its end incorrectly; unstressed function words were repeated intact and not transformed; and the first syllabic onset was overlooked when the syllable was unstressed. Because speech games like this one depend upon basic language skills, they can clarify aspects of ordinary language development.

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Cognitive predictors of counting skills

Journal of Numerical Cognition ◽

10.5964/jnc.v4i2.116 ◽

2018 ◽

Vol 4 (2) ◽

pp. 410-428 ◽

Cited By ~ 5

Author(s):

Tuire Koponen ◽

Kenneth Eklund ◽

Paula Salmi

Keyword(s):

Predictive Value ◽

Structural Equation ◽

Short Term Memory ◽

Language Skills ◽

First Grade ◽

Cognitive Factors ◽

Short Term ◽

Term Memory ◽

Reasoning Skills ◽

Nonverbal Reasoning

Rote counting skills have found to be a strong predictor of later arithmetic and reading fluency. However, knowledge of the underlying cognitive factors influencing counting skill is very limited. Present study examined to what extent language skills (phonology, vocabulary, and morphology), nonverbal reasoning skills, and memory at the age of five could explain counting skill at the beginning of first grade. Gender, parents’ education level and child’s persistence were included as control variables. The question was examined in a longitudinal sample (N = 101) with a structural equation model. Results showed that language skills together with memory, nonverbal reasoning skills and parent’s education explained only 22% of the variance in counting at the beginning of the first grade. Vocabulary, morphology, and verbal short-term memory were found to be interchangeable predictors, each explaining approximately 7%–9%, of counting skill. These findings challenge the interpretation of counting as a strongly language-based number skill. However, additional analysis among children with dyslexia revealed that memory and language skills, together with a child’s persistence and gender, had a rather strong predictive value, explaining 34%–46% of counting skill. Together these results suggest that verbal short-term memory and language skills at the age of five have not the same predictive value on counting skill at the beginning of school among a population-based sample as found in subjects with language impairment or learning difficulties, and thus, other cognitive factors should be taken into account in further research related to typical development of counting skill.

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Estimation of municipal solid waste amount based on one-dimension convolutional neural network and long short-term memory with attention mechanism model: A case study of Shanghai

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.148088 ◽

2021 ◽

Vol 791 ◽

pp. 148088

Author(s):

Kunsen Lin ◽

Youcai Zhao ◽

Lu Tian ◽

Chunlong Zhao ◽

Meilan Zhang ◽

...

Keyword(s):

Neural Network ◽

Municipal Solid Waste ◽

Convolutional Neural Network ◽

Short Term Memory ◽

One Dimension ◽

Short Term ◽

Term Memory ◽

Mechanism Model ◽

Long Short Term Memory

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Arabic dialect sentiment analysis with ZERO effort. \\ Case study: Algerian dialect

INTELIGENCIA ARTIFICIAL ◽

10.4114/intartif.vol23iss65pp124-135 ◽

2020 ◽

Vol 23 (65) ◽

pp. 124-135

Author(s):

Imane Guellil ◽

Marcelo Mendoza ◽

Faical Azouaou

Keyword(s):

Sentiment Analysis ◽

Short Term Memory ◽

State Of The Art ◽

Short Term ◽

Term Memory ◽

Ongoing Work ◽

Long Short Term Memory ◽

Large Corpus ◽

Unique Condition

This paper presents an analytic study showing that it is entirely possible to analyze the sentiment of an Arabic dialect without constructing any resources. The idea of this work is to use the resources dedicated to a given dialect \textit{X} for analyzing the sentiment of another dialect \textit{Y}. The unique condition is to have \textit{X} and \textit{Y} in the same category of dialects. We apply this idea on Algerian dialect, which is a Maghrebi Arabic dialect that suffers from limited available tools and other handling resources required for automatic sentiment analysis. To do this analysis, we rely on Maghrebi dialect resources and two manually annotated sentiment corpus for respectively Tunisian and Moroccan dialect. We also use a large corpus for Maghrebi dialect. We use a state-of-the-art system and propose a new deep learning architecture for automatically classify the sentiment of Arabic dialect (Algerian dialect). Experimental results show that F1-score is up to 83% and it is achieved by Multilayer Perceptron (MLP) with Tunisian corpus and with Long short-term memory (LSTM) with the combination of Tunisian and Moroccan. An improvement of 15% compared to its closest competitor was observed through this study. Ongoing work is aimed at manually constructing an annotated sentiment corpus for Algerian dialect and comparing the results

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Prediksi Kunjungan Wisatawan Taman Nasional Gunung Merbabu dengan Time Series Forecasting dan LSTM

Jurnal Buana Informatika ◽

10.24002/jbi.v11i2.3825 ◽

2020 ◽

Vol 11 (2) ◽

pp. 131

Author(s):

Josua Manullang ◽

Albertus Joko Santoso ◽

Andi Wahju Rahardjo Emanuel

Keyword(s):

Time Series ◽

National Park ◽

Short Term Memory ◽

Lag Time ◽

Time Series Forecasting ◽

Mean Square ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Abstract. Prediction of tourist visits of Mount Merbabu National Park (TNGMb) needs to be done to control the number of visitors and to preserve the national park. The combination of time series forecasting (TSF) and deep learning methods has become a new alternative for prediction. This case study was conducted to implement several methods combination of TSF and Long-Short Term Memory (LSTM) to predict the visits. In this case study, there are 18 modelling scenarios as research objects to determine the best model by utilizing tourist visits data from 2013 to 2018. The results show that the model applying the lag time method can improve the model's ability to capture patterns on time series data. The error value is measured using the root mean square error (RMSE), with the smallest value of 3.7 in the LSTM architecture, using seven lags as a feature and one lag as a label.Keywords: Tourist Visit, Taman Nasional Gunung Merbabu, Prediction, Recurrent Neural Network, Long-Short Term MemoryAbstrak. Prediksi kunjungan wisatawan Taman Nasional Gunung Merbabu (TNGMb) perlu dilakukan untul pengendalian jumlah pengunjung dan menjaga kelestarian taman nasional. Gabungan metode antara time series forecasting (TSF) dan deep learning telah menjadi alternatif baru untuk melakukan prediksi. Studi kasus ini dilakukan untuk mengimplementasi gabungan dari beberapa macam metode antara TSF dan Long-Short Term Memory (LSTM) untuk memprediksi kunjungan pada TNGMb. Pada studi kasus ini, terdapat 18 skenario pemodelan sebagai objek penelitian untuk menentukan model terbaik, dengan memanfaatkan data jumlah kunjungan wisatawan di TNGMb mulai dari tahun 2013 sampai dengan tahun 2018. Hasil prediksi menunjukkan pemodelan dengan menerapkan metode lag time dapat meningkatakan kemampuan model untuk menangkap pola pada data deret waktu. Besar nilai kesalahan diukur menggunakan root mean square error (RMSE), dengan nilai terkecil sebesar 3,7 pada arsitektur LSTM, menggunakan tujuh lag sebagai feature dan satu lag sebagai label. Kata Kunci: Kunjungan Wisatawan, Taman Nasional Gunung Merbabu, Prediksi, Recurrent Neural Network, Long-Short Term Memory

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Peak Load Forecasting Using Long-Short Term Memory : Case Study of Jawa-Madura-Bali System

10.1109/ict-pep53949.2021.9600945 ◽

2021 ◽

Author(s):

Anindita Satria Surya ◽

Musa Partahi Marbun ◽

K.G.H. Mangunkusumo ◽

Muhammad Ridwan

Keyword(s):

Short Term Memory ◽

Peak Load ◽

Load Forecasting ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Time Series Forecasting in Retail Sales Using LSTM and Prophet

Advances in Business Information Systems and Analytics - Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry ◽

10.4018/978-1-7998-6985-6.ch011 ◽

2021 ◽

pp. 241-262

Author(s):

Clony Junior ◽

Pedro Gusmão ◽

José Moreira ◽

Ana Maria M. Tome

Keyword(s):

Time Series ◽

Data Science ◽

Short Term Memory ◽

Special Kind ◽

Time Series Forecasting ◽

Short Term ◽

Term Memory ◽

Use Of Time

Data science highlights fields of study and research such as time series, which, although widely explored in the past, gain new perspectives in the context of this discipline. This chapter presents two approaches to time series forecasting, long short-term memory (LSTM), a special kind of recurrent neural network (RNN), and Prophet, an open-source library developed by Facebook for time series forecasting. With a focus on developing forecasting processes by data mining or machine learning experts, LSTM uses gating mechanisms to deal with long-term dependencies, reducing the short-term memory effect inherent to the traditional RNN. On the other hand, Prophet encapsulates statistical and computational complexity to allow broad use of time series forecasting, prioritizing the expert's business knowledge through exploration and experimentation. Both approaches were applied to a retail time series. This case study comprises daily and half-hourly forecasts, and the performance of both methods was measured using the standard metrics.

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