scholarly journals On Computability of Data Word Functions Defined by Transducers

2021 ◽  
Author(s):  
Léo Exibard
Keyword(s):  
Data Word

Global North-Global South Relations Around a Notional U.S.-Mexico “Border Wall”

2020 ◽  
pp. 296-344
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Image Analysis ◽  
Global South ◽  
The Other ◽  
Data Word ◽  
Textual Data ◽  
Mexico Border ◽  
Global North ◽  
Word Search ◽  
The U.S ◽  
Image Analyses

In the present political moment, “border walls” between the U.S. and Mexico have become a flashpoint, representing binaries like governed / ungoverned spaces, security / insecurity, morality / immorality, respect / disrespect for human rights, human unity / disunity, North / South, haves / have-nots, citizens / non-citizens, Republicans / Democrats, conservatives / liberals, patriots / traitors, nationalists / internationalists (or globalists), and others. This work explores some of the thematic Global North – Global South implications of a notional “border wall” based on social imagery (in a multi-loop image analysis approach). This work questions how the “other” may be viewed through the limiting slats of a fence or windows in a wall. In addition to the image analyses, topic-related textual data will also be studied from various sources: academia, journalism, and social media (including mass search correlations, big data word search, related tags networks, and #hashtag network analysis).

scholarly journals Prosodically Conditioned Consonant Duration in Djambarrpuyŋu

2019 ◽  
pp. 002383091982660
Author(s):  
Kathleen Jepson ◽  
Janet Fletcher ◽  
Hywel Stoakes
Keyword(s):  
Initial Position ◽  
Northern Territory ◽  
Indigenous Languages ◽  
Pitch Accent ◽  
Data Word ◽  
The World ◽  
Disyllabic Words ◽  
Phonological Phrase ◽  
Australian Languages ◽  
Segment Duration

Cross-linguistically, segments typically lengthen because of proximity to prosodic events such as intonational phrase or phonological phrase boundaries, a phrasal accent, or due to lexical stress. Australian Indigenous languages have been claimed to operate somewhat differently in terms of prosodically conditioned consonant lengthening and strengthening. Consonants have been found to lengthen after a vowel bearing a phrasal pitch accent. It is further claimed that this post-tonic position is a position of prosodic strength in Australian languages. In this study, we investigate the effects of proximity to a phrasal pitch accent and prosodic constituent boundaries on the duration of stop and nasal consonants in words of varying lengths in Djambarrpuyŋu, an Australian Indigenous language spoken in northeast Arnhem Land, Northern Territory, Australia. Our results suggest that the post-tonic consonant position does not condition longer consonant duration compared with other word-medial consonants, with one exception: Intervocalic post-tonic consonants in disyllabic words are significantly longer than word-medial consonants elsewhere. Therefore, it appears that polysyllabic shortening has a strong effect on segment duration in these data. Word-initial position did not condition longer consonant duration than word-medial position. Further, initial consonants in higher-level prosodic domains had shorter consonant duration compared with domain-medial word-initial consonants. By contrast, domain-final lengthening was observed in our data, with word-final nasals preceding a pause found to be significantly longer than all other consonants. Taken together, these findings for Djambarrpuyŋu suggest that, unlike other Australian languages, post-tonic lengthening is not a cue to prosodic prominence, whereas prosodic domain-initial and -final duration patterns of consonants are like those that have been observed in other languages of the world.

scholarly journals Implementasi Deep Learning Menggunakan Metode CNN dan LSTM untuk Menentukan Berita Palsu dalam Bahasa Indonesia

2021 ◽  
Vol 5 (4) ◽  
pp. 544
Author(s):  
Antonius Angga Kurniawan ◽  
Metty Mustikasari
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Data Input ◽  
Fake News ◽  
Accuracy Test ◽  
Learning Techniques ◽  
Data Word ◽  
Wrong Prediction ◽  
Long Short Term Memory ◽  
Better Than

This research aims to implement deep learning techniques to determine fact and fake news in Indonesian language. The methods used are Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The stages of the research consisted of collecting data, labeling data, preprocessing data, word embedding, splitting data, forming CNN and LSTM models, evaluating, testing new input data and comparing evaluations of the established CNN and LSTM models. The Data are collected from a fact and fake news provider site that is valid, namely TurnbackHoax.id. There are 1786 news used in this study, with 802 fact and 984 fake news. The results indicate that the CNN and LSTM methods were successfully applied to determine fact and fake news in Indonesian language properly. CNN has an accuracy test, precision and recall value of 0.88, while the LSTM model has an accuracy test and precision value of 0.84 and a recall of 0.83. In testing the new data input, all of the predictions obtained by CNN are correct, while the prediction results obtained by LSTM have 1 wrong prediction. Based on the evaluation results and the results of testing the new data input, the model produced by the CNN method is better than the model produced by the LSTM method.

scholarly journals Novel Approaches for Efficient Delay-Insensitive Communication

2019 ◽  
Vol 9 (2) ◽  
pp. 16 ◽  
Author(s):  
Florian Huemer ◽  
Andreas Steininger
Keyword(s):  
Power Efficiency ◽  
Communication Channels ◽  
Solution Space ◽  
Constant Weight Codes ◽  
Constant Weight ◽  
Coding Efficiency ◽  
Data Word ◽  
Pros And Cons ◽  
Intermediate Solution ◽  
Cost Efficient

The increasing complexity and modularity of contemporary systems, paired with increasing parameter variabilities, makes the availability of flexible and robust, yet efficient, module-level interconnections instrumental. Delay-insensitive codes are very attractive in this context. There is considerable literature on this topic that classifies delay-insensitive communication channels according to the protocols (return-to-zero versus non-return-to-zero) and with respect to the codes (constant-weight versus systematic), with each solution having its specific pros and cons. From a higher abstraction, however, these protocols and codes represent corner cases of a more comprehensive solution space, and an exploration of this space promises to yield interesting new approaches. This is exactly what we do in this paper. More specifically, we present a novel coding scheme that combines the benefits of constant-weight codes, namely simple completion detection, with those of systematic codes, namely zero-effort decoding. We elaborate an approach for composing efficient “Partially Systematic Constant Weight” codes for a given data word length. In addition, we explore cost-efficient and orphan-free implementations of completion detectors for both, as well as suitable encoders and decoders. With respect to the protocols, we investigate the use of multiple spacers in return-to-zero protocols. We show that having a choice between multiple spacers can be beneficial with respect to energy efficiency. Alternatively, the freedom to choose one of multiple spacers can be leveraged to transfer information, thus turning the original return-to-zero protocol into a (very basic version of a) non-return-to-zero protocol. Again, this intermediate solution can combine benefits from both extremes. For all proposed solutions we provide quantitative comparisons that cover the whole relevant design space. In particular, we derive coding efficiency, power efficiency, as well as area effort for pipelined and non-pipelined communication channels. This not only gives evidence for the benefits and limitations of the presented novel schemes—our hope is that this paper can serve as a reference for designers seeking an optimized delay-insensitive code/protocol/implementation for their specific application.

scholarly journals On Computability of Data Word Functions Defined by Transducers

2020 ◽  
pp. 217-236
Author(s):  
Léo Exibard ◽  
Emmanuel Filiot ◽  
Pierre-Alain Reynier
Keyword(s):  
Finite Alphabet ◽  
Turing Machines ◽  
Infinite Words ◽  
Data Word ◽  
Computable Functions

AbstractIn this paper, we investigate the problem of synthesizing computable functions of infinite words over an infinite alphabet (data $$\omega $$ ω -words). The notion of computability is defined through Turing machines with infinite inputs which can produce the corresponding infinite outputs in the limit. We use non-deterministic transducers equipped with registers, an extension of register automata with outputs, to specify functions. Such transducers may not define functions but more generally relations of data $$\omega $$ ω -words, and we show that it is PSpace-complete to test whether a given transducer defines a function. Then, given a function defined by some register transducer, we show that it is decidable (and again, PSpace-c) whether such function is computable. As for the known finite alphabet case, we show that computability and continuity coincide for functions defined by register transducers, and show how to decide continuity. We also define a subclass for which those problems are PTime.

Sign in / Sign up

Export Citation Format

Share Document