Input Language Research Articles

Language choice and code-mixing in a longitudinal study of an Estonian-English bilingual child

The input that bilingual children receive influences their language proportions, language development, and code-mixing. Most studies on these topics have included early bilinguals whose input proportions undergo large changes in early childhood and whose parents use the one-parent-one-language family language policy. This paper examines the input-output proportions of an Estonian-English bilingual child over a period of 2.5 years (2;3-5;01) using recorded spontaneous speech from a situation where the input language proportions did not change and where the family language policy was different from the one-parent-one-language policy that is presented in most studies: the family rotated the language they all spoke by the day of the week. Additionally, the child’s code-mixing rate and her MLU scores are investigated to provide an overview of these factors in an unstudied input situation. Lastly, it is analyzed whether code-mixing by older siblings influences the code-mixing rate of the younger sibling. The results indicate that in the early phases of language development the child uses all the linguistic resources available to her, and as her language develops, she responds more in the language of the conversation and code-mixes less. However, there is also a period where the child unexpectedly almost stops speaking in Estonian regardless of the unchanged input. The data shows that code-mixed utterances are the longest, hence supporting previous research findings and indicating that code-mixing is a tool that helps the child communicate better. Code-mixing by siblings does not show any signs of affecting the younger sibling’s code-mixing rate, though a more thorough analysis is necessary. Hence, the results indicate the importance of input and shed light on input effects in bilingual language acquisition in an understudied input situation.

Development of automatic CNC machine with versatile applications in art, design, and engineering

The area of computer numerical control (CNC) machines has grown fast, and their use has risen significantly in recent years. This article presents the design and development of a CNC writing machine that uses an Arduino, a motor driver, a stepper motor, and a servo motor. The machine is meant to create 2D designs and write in numerous input languages using 3-axis simultaneous interpolated operations. The suggested machine is low-cost, simple to build, and can be operated with merely G codes. The performance of the CNC writing machine was assessed by testing it on a range of solid surfaces, including paper, cardboard, and wood. The results reveal that the machine can generate high-quality text and images with great accuracy and consistency. The proposed machine's ability to write in several input languages makes it appropriate for various applications, including art, design, and engineering.

Monte Carlo Workflow Unification for Nuclear Reactor Analysis with Metamodel-Driven Modeling

Monte Carlo (MC) transport codes are a cornerstone of nuclear reactor analysis frameworks, providing reference solutions and multigroup cross sections, and even as core components in multiphysics couplings. These applications can be seen in toolkits from the Nuclear Energy Advanced Modeling and Simulation program and the U.S. Nuclear Regulatory Commission’s BlueCRAB (Comprehensive Reactor Analysis Bundle). Contrary to their ubiquitousness in reactor physics modeling and simulation, popular MC codes, such as MCNP, Serpent, and KENO, are still reliant on antiquated textual input formats. These input languages use a plethora of keywords with terse syntax to specify all facets of a model, including its geometry, materials, physics settings, and tally options. This poses a steep learning curve and a poor user experience. Being tied to unique text-based input formats also significantly complicates programmatic input generation or modification that may be desired and/or required within a multiphysics framework. This work demonstrates how the development of programmatic interfaces for MC codes can support model unification and translation activities. Building on the Python application program interface (API) development of MCNPy, similar capabilities are being implemented for Serpent that are able to support model translation. In future work, the Serpent API capabilities will be made more robust and the work will be further expanded to include translations with KENO.

Establishing the reliability of metrics extracted from long-form recordings using LENA and the ACLEW pipeline.

Long-form audio recordings are increasingly used to study individual variation, group differences, and many other topics in theoretical and applied fields of developmental science, particularly for the description of children's language input (typically speech from adults) and children's language output (ranging from babble to sentences). The proprietary LENA software has been available for over a decade, and with it, users have come to rely on derived metrics like adult word count (AWC) and child vocalization counts (CVC), which have also more recently been derived using an open-source alternative, the ACLEW pipeline. Yet, there is relatively little work assessing the reliability of long-form metrics in terms of the stability of individual differences across time. Filling this gap, we analyzed eight spoken-language datasets: four from North American English-learning infants, and one each from British English-, French-, American English-/Spanish-, and Quechua-/Spanish-learning infants. The audio data were analyzed using two types of processing software: LENA and the ACLEW open-source pipeline. When all corpora were included, we found relatively low to moderate reliability (across multiple recordings, intraclass correlation coefficient attributed to the child identity [Child ICC], was < 50% for most metrics). There were few differences between the two pipelines. Exploratory analyses suggested some differences as a function of child age and corpora. These findings suggest that, while reliability is likely sufficient for various group-level analyses, caution is needed when using either LENA or ACLEW tools to study individual variation. We also encourage improvement of extant tools, specifically targeting accurate measurement of individual variation.

Are All Conversational Turns Equal? Parental Language Input and Child Language in Children with Hearing Loss during Daily Interactions

Are All Conversational Turns Equal? Parental Language Input and Child Language in Children with Hearing Loss during Daily Interactions

Supporting Early Language by Supporting Systemic Solutions

A large body of research shows connections between infants’ and toddlers’ home language input and a wide range of receptive and expressive early language skills. Some facets of caretaker input and early language skills are associated with socioeconomic status (SES), though not all. Given the complexity of language learning, language use, and its many pathways of connection to SES, testing causal links between these dimensions is difficult at best. Interventions aimed at changing parent language use have seen mixed success, in part because “language infusions” generally fail to target underlying challenges facing underresourced families, and perhaps because parent language is the wrong target. System-level interventions such as paid parental leave and expansion and enrichment of childcare and early education options hold greater promise for improving families’ lives, with positive repercussions for a broad range of family and child outcomes, including linguistic ones.

Language environment and early language production in Slovenian infants: An exploratory study using daylong recordings.

Daylong recordings provide an ecologically valid option for analyzing language input, and have become a central method for studying child language development. However, the vast majority of this work has been conducted in North America. We harnessed a unique collection of daylong recordings from Slovenian infants (age: 16-30months, N=40, 18 girls), and focus our attention on manually annotated measures of parentese (infant-directed speech with a higher pitch, slower tempo, and exaggerated intonation), conversational turns, infant words, and word combinations. Measures from daylong recordings showed large variation, but were comparable to previous studies with North American samples. Infants heard almost twice as much speech and parentese from mothers compared to fathers, but there were no differences in language input to boys and girls. Positive associations were found between the social-interactional features of language input (parentese, turn-taking) and infants' concurrent language production. Measures of child speech from daylong recordings were positively correlated with measures obtained through the Slovenian MacArthur-Bates Communicative Development Inventory. These results support the notion that the social-interactional features of parental language input are the foundation of infants' language skills, even in an environment where infants spend much of their waking hours in childcare settings, as they do in Slovenia.

Multilingual Speech Transcription and Translation System

This project implements a multilingual speech recognition and translation system using Python. The system leverages libraries such as `pygame` for audio playback, `gTTS` for text-to-speech conversion, `speech_recognition` for capturing and recognizing spoken language, and Machine Translation model for translating text between languages. Users interact with the system via a Streamlit-based web interface, where they can select the input and target languages and control the start and stop of the translation process. The program can auto-detect the input language if the user chooses, and it provides real-time feedback by displaying the recognized text, translating it into the chosen target language, and converting the translated text into speech. At the core of the system is a continuous loop that listens for speech input through the microphone, processes the audio to recognize the spoken words, and then translates the recognized text into the desired language. The translated text is not only displayed but also spoken aloud using the text-to-speech functionality. The interface is user-friendly, with clear indications of the current status (listening, processing, recognized text, translated text) and handles errors gracefully, providing feedback in case of issues. This project demonstrates a practical application of speech recognition and translation technologies, offering a seamless and interactive multilingual communication tool

Clingo2DSR - A clingo-based software system for declarative spatial reasoning

ABSTRACT We present a modified ground-and-solve approach based on the clingo Answer Set Programming (ASP) system to perform non-monotonic spatial reasoning tasks, Clingo2DSR. Our system is distinct from previous research integrating ASP with space in that it deals with complex real-world data and numerous time steps. Clingo2DSR is composed of (i) an input language defining spatial entities, functions, and relations; (ii) an external geometry database for performing spatial computations and checking spatial constraints; and (iii) a theory-based solving approach coupling symbolic ASP with external sources for sound and fast model search. We demonstrate our system on three real building models, in the context of architectural design, submitted for analyses and queries where spatial components play an important role.

Model checking programs in process-oriented IEC 61131-3 Structured Text

The process-oriented programming is a paradigm based on the process concept where each process is a concurrent finite state machine inside. The paradigm is intended for PLC (programmable logic controllers) developers to write Industry 4.0-enabled software. The poST language is a promising process-oriented extension of the IEC 61131-3 Structured Text (ST) language designed to provide a conceptual consistency of the PLC source code with technological description of the process under control. This language combines the advantages of FSM-based programming with the standard syntax of the ST language. We propose transformational semantics of poST providing rules for translation of poST language statements to Promela — the input language of the SPIN model checker. Following these semantic rules, our Xtext-based translator outputs a Promela model for the poST program. Our contribution is the poST transformational semantics and the method for automatic generation of the Promela code from poST control programs. The resulting Promela program is ready to be verified with SPIN model checker against linear temporal logic requirements to the source poST program. In the paper we provide an overview of related work, as well as a brief description of the poST and Promela languages. Further, the Promela poST translation rules cover control flow statements, process creation and state management constructs, and timeout management. Then we define service processes for modeling the external environment and managing high-level LTL specifications. Then we present the main ideas of implementing the translator poST to Promela. We also illustrate our approach using the example of a system for managing electricity consumption and production, including renewable sources.

The fast and the capacious: memory-efficient multi-GPU accelerated explicit state space exploration with GPUexplore 3.0

The GPU acceleration of explicit state space exploration, for explicit-state model checking, has been the subject of previous research, but to date, the tools have been limited in their applicability and in their practical use. Considering this research, to our knowledge, we are the first to use a novel tree database for GPUs. This novel tree database allows high-performant, memory-efficient storage of states in the form of binary trees. Besides the tree compression this enables, we also propose two new hashing schemes, compact-cuckoo and compact multiple-functions. These schemes enable the use of Cleary compression to compactly store tree roots. Besides an in-depth discussion of the tree database algorithms, the input language and workflow of our tool, called GPUexplore 3.0, are presented. Finally, we explain how the algorithms can be extended to exploit multiple GPUs that reside on the same machine. Experiments show single-GPU processing speeds of up to 144 million states per second compared to 20 million states achieved by 32-core LTSmin. In the multi-GPU setting, workload and storage distributions are optimal, and, frequently, performance is even positively impacted when the number of GPUs is increased. Overall, a logarithmic acceleration up to 1.9× was achieved with four GPUs, compared to what was achieved with one and two GPUs. We believe that a linear speedup can be easily accomplished with faster P2P communications between the GPUs.

Strategic syntactic restructuring during simultaneous interpreting from Turkish into English

This study investigated the effects of contextual constraint and transitional probability on verb interpreting latency and syntactic restructuring during simultaneous interpreting from Turkish verb-final into English (verb-medial) sentences by trainee and professional Turkish (A)—English (B) interpreters. We found that contextual constraint, but not transitional probability, leads to both a decrease in interpreting latency on the sentence-final verb and a higher degree of syntactic restructuring between the source language input and target language output in both trainee and professional interpreters. Moreover, no between-group differences were observed in the effect of contextual constraint on verb interpreting latency and syntactic restructuring. The present findings suggest that, irrespective of experience, interpreters use contextual cues to restructure the word order between the source input and target output and produce the verb faster in the target output. This provides an argument for examining interpreting latency and syntactic restructuring together, as possible indicators of unvoiced anticipation during simultaneous interpreting between languages with dissimilar structures, such as Turkish and English.

Features of Using Models Pspice Software

The paper notes the features of the input language of Pspice software package, methods of describing sentences of the input language, building own models of the studied object and using the models specified in the open library of this complex. Possible operating modes of the complex and methods of their effective use are described. The results obtained make it possible to carry out effective circuit and technical modeling and analysis of both known objects and newly created ones, while ensuring the economy of material and human resources

Fuzzing-based grammar learning from a minimal set of seed inputs

To be effective, a fuzzer needs to generate inputs that are well formed, so that they are not outright rejected by the Software Under Test (SUT) and can thus detect meaningful bugs. Grammar based fuzzers solve this problem, but they obviously require a grammar of the input language accepted by the SUT. Many times such grammar is unknown. Therefore, different black- and white-box algorithms have been proposed for learning them from SUTs. Black-box algorithms rely only on membership queries, but need access to carefully crafted well formed inputs in order to obtain good results. White-box algorithms require access to the source code and generally produce grammars with higher precision and recall, but at the expense of working only for specific programming languages and libraries. We propose a new algorithm and show through extensive experimentation that it can learn grammars from recursive descendent parsers with consistently high levels of both, recall and precision. Notably, this result was obtained starting with a couple of arbitrary seed inputs and includes evaluations with sophisticated languages such as Java Script Object Notation (JSON). Different to other state of the art white-box approaches, our method does not require sophisticated program analysis techniques such as dynamic tainting or symbolic execution. In fact, the experiments confirm that our method performs extremely well with just a (standard) generic Abstract Syntax Tree (AST) of the parsing program as input. The core of our method uses fuzzing techniques combined with fundamental theoretical results on grammar learning. Compared to other white-box approaches, ours is not tied to specific programming languages and tools, and thus can be easily ported. Regarding performance, we have shown that our algorithm works well in practice and that, under reasonable assumptions, its worst-case complexity is polynomial (with low exponents) w.r.t. time and space requirements.

Language support for verifying reconfigurable interacting systems

Reconfigurable interacting systems consist of a set of autonomous agents, with integrated interaction capabilities that feature opportunistic interaction. Agents seemingly reconfigure their interaction interfaces by forming collectives and interact based on mutual interests. Finding ways to design and analyse the behaviour of these systems is a vigorously pursued research goal. In this article, we provide a modelling and analysis environment for the design of such system. Our tool offers simulation and verification to facilitate native reasoning about the domain concepts of such systems. We present our tool named R-CHECK (please find the associated toolkit repository here: https://github.com/dsynma/recipe). R-CHECK supports a high-level input language with matching enumerative and symbolic semantics and provides modelling convenience for features such as reconfiguration, coalition formation, and self-organisation. For analysis, users can simulate the designed system and explore arising traces. Our included model checker permits reasoning about interaction protocols and joint missions.

Stem overgeneralizations in the acquisition of Croatian verbal morphology: Evidence from parental questionnaires

Studies on verbal overgeneralization often focus on languages with low morphological complexity. The Croatian conjugational system exhibits varying degrees of complexity, and this complexity is not primarily based on the number of inflectional morphemes, but on an elaborate system of stem changes. During early language development, children face the difficult task of acquiring this system, using overgeneralized forms to overcome its complexity. To date, studies have used a corpus-based method to retrieve overgeneralizations in child language, which has had limited success in capturing this phenomenon. The aim of this study was to investigate the production of overgeneralized verb forms in Croatian monolingual children aged 2;6 to 5;11 using a questionnaire in which parents report overgeneralizations used by their children. We tested the relationship between the production of overgeneralized forms and features of the input language (token frequency and class size). We hypothesized that the rate of overgeneralizations will depend on input language features, i.e. a higher rate of overgeneralizations for infrequent verbs and for verbs with smaller class size. The items selected for the questionnaire are the verbs with stem change used by parents in the longitudinal Croatian corpus of child language. Parents report overgeneralized forms in all verb classes, and verb frequency and class size negatively correlate with the proportion of overgeneralizations. Our results show that children gradually abstract morphological systems in a way that is highly sensitive to the properties of the input.

Influence of caregiver input and language experience on the production of coda laterals by English-Malay bilingual preschoolers in multi-accent Singapore.

Linguistic input in multi-lingual/-cultural contexts is highly variable. We examined the production of English and Malay laterals by fourteen early bilingual preschoolers in Singapore who were exposed to several allophones of coda laterals: Malay caregivers use predominantly clear-l in English and Malay, but their English coda laterals can also be l-less (vocalised/deleted) and in formal contexts, velarised. Contrastingly, the English coda laterals of the Chinese majority are typically l-less. Findings show that English coda laterals were overall more likely to be l-less than Malay laterals like their caregivers', but English coda laterals produced by children with close Chinese peer(s) were more likely to be l-less than those without. All children produced English coda clear-l, demonstrating the transmission of an ethnic marker that had emerged from long-term contact. In diverse settings, variation is intrinsic to the acquisition process, and input properties and language experience are important considerations in predicting language outcomes.

Uncertainty-Aware Self-Training for Low-Resource Neural Sequence Labeling

Neural sequence labeling (NSL) aims at assigning labels for input language tokens, which covers a broad range of applications, such as named entity recognition (NER) and slot filling, etc. However, the satisfying results achieved by traditional supervised-based approaches heavily depend on the large amounts of human annotation data, which may not be feasible in real-world scenarios due to data privacy and computation efficiency issues. This paper presents SeqUST, a novel uncertain-aware self-training framework for NSL to address the labeled data scarcity issue and to effectively utilize unlabeled data. Specifically, we incorporate Monte Carlo (MC) dropout in Bayesian neural network (BNN) to perform uncertainty estimation at the token level and then select reliable language tokens from unlabeled data based on the model confidence and certainty. A well-designed masked sequence labeling task with a noise-robust loss supports robust training, which aims to suppress the problem of noisy pseudo labels. In addition, we develop a Gaussian-based consistency regularization technique to further improve the model robustness on Gaussian-distributed perturbed representations. This effectively alleviates the over-fitting dilemma originating from pseudo-labeled augmented data. Extensive experiments over six benchmarks demonstrate that our SeqUST framework effectively improves the performance of self-training, and consistently outperforms strong baselines by a large margin in low-resource scenarios.

A fuzzy mathematical model-based approach to customer-side energy management for power companies

Abstract The power industry has a long history of unreasonable structure of electricity consumption, high energy-consuming enterprises, low efficiency of electricity, electric energy in the current is still a bottleneck that restricts economic development. First, a fuzzy mathematical model is established and optimized using adaptive fuzzy control, which can transform the fuzzy information of people’s input language into operation instructions and perform real-time regulation. Then the structure of the power customer side energy management system is designed to control the flexible load of the power customer. Describe the control principles of different devices, and analyze the strategies for start/stop control of power generation devices and charge/discharge control of energy storage devices. This paper combined with the actual situation of the current smart grid construction, researches and elaborates on the methods of power companies to carry out demand management i//n this situation, aiming to provide effective reference for power companies to carry out demand management, so that electricity can better serve the economic development. The research results show that the cost of electricity consumption of the proposed method is only $2.9426, which can effectively improve the economic efficiency of the system and improve the supply and demand balance.

Beyond model checking of idealized Lustre in Kind 2

This paper describes several new features of the open-source model checker Kind 2. Its input language and model checking engines have been extended to allow users to model and reason about systems with machine integers. In addition, Kind 2 can now provide traceability information between specification and design elements, which can be used for several purposes, including assessing the quality of a system specification, tracking the safety impact of model changes, and analyzing the tolerance and resilience of a system against faults or cyber-attacks. Finally, Kind 2 is also able to check whether a component contract is realizable or not, and provide a deadlocking computation and a set of conflicting guarantees when the contract is unrealizable.