Language-based Approaches Research Articles

Examining the challenges of tertiary teaching and learning in the accounting discipline within KwaZulu-Natal South Africa

Financial accounting poses significant challenges for students at tertiary institutions, often resulting in failure, extended graduation timelines, or dropouts, particularly in their first year of study. This study aimed to identify the obstacles encountered in teaching and learning accounting education within South African universities. The research, which employed a qualitative approach, focused on understanding the challenges faced by students in mastering financial accounting at the university level and the underlying causes. Twenty accounting learners from four public tertiary institutions in KwaZulu-Natal were interviewed using open-ended, structured questions via MS Teams. Results highlighted several key themes, including the transition from high school to university, learning strategies and the influence of high school accounting. Challenges identified encompassed the difficult nature of the subject, a shortage of qualified accounting educators, insufficient support staff, poor infrastructure conditions, inadequate classroom facilities and the absence of practical training environments. This study contributes to financial accounting higher education in South Africa by introducing a novel methodological approach, utilising a textual collage to represent data. By merging visual and language-based approaches, the collage offers a fresh perspective on educational research in accounting. Moreover, it contributes to the academic literature within the discipline, aiming to mitigate student dropout rates and prolonged graduation timelines in accounting programmes. Overall, this research endeavours to enhance pedagogical practices and support mechanisms within accounting education, fostering a conducive learning environment for students.

Summary of the 1st Natural Language-based Software Engineering Workshop (NLBSE 2022)

Natural language processing (NLP) refers to automatic computa- tional processing of human language, including both algorithms that take human-produced text as input and algorithms that pro- duce natural-looking text as outputs. There is a widespread and growing usage of NLP approaches to optimize many aspects of the development process of software systems. In particular, since natural language artifacts are used and reused during the software development lifecycle, the availability of natural language-based approaches and tools enabled the envisioning of methods for im- proving efficiency in software engineers, processes, and products. The research community has been discussing these approaches in the 1st edition of the Natural Language-Based Software Engineer- ing Workshop (NLBSE), collocated with ICSE (the International Conference on Software Engineering) in 2022. This event brought together researchers and industrial practitioners from NLP and the software engineering community to share experiences, pro- vide directions for future research, and encourage the usage of NLP techniques and tools for addressing software engineering- speci c challenges. In this paper, we present a summary of the 1st edition of the workshop, which comprised ve full papers, four short/position papers, ve tool competition/demonstration pa- pers, one keynote (\Deep Learning & Software Engineering: Past, Present and Future"by Denys Poshyvanyk), followed by extensive discussion among NLBSE participants. More details can be found at https://nlbse2022.github.io/index.html

Going Local to Global through Technology-Needs Assessment and Development of a Virtual Arctic Youth Wellbeing Network.

Strengths-based approaches to suicide prevention and life promotion in circumpolar regions must engage youth participation and leadership given the impact of suicide on this demographic. We describe the development of a youth-engaged community of practice (CoP) across circumpolar regions, and adaptations to the ECHO model as a foundation for this virtual CoP. We describe youth priorities for learning in the area of mental health and wellbeing, identified through a learning needs assessment. A curriculum was developed to address key areas of interest, including: cultural approaches to mental wellbeing; language-based approaches to mental wellbeing; resilience; government and policy; and suicide prevention. We describe steps taken to adapt the ECHO model, and to introduce Indigenous pedagogical and knowledge sharing approaches into the CoP in order to meet youth learning interests. We conclude that this virtual CoP was a feasible way to create a learning community, and suggest that a priority future direction will be to evaluate the impacts of this virtual CoP on youth engagement, satisfaction and learning.

Effects of Pain Reprocessing Therapy on Attributed Causes of Chronic Back Pain

Fear of pain is a modifiable risk factor that predicts the development and persistence of chronic pain. It is due in part to patients' beliefs that pain reflects injury or tissue damage—though this is often not the case. We recently developed a novel psychological treatment, Pain Reprocessing Therapy (PRT), aiming to help patients reattribute their pain as due to mind/brain rather than peripheral tissue causes. Here, we tested whether such changes in pain attributions, as revealed by natural language measures of pain attributions, may serve as a psychological mechanism of PRT. We randomized chronic back pain patients (N = 151) to PRT, open-label placebo, or usual care, with large benefits observed for PRT vs. both comparison groups (Ashar et al. 2021). In this secondary analysis, blinded raters coded natural language attributions provided at pre- and post-treatment as either peripheral damage attributions (e.g., "disc") or mind/brain processes (e.g., "fear") attributions. Pre-treatment, only 10% of attributions were coded as mind/brain. PRT led to large increases in mind/brain attributions compared to both placebo, g = 1.74, and usual care, g = 1.64, both p < .001, with pre-to-post-treatment increases in mind/brain attributions correlating with pain intensity reductions within the PRT group, r(42) = -.34, p = .02. These increases in mind/brain attributions also partially mediated the effect of PRT vs. control on pain intensity at 1-year follow-up, βpath-ab = -0.37, p < .05. Within the PRT group, we observed marked decreases in the prevalence of peripheral-tissue related words (e.g., "activity", "posture") and increases in mind/brain-related words (e.g., "fear", "anxiety"). Overall, results suggest that patients largely attributed their pain to peripheral tissue causes at pre-treatment, that reattributing pain to mind/brain causes may be a mechanism underlying successful PRT treatment, and that natural language-based approaches may offer valuable insight into relevant pain beliefs. This study was funded by NIH grant R01 DA035484 (T.D.W.), R01 MH076136 (T.D.W.), NIH/NCATS Grant # TL1-TR-002386 (Y.K.A.), RSNA Presidents Circle Research Resident Grant, 2017 (T.F.F.), German Research Foundation (DFG) GE 2774/1-1 (S.G.), the Psychophysiologic Disorders Association, the Foundation for the Study of the Therapeutic Encounter, and community donations.

Language-based approach in achieving Sustainable Development Goals: A qualitative meta-analysis

Language-based approach in achieving Sustainable Development Goals: A qualitative meta-analysis

Parallel Logic Programming: A Sequel

AbstractMulti-core and highly connected architectures have become ubiquitous, and this has brought renewed interest in language-based approaches to the exploitation of parallelism. Since its inception, logic programming has been recognized as a programming paradigm with great potential for automated exploitation of parallelism. The comprehensive survey of the first twenty years of research in parallel logic programming, published in 2001, has served since as a fundamental reference to researchers and developers. The contents are quite valid today, but at the same time the field has continued evolving at a fast pace in the years that have followed. Many of these achievements and ongoing research have been driven by the rapid pace of technological innovation, that has led to advances such as very large clusters, the wide diffusion of multi-core processors, the game-changing role of general-purpose graphic processing units, and the ubiquitous adoption of cloud computing. This has been paralleled by significant advances within logic programming, such as tabling, more powerful static analysis and verification, the rapid growth of Answer Set Programming, and in general, more mature implementations and systems. This survey provides a review of the research in parallel logic programming covering the period since 2001, thus providing a natural continuation of the previous survey. In order to keep the survey self-contained, it restricts its attention to parallelization of the major logic programming languages (Prolog, Datalog, Answer Set Programming) and with an emphasis on automated parallelization and preservation of the sequential observable semantics of such languages. The goal of the survey is to serve not only as a reference for researchers and developers of logic programming systems but also as engaging reading for anyone interested in logic and as a useful source for researchers in parallel systems outside logic programming.

Ancient West-Coastal Bantu Banana Vocabulary: Testing Linguistic Method for Reconstructing Musa History in Africa

Lexical data has been key in attempts to reconstruct the early history of the banana (Musa sp.) in Africa. Previous language-based approaches to the introduction and dispersal of this staple crop of Asian origin have suffered from the absence of well-established genealogical classifications and inadequate historical-linguistic analysis. We therefore focus in this article on West-Coastal Bantu (WCB), one specific branch within the Bantu family whose genealogy and diachronic phonology are well established. We reconstruct three distinct banana terms to Proto-West-Coastal Bantu (PWCB), i.e. *dɪ̀‑ŋkòndò/*mà‑ŋkòndò ‘plantain’, *dɪ̀‑ŋkò/*mà‑ŋkò ‘plantain’ and *kɪ̀‑túká/*bì‑túká ‘bunch of bananas’. From this new historical-linguistic evidence we infer that AAB Plantains, one of Africa’s two major cultivar subgroups, already played a key role in the subsistence economy of the first Bantu speakers who assumedly migrated south of the rainforest around 2500 years ago. We furthermore analyze four innovations that emerged after WCB started to spread from its interior homeland in the Kasai-Kamtsha region of Congo-Kinshasa towards the Atlantic coast, i.e. dɪ̀‑kòndè ‘plantain’, kɪ̀‑tébè ‘starchy banana’, banga ‘False Horn plantain’, and dɪ̀‑tòtò ‘sweet banana’. Finally, we assess the historical implications of these lexical retentions and innovations both within and beyond WCB and sketch some perspectives for future lexicon-based banana research.

A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter

Social media enable companies to assess consumers’ opinions, complaints and needs. The systematic and data-driven analysis of social media to generate business value is summarized under the term Social Media Analytics which includes statistical, network-based and language-based approaches. We focus on textual data and investigate which conversation topics arise during the time of a new product introduction on Twitter and how the overall sentiment is during and after the event. The analysis via Natural Language Processing tools is conducted in two languages and four different countries, such that cultural differences in the tonality and customer needs can be identified for the product. Different methods of sentiment analysis and topic modeling are compared to identify the usability in social media and in the respective languages English and German. Furthermore, we illustrate the importance of preprocessing steps when applying these methods and identify relevant product insights.

Switched Systems as Hybrid Programs

Real world systems of interest often feature interactions between discrete and continuous dynamics. Various hybrid system formalisms have been used to model and analyze this combination of dynamics, ranging from mathematical descriptions, e.g., using impulsive differential equations and switching, to automata-theoretic and language-based approaches. This paper bridges two such formalisms by showing how various classes of switched systems can be modeled using the language of hybrid programs from differential dynamic logic (dL). The resulting models enable the formal specification and verification of switched systems using dL and its existing deductive verification tools such as KeYmaera X. Switched systems also provide a natural avenue for the generalization of dL's deductive proof theory for differential equations. The completeness results for switched system invariants proved in this paper enable effective safety verification of those systems in dL.

Transforming task representations to perform novel tasks

An important aspect of intelligence is the ability to adapt to a novel task without any direct experience (zero shot), based on its relationship to previous tasks. Humans can exhibit this cognitive flexibility. By contrast, models that achieve superhuman performance in specific tasks often fail to adapt to even slight task alterations. To address this, we propose a general computational framework for adapting to novel tasks based on their relationship to prior tasks. We begin by learning vector representations of tasks. To adapt to new tasks, we propose metamappings, higher-order tasks that transform basic task representations. We demonstrate the effectiveness of this framework across a wide variety of tasks and computational paradigms, ranging from regression to image classification and reinforcement learning. We compare to both human adaptability and language-based approaches to zero-shot learning. Across these domains, metamapping is successful, often achieving 80 to 90% performance, without any data, on a novel task, even when the new task directly contradicts prior experience. We further show that metamapping can not only generalize to new tasks via learned relationships, but can also generalize using novel relationships unseen during training. Finally, using metamapping as a starting point can dramatically accelerate later learning on a new task and reduce learning time and cumulative error substantially. Our results provide insight into a possible computational basis of intelligent adaptability and offer a possible framework for modeling cognitive flexibility and building more flexible artificial intelligence systems.

StreamQL: a query language for processing streaming time series

Real-time data analysis applications increasingly rely on complex streaming computations over time-series data. We propose StreamQL, a language that facilitates the high-level specification of complex analyses over streaming time series. StreamQL is designed as an algebra of stream transformations and provides a collection of combinators for composing them. It integrates three language-based approaches for data stream processing: relational queries, dataflow composition, and temporal formalisms. The relational constructs are useful for specifying simple transformations, aggregations, and the partitioning of data into key-based groups or windows. The dataflow abstractions enable the modular description of a computation as a pipeline of stages or, more generally, as a directed graph of independent tasks. Finally, temporal constructs can be used to specify complex temporal patterns and time-varying computations. These constructs can be composed freely to describe complex streaming computations. We provide a formal denotational semantics for StreamQL using a class of monotone functions over streams. We have implemented StreamQL as a lightweight Java library, which we use to experimentally evaluate our approach. The experiments show that the throughput of our implementation is competitive compared to state-of-the-art streaming engines such as RxJava and Reactor.

Performance Based Abstraction of Biomimicry Design Principles using Prototyping

A key challenge faced by biomimicry practitioners is making the conceptual leap between biology and design, particularly regarding collaborating across these knowledge domains and developing and evaluating design principles abstracted from biology. While many tools and resources to support biomimicry design exist, most largely rely on semantic techniques supporting analogical translation of information between biology and design. However, the challenges of evaluation and collaboration are common in design practice and frequently addressed through prototyping. This study explores the utility of prototyping in the unique context of biomimicry by investigating its impact on the abstraction and transfer of design principles derived from biology as well as on cross-domain collaboration between biologists and designers. Following a survey exploring current practices of practitioners, in depth interviews provided detailed accounts of project experiences that leveraged prototyping. Four primary themes were observed: (1) Approximation; (2) The Prototyping Principle; (3) Synthesis and Testing; and (4) Validation. These themes introduce a unique abstraction and transfer process based on form-finding and collaborative performance evaluation in contrast to the widely accepted semantic language-based approaches. Our findings illustrate how designers and engineers can leverage a prototyping skillset in order to develop boundary objects between the fields of biology and design to navigate challenges uniquely associated with the biomimicry approach.

Language and Argument: a Review of the Field

This paper has a dual purpose: it both seeks to introduce the other works in this issue by illustrating how they are related to the field of argumentation as a whole, and to make clear the tremendous range of research currently being carried out by argumentation theorists which is concerned with the interaction and inter-reliance of language and argument. After a brief introduction to the development of the field of argumentation, as many as eight language-based approaches to the study of argument are identified, taking as their perspective: rhetoric, argument structure, argument as act, discourse analysis, corpus methods, emotive argument, and narrative argument. The conclusion makes it clear that these branches of study are all themselves interconnected and that it is the fusion of methodologies and theory from linguistics and the philosophical study of argument which lends this area of research its dynamism.

Language as design tool: an empirical and design perspective in the field of architecture and planning

ABSTRACTThe use of language-based tools is not popular and not common in spatial design processes. But the literature shows the potential of such approaches in different steps of the design process. Thus enhancing the skills of language-based design tools of urban planners and architects can positively influence a design process. The perception and use of such tools was examined in a survey along a standardized questionnaire with 59 urban planning and 42 architecture students at four German universities. Two student groups in urban planning and architecture were asked additionally to apply two specific language-based tools in a design task. The survey results show that students initially evaluated the potential of language as quite low. Introducing students to language-based tools upgraded their assessment. Interestingly, some language-based tools were ranked higher by architecture students than by their urban planning colleagues. Considering that a design process especially in more complex projects asks for different abilities and tools, it seems meaningful to expand the toolbox of professionals also towards language-based approaches. Students also benefit from this because this expansion creates more options for the different ‘design personalities’. Further studies are needed for assessing which language-based tools are specifically qualified to enhance a design.

Incremental computation with names

Over the past thirty years, there has been significant progress in developing general-purpose, language-based approaches to incremental computation, which aims to efficiently update the result of a computation when an input is changed. A key design challenge in such approaches is how to provide efficient incremental support for a broad range of programs. In this paper, we argue that first-class names are a critical linguistic feature for efficient incremental computation. Names identify computations to be reused across differing runs of a program, and making them first class gives programmers a high level of control over reuse. We demonstrate the benefits of names by presenting Nominal Adapton, an ML-like language for incremental computation with names. We describe how to use Nominal Adapton to efficiently incrementalize several standard programming patterns---including maps, folds, and unfolds---and show how to build efficient, incremental probabilistic trees and tries. Since Nominal Adapton's implementation is subtle, we formalize it as a core calculus and prove it is from-scratch consistent, meaning it always produces the same answer as simply re-running the computation. Finally, we demonstrate that Nominal Adapton can provide large speedups over both from-scratch computation and Adapton, a previous state-of-the-art incremental computation system.

Language, corpus and empowerment: applications to deaf education, healthcare and online discourses

Language, Corpus and Empowerment applies a novel corpus-driven approach to the exploration of the concept of empowerment in healthcare. The book proposes an innovative corpus-based methodology for finding evidence of empowerment in language use, using data from a video intervention delivered to families of deaf children, as well as assessing the effects of the intervention on the family. Language, Corpus and Empowerment provides a working definition of empowerment which incorporates concepts from linguistics and learning theory; uses corpus analysis to provide evidence of how video interventions can transform peoples perspectives; examines this new methodology as a potential tool for analysing conversational data longitudinally and at a case-by-case level; demonstrates how a corpus-based methodological approach can be applied in conjunction with other language-based approaches, such as discourse analysis and conversation analysis, to explore the ways in which complex social processes occur in interaction; makes a valuable development in the assessment of the impact of healthcare interventions and the language of empowerment. Insightful and ground-breaking, Language, Corpus and Empowerment is essential reading for anyone undertaking research within corpus linguistics.

Language-based approaches to support reading comprehension

Language-based approaches to support reading comprehension

Comparing Coarray Fortran (CAF) with MPI for several structured mesh PDE applications

Language-based approaches to parallelism have been incorporated into the Fortran standard. These Fortran extensions go under the name of Coarray Fortran (CAF) and full-featured compilers that support CAF have become available from Cray and Intel; the GNU implementation is expected in 2015. CAF combines elegance of expression with simplicity of implementation to yield an efficient parallel programming language. Elegance of expression results in very compact parallel code. The existence of a standard helps with portability and maintainability. CAF was designed to excel at one-sided communication and similar functions that support one-sided communication are also available in the recent MPI-3 standard. One-sided communication is expected to be very valuable for structured mesh applications involving partial differential equations, amongst other possible applications. This paper focuses on a comparison of CAF and MPI for a few very useful applications areas that are routinely used for solving partial differential equations on structured meshes. The three specific areas are Fast Fourier Techniques, Computational Fluid Dynamics, and Multigrid Methods.For each of those applications areas, we have developed optimized CAF code and optimized MPI code that is based on the one-sided messaging capabilities of MPI-3. Weak scalability studies that compare CAF and MPI-3 are presented on up to 65,536 processors. Both paradigms scale well, showing that they are well-suited for Petascale-class applications. Some of the applications shown (like Fast Fourier Techniques and Computational Fluid Dynamics) require large, coarse-grained messaging. Such applications emphasize high bandwidth. Our other application (Multigrid Methods) uses pointwise smoothers which require a large amount of fine-grained messaging. In such applications, a premium is placed on low latency. Our studies show that both CAF and MPI-3 offer the twin advantages of high bandwidth and low latency for messages of all sizes. Even for large numbers of processors, CAF either draws level with MPI-3 or shows a slight advantage over MPI-3. Both CAF and MPI-3 are shown to provide substantial advantages over MPI-2.In addition to the weak scalability studies, we also catalogue some of the best-usage strategies that we have found for our successful implementations of one-sided messaging in CAF and MPI-3. We show that CAF code is of course much easier to write and maintain, and the simpler syntax makes the parallelism easier to understand.

A survey of language-based approaches to Cyber-Physical and embedded system development

As computers continue to advance, they are becoming more capable of sensing, interacting, and communicating with the physical and cyber world. Medical devices, electronic braking systems in automotive applications, and industrial control systems are examples of the many Cyber-Physical Systems (CPS) that utilize these computing capabilities. Given the potential consequences of software related failures in such systems, a high degree of safety, security, and reliability is often required. Programming languages are important tools used by programmers to develop CPS. They provide a programmer with the ability to transform designs into machine code. Of equal importance is their ability to detect and avoid programming mistakes. The development of CPS has predominantly been accomplished using the C programming language. Although C is a powerful language, it lacks features present in other languages that facilitate the development of reliable systems. This has prompted research into language-based alternatives for improving program quality through the use of programming languages. This paper presents an overview of the characteristics of embedded and cyber-physical systems and the associated requirements imposed on programming languages. This is followed by a survey of relevant research into language-based methods for creating safe, reliable, and robust software for CPS.

Narco-narratives and Transnational Form: The Geopolitics of Citation in the Circum-Caribbean

This essay argues that narco-narratives--in film, television, literature, and music--depend on structures of narrative doubles to map the racialized and spatialized construction of illegality and distribution of death in the circum-Caribbean narco-economy. Narco-narratives stage their own haunting by other geographies, other social classes, other media; these hauntings refract the asymmetries of geo-political and socio-cultural power undergirding both the transnational drug trade and its artistic representation. The circum-Caribbean cartography offers both a corrective to nation- or language-based approaches to narco-culture, as well as a vantage point on the recursive practices of citation that are constitutive of transnational narco-narrative production.