Language Code Research Articles

Physiological signal analysis and open science using the Julia language and associated software

In this mini review, we propose the use of the Julia programming language and its software as a strong candidate for reproducible, efficient, and sustainable physiological signal analysis. First, we highlight available software and Julia communities that provide top-of-the-class algorithms for all aspects of physiological signal processing despite the language’s relatively young age. Julia can significantly accelerate both research and software development due to its high-level interactive language and high-performance code generation. It is also particularly suited for open and reproducible science. Openness is supported and welcomed because the overwhelming majority of Julia software programs are open source and developed openly on public platforms, primarily through individual contributions. Such an environment increases the likelihood that an individual not (originally) associated with a software program would still be willing to contribute their code, further promoting code sharing and reuse. On the other hand, Julia’s exceptionally strong package manager and surrounding ecosystem make it easy to create self-contained, reproducible projects that can be instantly installed and run, irrespective of processor architecture or operating system.

Language Codes, Topic Preferences and Identity Construction: The Case of Gendered Language on Social Media

This study on exploring the use of gendered language on social media is a qualitative-quantitative analysis of the male and female differences in the language codes, topic preferences, and identity construction. In-depth understanding of the gendered use of language in Facebook was investigated with a purposive sampling of 90 status updates taken from the posts of respondents who are enrolled in one of the premier public high schools in Cagayan Valley. Anchored on Thematic Analysis by Braun and Clarke (2006), the language codes, topic preferences and gender-based identity construction were explored. Findings reveal that both male and female who are active users of Facebook are multilingual whenever they go online. The discussion on the variety of topics speaks of the diversity of the interest of those involved in the communication platform. The interactivity that Facebook provides makes it a medium through which people define themselves by sharing what is going on in their lives or how they are feeling. This makes Facebook a forum where extension of a person’s real-life identity radiates. Moreover, the use of language by male and female are influenced by their social network in Facebook. Through Facebook status updates, male and female do not only express their emotions but also share details about their daily life activities as to what they were doing or what was going on in their lives at that moment. Thus, trainings and seminars among students and teachers on upholding preservation of culture and language must be part of the curriculum to promote youth empowerment, gender fair language and awareness of cyber-bullying, are hereby recommended to develop communicative competence leading to deeper understanding of one another.

Llasm: Naming Functions in Binaries by Fusing Encoder-only and Decoder-only LLMs

Predicting function names in stripped binaries, which requires succinctly summarizing semantics of binary code in natural languages, is a crucial but challenging task. Recently, many machine learning based solutions have been proposed. However, they have poor generalizability, i.e., fail to handle unseen binaries. To advance the state of the art, we present llasm ( L arge AS sembly L anguage M odel), a novel framework which fuses encoder-only and decoder-only LLMs for function name prediction. It refines encoder-only models to preserve more binary information and learn better binary representations. Then it adopts a novel architecture to project the encoding to the input space of a decoder-only natural language model, which enables it to have better capability of inferring general knowledge and better generalizability. We have evaluated llasm in the BinaryCorp and Debin datasets. llasm outperforms the state-of-the-art function name prediction tools by up to 19.9%, 40.7%, and 36.5% in precision, recall, and F1 score, with significantly better generalizability in unseen binaries. Our case studies further demonstrate the practical use cases of llasm in analyzing real-world malware, showing the usefulness of function name prediction.

‘Helping academics shine’: An exploration into the relationships working-class professional services staff have with others in UK higher education

AbstractDrawing on research interviews with thirteen self-identifying working-class professional services employees in Russell Group institutions, this article attempts to develop a nuanced approach to understanding institutional cultures in UK universities, through the relationships within these establishments. Throughout the study, the tensions between working-class professional services or administrative staff and their counterparts working on academic trajectories became clear. Often highly qualified themselves, the participants in this study describe feelings relating to inadequacy and often negative attitudes proliferated towards them by academic staff. These relationships are framed within the perspective of three themes: academic hierarchies and superior academic value, treatment of staff, and language codes and patterns. In contrast, the participants in this sample often describe their relationships with their networks in professional services as highly positive, developmental, and enriching. This study focused on working-class professional services staff specifically. In the field of working-class research, professional services staff from working-class backgrounds have currently been largely omitted from research, which tends to focus on working-class students and working-class academics. Not all professional services staff identify as working-class, but due to this deficit of evidence in the research field, it is this group in particular that this paper focuses on. The findings in this paper suggest that the relationships these professional services staff have with academics are often fraught, difficult to manage, and at times, derogatory in their nature. This finding comes at the juxtaposition of the wider inclusion agenda UK universities pursue.

Retrieval-Pretrained Transformer: Long-range Language Modeling with Self-retrieval

Abstract Retrieval-augmented language models (LMs) have received much attention recently. However, typically the retriever is not trained jointly as a native component of the LM, but added post-hoc to an already-pretrained LM, which limits the ability of the LM and the retriever to adapt to one another. In this work, we propose the Retrieval-Pretrained Transformer (RPT), an architecture and training procedure for jointly training a retrieval-augmented LM from scratch and applying it to the task of modeling long texts. Given a recently generated text chunk in a long document, the LM computes query representations, which are then used to retrieve earlier chunks in the document, located potentially tens of thousands of tokens before. Information from retrieved chunks is fused into the LM representations to predict the next target chunk. We train the retriever component with a semantic objective, where the goal is to retrieve chunks that increase the probability of the next chunk, according to a reference LM. We evaluate RPT on four long-range language modeling tasks, spanning books, code, and mathematical writing, and demonstrate that RPT improves retrieval quality and subsequently perplexity across the board compared to strong baselines.

Critical Exploration of Eliza’s Use of Restricted Code and Elaborated Code in Pygmalion

This research paper explored the character of Eliza Doolittle in Pygmalion and its movie adaptation, My Fair Lady, through the use of Restricted and Elaborated Code. These concepts examined Eliza’s transformation from a common flower girl to a duchess at the hands of Henry Higgins. The research employed the qualitative method and used Restricted Code and Elaborated Code concepts presented by Basildon Bernstein in his 2003 book, Theoretical Studies towards a Sociology of Language (Class, Codes and Control) for deductive thematic analysis. Given the context, Eliza’s dialogues and the settings in the play and the movie where she delivered those dialogues were also taken into account because the relationship between social class and language could not be ignored in the society Eliza inhabits. The study also focused on her dilemmas, dreams, desires and struggles to determine the outcomes of Eliza’s transformation, whether it was a blessing or a curse. By analysing Eliza’s use of language and interactions, the findings offered insights into the power dynamics of language and social class in shaping individual’s identity.

Reflexive neural circuits and the origin of language and music codes

Conscious activity is grounded on the reflexive self-awareness in sense perception, through which the codes signifying sensual perceptive events operate and constrain human behavior. These codes grow via the creative generation of hypertextual statements. We apply the model of Vladimir Lefebvre (Lefebvre, V.A., 1987, J. Soc. Biol. Struct. 10, 129-175) to reveal the underlying structures on which the perception and creative development of language and music codes are based. According to this model, the reflexive structure of conscious subject is grounded on three thermodynamic cycles united by the control of the basic functional cycle by the second one, and resulting in the internal action that it turn is perceived by the third cycle evaluating this action. In this arrangement, the generative language structures are formed and the frequencies of sounds that form musical phrases and patterns are selected. We discuss the participation of certain neural brain structures and the establishment of reflexive neural circuits in the ad hoc transformation of perceptive signals and show the similarities between the processes of perception and of biological self-maintenance and morphogenesis. We trace the peculiarities of the temporal encoding of emotions in music and musical creativity, as well as the principles of sharing musical information between the performing and the perceiving individuals.

An efficient XOR-free implementation of polar encoder for reconfigurable hardware

— This paper presents a novel approach to implementing an XOR-Free architecture of the non-systematic polar encoder (NSPE) for 5G radio. The optimization of XOR logic for hardware (HW) implementation is essential to reduce delay and power consumption. The proposed architecture for NSPE replaces XOR operations with combinational logical patterns, and some redundant patterns are removed with the help of bit manipulation to make it more efficient. The design infers multiplexers (2:1 or 4:1) and inverters as its functional units, making the design adequate and effective in alleviating HW complexity. The XOR-Free encoder performs the same functionality as the XOR-based conventional encoder. We have written a MATLAB script that generates Verilog hardware description language (HDL) code for fully or partially parallel polar encoders tailored to specific code lengths (N) and degrees of parallelism (M). A comparative analysis of various fully and partially parallel encoders with the XOR-Free algorithm is presented. The implementation results show that the proposed architectures are more efficient in terms of HW cost, power consumption, throughput, and latency.

Efficient Data Exchange between WebAssembly Modules

In the past two decades, there has been a noticeable decoupling of machines and operating systems. In this context, WebAssembly has emerged as a promising alternative to traditional virtual machines. Originally designed for execution in web browsers, it has expanded to executing code in restricted and secure environments, and it stands out for its rapid startup, small footprint, and portability. However, WebAssembly presents significant challenges in data transfer and the management of interactions with the module. Its specification requires each module to have its own memory, resulting in a “share-nothing” architecture. This restriction, combined with the limitations of importing and exporting functions that only handle numerical values, and the absence of an application binary interface (ABI) for sharing more complex data structures, leads to efficiency problems; these are exacerbated by the variety of programming languages that can be compiled and executed in the same environment. To address this inefficiency, the Karmem was designed and developed. It includes a new interface description language (IDL) aimed at facilitating the definition of data, functions, and relationships between modules. Additionally, a proprietary protocol—an optimized ABI for efficient data reading and minimal decoding cost—was created. A code generator capable of producing code for various programming languages was also conceived, ensuring harmonious interaction with the ABI and the foreign function interface. Finally, the compact runtime of Karmem, built atop a WebAssembly runtime, enables communication between modules and shared memory. Results of the experiments conducted show that Karmem represents an innovation in data communication for WASM in multiple environments and demonstrates the ability to overcome challenges of efficiency and interoperability.

Bilingualism of Children in Different Multilingual Contexts

Many parents and professionals believe that learning to speak, read and write in two languages can lead to academic deficiencies due to cognitive overload and the risk of confusion linked to handling two language codes. Therefore, some bilinguals abandon or are tempted to abandon one of the two languages, often the first language, in exchanges with their children, in favor of the language of schooling. However, all recent scientific data tend to show that bilingualism is an asset more than a handicap. Nevertheless, these positive results most often concern English-speaking contexts and are not directly transposable to a French-speaking context. Drawing on the results of our work carried out in Oceania and in other territories, this article will deal with bilingual development and the impact of educational systems that promote the heritage or local languages from primary school. More specifically, the oral language of the bilingual, biliteracy and the effects of cross-linguistic transfer will be addressed. Results from both longitudinal studies in New Caledonia and French Polynesia, confirmed by other studies conducted in Sub-Saharan African, show a positive effect of the bilingual education curriculum on local language (Drehu and Tahitian) skills without having negative effects on French. We demonstrated that the expected effects of cross-linguistic transfer are only possible if the pupils learn to read and write in the two languages (local language and French). Additionally, learning to read in one of those local languages makes it easier to learn to read in French, which has a more opaque writing system.

May the Source Be with You: On ChatGPT, Cybersecurity, and Secure Coding

Software security is an important topic that is gaining more and more attention due to the rising number of publicly known cybersecurity incidents. Previous research has shown that one way to address software security is by means of a serious game, the CyberSecurity Challenges, which are designed to raise awareness of software developers of secure coding guidelines. This game, proven to be very successful in the industry, makes use of an artificial intelligence technique (laddering technique) to implement a chatbot for human–machine interaction. Recent advances in machine learning have led to a breakthrough, with the implementation and release of large language models, now freely available to the public. Such models are trained on a large amount of data and are capable of analyzing and interpreting not only natural language but also source code in different programming languages. With the advent of ChatGPT, and previous state-of-the-art research in secure software development, a natural question arises: to what extent can ChatGPT aid software developers in writing secure software? In this work, we draw on our experience in the industry, and also on extensive previous work to analyze and reflect on how to use ChatGPT to aid secure software development. Towards this, we conduct two experiments with large language models. Our engagements with ChatGPT and our experience in the field allow us to draw conclusions on the advantages, disadvantages, and limitations of the usage of this new technology.

Tutoring as a form of support for the adaptation of foreign students at the university

Introduction. Strengthening globalization processes leads to the expansion of international cooperation in the field of education. This is especially reflected in the growth of international mobility of students in higher education institutions, in particular, Russian universities. And if the first students of the beginning of the large waves of mass educational migration were proficient in the Russian language and were familiar with Russian culture, then the students who followed them were much less proficient in the language and cultural code. And, since the internationalization of education remains a prerequisite for the existence of universities, and long-term adaptation of foreign students to learning conditions worsens the quality of their training, there is a need to organize their psychological and pedagogical support in the first years of study. The purpose of the article: to specify the main problems of adaptation of foreign students to the conditions of study in Russian universities and to justify the need to organize their tutor support. Materials and methods. The study involved 556 international students of the first and second years of study of three Tatarstan universities (Kazan National Research Technological University (KNRTU-KICT), (Kazan); Kazan National Research Technical University named after A. N. Tupolev (KNRTU-KAI), (Kazan); Elabuga Institute of the Kazan (Volga Region) Federal University (EI KFU) (Elabuga). Research results. The results of the study confirmed the presence of adaptation problems among foreign students. The main ones are difficulties with the Russian language (41%) and acclimatization (21%). It takes 46% of students about a year to get used to the learning conditions, and 23% – more than a year. The main learning difficulties they identify are awkwardness when speaking in public in front of an audience (23%) and teachers’ underestimation of their educational results (20%). More than half of the students see older compatriots as a source of help in overcoming cultural and educational barriers; The help of a supervisor turned out to be effective only for 15% of respondents. Taking into account the results obtained, it is proposed to organize the training of future tutors from among foreign senior students who have undergone appropriate psychological and pedagogical training. Conclusion. While maintaining the institute of curatorship, it is necessary to create a tutoring institute in universities that train foreign students. The main functions of tutors, which are absent from the curators of academic groups, should be to help foreign students overcome barriers to socio-cultural adaptation and insufficient proficiency in Russian, promote the acceptance of foreigners by their new environment, help in building effective interaction with the university administration and teachers, study students' personalities, monitor the results of their educational activities, help in professional identification, monitoring of newly emerging difficulties of students, strengthening cooperation between Russian and foreign students.

Deeper, Sharper, Faster: Application of Efficient Transformer to Galaxy Image Restoration

The Transformer architecture has revolutionized the field of deep learning over the past several years in diverse areas, including natural language processing, code generation, image recognition, and time-series forecasting. We propose to apply Zamir et al.'s efficient transformer to perform deconvolution and denoising to enhance astronomical images. We conducted experiments using pairs of high-quality images and their degraded versions, and our deep learning model demonstrates exceptional restoration of photometric, structural, and morphological information. When compared with the ground-truth James Webb Space Telescope images, the enhanced versions of our Hubble Space Telescope–quality images reduce the scatter of isophotal photometry, Sérsic index, and half-light radius by factors of 4.4, 3.6, and 4.7, respectively, with Pearson correlation coefficients approaching unity. The performance is observed to degrade when input images exhibit correlated noise, point-like sources, and artifacts. We anticipate that this deep learning model will prove valuable for a number of scientific applications, including precision photometry, morphological analysis, and shear calibration.

Code Mixing Used by Yuki Kato in Bicara Cinta on Puella Id Youtube Channel

Code-mixing gives an understanding of a mix of two languages or more by the multilingual. This study aims at defining the types and the factors causing the code-mixing used by Yuki Kato in a Youtube video. A mixed method was applied, with both qualitative and quantitative methods used in the study. The data is all of the words, phrases, or sentences that include the use of mixed codes in English and Indonesian Language, which was collected from one episode of Bicara Cinta on the PUELLA ID Youtube Channel. The code-mixing theory from Hoffman (1991) was used to analyze the data, including the types of code-mixing and the factors that caused the code-mixing. The analysis has identified 93 data on the use of code-mixing by Yuki Kato, including three types of code-mixing which are intra-sentential with 80 data found (86,02%), intra-lexical with 10 data found (10, 75%), and involving a change of pronunciation with 3 data found (3,23%). Six out of seven factors were applied in the code-mixing by Yuki Kato. The code-mixing produced by Yuki Kato is caused by several factors, those are talking about particular topic with 46 data found (49,46%), being emphatic about something with 35 data found (37,63%), intention of clarifying the speech content for the interlocutor with 7 data found (7,53%), repetition used for clarification with 2 data found (2,15%), interjection with 2 data found (2,15%), and quoting somebody else with only 1 data found (1,08%). There is no code-mixing caused by expressing a group identity. Keywords: Code-mixing, Codes, Youtube Channel

Analisis Campur Kode Bahasa Batak Pada Saat Pembelajaran di Kelas X SMA YAPIM Merek Tahun Ajaran 2023/2024

The research was carried out with the title "Analysis of Batak Language Code Mixing When Learning Indonesian in Class The use of code mixing is the Batak regional language. The aim of this research is to determine the main causes of the use of code mixing during learning. This research methodology is a qualitative descriptive method. Data was obtained from class X students of YAPIM Brand High School and the results of interviews from Indonesian language teachers. Data collection techniques were carried out by interviewing, recording and taking notes. The results of this research are forms of code mixing in teacher and student conversations in Indonesian language learning in class X SMA YAPIM Brand, namely: a. Use of Toba Batak language code mixing, b. There are 76 code-mixed data, the forms of code-mixing use are 13 code-mixes of word insertion, 17 phrase insertions, 46 clause insertions, word and sentence insertions. Factors causing code mixing are: a. Students' proficiency in speaking two languages, including the Toba Batak regional language and Indonesian, b. Social environment, c. Contextual situation. However, Indonesian language interference is not eliminated by the presence of the regional language, it's just that students sometimes focus more on using the regional language instead of being in context when speaking with fellow students.

The Effects of Sheng’ Vocabulary Usage in the Learning of Kiswahili Grammar in Public Secondary Schools in Kenya

Purpose: Sheng’ is a linguistic code based primarily on the Kiswahili structure and grammar with the lexicon drawn from Kiswahili, English and the various ethnic languages of Kenya; that are mostly spoken in towns and other urban areas. The purpose of this paper was to examine the effects of Sheng’ vocabulary on the learning of Kiswahili grammar in public secondary schools in Kenya. Methodology: Krejcie & Morgan sampling table was used to sample the schools. Stratified random sampling, simple random sampling and purposive strategy were used to sample the public secondary schools to participate. Krejcie and Morgan sampling table was used to sample 55 teachers of Kiswahili and 360 students to take part in the study. Data was collected using observation schedules, focused group discussion guides and a writing task for students. Questionnaires and interview guides were used for teachers of Kiswahili. Qualitative data collected was analyzed thematically to generate information that was summarized in graphics and discussed in narrative form. Quantitative data was analyzed with the help of Statistical package for social Studies. Findings: The findings of the study further revealed that Sheng’ vocabulary influenced written Kiswahili grammar as learners wrote Sheng’ words whereas they were expected to write standard Kiswahili. Unique Contribution to Theory, Policy, and Practice: School administrators should come up with a school-based language policy so as to control learners’ language environment. The ministry of education should encourage the culture of drama, music and debate competition among schools. The Kenya Institute of Curriculum Development should also ensure language use is stipulated in Kiswahili curriculum and Sheng’ be considered as an independent language code in the country.

Design of an efficient Transformer-XL model for enhanced pseudo code to Python code conversion

The landscape of programming has long been challenged by the task of transforming pseudo code into executable Python code, a process traditionally marred by its labor-intensive nature and the necessity for a deep understanding of both logical frameworks and programming languages. Existing methodologies often grapple with limitations in handling variable-length sequences and maintaining context over extended textual data. Addressing these challenges, this study introduces an innovative approach utilizing the Transformer-XL model, a significant advancement in the domain of deep learning. The Transformer-XL architecture, an evolution of the standard Transformer, adeptly processes variable-length sequences and captures extensive contextual dependencies, thereby surpassing its predecessors in handling natural language processing (NLP) and code synthesis tasks. The proposed model employs a comprehensive process involving data preprocessing, model input encoding, a self-attention mechanism, contextual encoding, language modeling, and a meticulous decoding process, followed by post-processing. The implications of this work are far-reaching, offering a substantial leap in the automation of code conversion. As the field of NLP and deep learning continues to evolve, the Transformer-XL based model is poised to become an indispensable tool in the realm of programming, setting a new benchmark for automated code synthesis.

Harmonizing ML and Databases: A Symphony of Data (VLDB 2024 Keynote)

Large language models (LLMs) are rapidly transforming the landscape of computing and daily life, demonstrating immense potential across diverse applications like natural language processing, machine translation, and code generation. This talk delves into the impact of LLMs on database research. Specifically, we'll examine how LLMs are fueling innovation in natural language interfaces for data interaction, highlighting current limitations and advocating for semantic data models and enhanced context to improve the accuracy of these solutions. Drawing inspiration from LLMs, we'll introduce a novel paradigm for database cost modeling, leveraging pre-trained models and fine-tuning techniques. We'll share our early-stage prototype, initial results, and outline a research roadmap highlighting numerous exciting challenges in this evolving field.

Perceived impact of generative AI on assessments: Comparing educator and student perspectives in Australia, Cyprus, and the United States

The growing use of generative AI tools built on large language models (LLMs) calls the sustainability of traditional assessment practices into question. Tools like OpenAI's ChatGPT can generate eloquent essays on any topic and in any language, write code in various programming languages, and ace most standardized tests, all within seconds. We conducted an international survey of educators and students in higher education to understand and compare their perspectives on the impact of generative AI across various assessment scenarios, building on an established framework for examining the quality of online assessments along six dimensions. Across three universities, 680 students and 87 educators, who moderately use generative AI, consider essay and coding assessments to be most impacted. Educators strongly prefer assessments that are adapted to assume the use of AI and encourage critical thinking, while students' reactions are mixed, in part due to concerns about a loss of creativity. The findings show the importance of engaging educators and students in assessment reform efforts to focus on the process of learning over its outputs, alongside higher-order thinking and authentic applications.

Fairness in Classifying and Grouping Health Equity Information.

This paper explores the balance between fairness and performance in machine learning classification, predicting the likelihood of a patient receiving anti-microbial treatment using structured data in community nursing wound care electronic health records. The data includes two important predictors (gender and language) of the social determinants of health, which we used to evaluate the fairness of the classifiers. At the same time, the impact of various groupings of language codes on classifiers' performance and fairness is analyzed. Most common statistical learning-based classifiers are evaluated. The findings indicate that while K-Nearest Neighbors offers the best fairness metrics among different grouping settings, the performance of all classifiers is generally consistent across different language code groupings. Also, grouping more variables tends to improve the fairness metrics over all classifiers while maintaining their performance.