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This paper aims to disentangle Chinese-English-rich resources linguistic and speaker timbre features, achieving cross-lingual speaker transfer for Cambodian.

This study introduces a novel approach: the construction of a cross-lingual feature disentangler coupled with the integration of time-frequency attention adaptive normalization to proficiently convert Cambodian speaker timbre into Chinese-English without altering the underlying Cambodian speech content.

Considering the limited availability of multi-speaker corpora in Cambodia, conventional methods have demonstrated subpar performance in Cambodian speaker voice transfer.

The originality of this study lies in the effectiveness of the disentanglement process and precise control over speaker timbre feature transfer.

In times of crisis, such as the Covid-19 global pandemic, journalists who write about biomedical information must have the strategic aim to be clearly and easily understood by everyone. In this study, we assume that journalistic discourse could benefit from language redundancy to improve clarity and simplicity aimed at science popularization. The concept of language redundancy is theoretically discussed with the support of discourse analysis and information theory. The methodology adopted is a corpus-based qualitative approach. Two corpora samples with Brazilian Portuguese (BP) texts on Covid-19 were collected. One with texts from a monthly science digital magazine called Pesquisa FAPESP aimed at students and researchers for scientific information dissemination and the other with popular language texts from a news Portal G1 (Rede Globo) aimed at unspecified and/or non-specialized readers. The materials were filtered with two descriptors: “vaccine” and “test.” Preliminary analysis of examples from these materials revealed two categories of redundancy: paraphrastic and polysemic. Paraphrastic redundancy is based on concomitant language reformulation of words, sentences, text excerpts, or even larger units. Polysemic redundancy does not easily show material evidence, but is based on cognitively predictable semantic association in socio-cultural domains. Both kinds of redundancy contribute, each in their own way, to improving text readability for science popularization in Brazil.

This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice.

This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs.

To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required.

This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.

This study aims to examine how teacher candidates (TCs) in Türkiye and the USA navigate their intercultural communication skills in a telecollaboration project.

Forty-eight TCs participated (26 in Türkiye and 22 in the USA) in the study. TCs discussed critical issues in multicultural education on an online learning platform for six weeks. Their discussions were analyzed using content and discourse analysis.

The findings indicated that TCs approached the telecollaborative space as a translingual contact zone and positioned themselves and their interlocutors in the discourse by using the personal pronouns; I, we, you and they. When they positioned themselves using we (people in Türkiye/USA), they spoke on behalf of everyone included in the scope of we. Their interlocutors responded to these positionings either by accepting this positioning and responding with a parallel positioning or by engaging in translingual negotiation strategies to revise the scope of we and sharing some differences/nuances in beliefs and practices in their community.

When TCs talk about their culture and community in a singular manner using we, they frame them as the same across every member in that community. When they ask questions to each other using you, the framing of the questions prime the respondents to sometimes relay their own specific experiences as the norm or consider experiences from different points of view through translingual negotiation strategies. A singular approach to culture(s) may affect the marginalized communities the most because they are lost in this representation, and their experiences and voices are not integrated in the narratives or integrated with stereotypical representation.

Teachers and teacher educators should first pay attention to their language choices, especially use of pronouns, which may communicate inclusion or exclusion in intercultural conversations. Next, they should prepare their students to adopt and practice language choices that communicate respect for cultural diversity and are inclusive of marginalized populations.

Speakers’ pronoun use includes identity construction in discourse by drawing borders around and between communities and cultures with generalization and particularity, and by patrolling those borders to decide who is included and excluded. As a response, interlocutors use pronouns either to acknowledge those borders and respond with corresponding ones from their own context or negotiate alternative representations or further investigate for particularity or complexity. In short, pronouns could lead the direction of intercultural conversations toward criticality and complexity or otherwise, and might be reasons where there are breakdowns in communication or to fix those breakdowns.

This study shows that translingual negotiation strategies have explanatory power to examine how speakers from different language backgrounds negotiate second and third order positionings in the telecollaborative space.

This study aims to understand the interaction between tax fairness perceptions, equitable tax burden distribution and tax compliance within Morocco’s unique socio-economic context, with the goal of uncovering strategies to enhance tax compliance.

Using the Delphi method, this study engaged tax experts in the Moroccan context to explore the impact of taxpayers’ perception of fairness, tax rates and tax burden on compliance. Their responses were gathered and analyzed with the aid of IRaMuTeQ software, which helped the authors identify themes relevant to the research question.

The preliminary results indicate a positive correlation between perceptions of tax fairness and compliance behavior, corroborating earlier studies conducted in different contexts. Notably, a substantial majority of Moroccan taxpayers perceive the current tax system as inequitable, deeming tax rates too high and the tax burden unfairly distributed among various taxpayer categories. This perception potentially influences their voluntary tax compliance behavior.

The findings have significant policy implications for the Moroccan Government and stakeholders. They suggest that by improving tax fairness, particularly by aligning tax assessment and payment modalities for employees, civil servants and small to medium enterprises, policymakers can encourage higher voluntary tax compliance, thereby potentially enhancing the efficiency of the Moroccan tax system.

This study adds to the existing body of knowledge by exploring the dynamics of tax fairness and compliance behavior in Morocco, a context which has been significantly understudied.

Digital transformation (DT) is a major challenge for incumbent organisations, as research on this phenomenon has revealed a high failure rate. Given this consideration, this paper reviews the literature on DT in incumbent organisations to identify the main themes and research directions to be undertaken.

The authors adopt a systematic literature review (SLR) and computational literature review (CLR) employing a machine learning algorithm for topic modelling (LDA) to surface the themes discussed in 103 peer-reviewed studies published between 2010 and 2022 in a multidisciplinary article sample.

The authors identify and discuss the five main themes emerging from the studies, offering the state-of-the-art of DT in established firms' literature. The authors find that the most discussed topics revolve around the DT of healthcare, the process of renewal and change, the project management, the changes in value performances and capabilities and the consequences on the products of DT. Accordingly, the authors identify the topics overlooked by literature that future studies could tackle, which concern sustainability and contextualisation of the DT phenomenon.

The authors further propose managerial insights which equip managers with a revolutionary mindset that is not constraining but, rather, integration-seeking. DT is not only about technology (Tabrizi B et al., 2019). Successful DT initiatives require managerial capabilities that foster a sustainable departure from the current organising logic (Markus, 2004). This study pinpoints and prioritises the role that paradox-informed thinking can have to sustain an effective digital mindset (Eden et al., 2018) that allows for the building of momentum in DT initiatives and facilitates the renewal process. Indeed, managers lagging behind DT could shift from an “either-or” solutions mindset where one pole is preferred over the other (e.g. digital or physical) to embracing a “both-and-with” thinking balancing between poles (e.g. digital and physical) to successfully fuse the digital and the legacy (Lewis and Smith, 2022b; Smith, Lewis and Edmondson, 2022), enact the renewal, and build and maintain momentum for DTs. The outcomes of adopting a paradox mindset in managerial practice are enabling learning and creativity, fostering flexibility and resilience and, finally, unleashing human potential (Lewis and Smith, 2014).

The authors propose insight that will equip managers with a mindset that will allow DT to fail less often than current reported rates, which failure may imply potential organisational collapse, financial bankrupt and social crisis.

The authors offer a multidisciplinary review of the DT complementing existing reviews due to the focus on the organisational context of established organisations. Moreover, the authors advance paradoxical thinking as a novel lens through which to study DT in incumbent organisations by proposing an array of potential research questions and new avenues for research. Finally, the authors offer insights for managers to help them thrive in DT by adopting a paradoxical mindset.

Effective translation has become essential for seamless cross-cultural communication in an era of global interconnectedness. Translation management systems (TMS) have redefined the translation landscape, revolutionizing project management and execution. This study examines the attitudes of translation agencies and professional translators towards integrating and utilizing TMS, with a specific focus on Saudi Arabia.

The study's design was based on a thorough mixed-methods strategy that purposefully combined quantitative and qualitative procedures to create an array of findings. Through a survey involving 35 participants (both project managers and professional translators) and a series of interviews, this research explores the adoption of TMS, perceived benefits, influencing factors and future considerations. This integrated approach sought to investigate the nuanced perceptions of Saudi translation companies and expert translators about TMS. By combining the strengths of quantitative data's broad scopes and qualitative insights' depth, this mixed-methods approach sought to overcome the limitations of each method, ultimately resulting in a holistic understanding of the multifaceted factors shaping attitudes within Saudi Arabia's unique translation landscape.

Based on questionnaires and interviews, the study shows that 80% of participants were familiar with TMS, and 57% had adopted it in their work. Benefits included enhanced project efficiency, collaboration and quality assurance. Factors influencing adoption encompassed cost, compatibility and resistance to change. The study further delved into participants' demographic profiles and years of experience, with a notable concentration in the 6–10 years range. TMS adoption was linked to improved translation processes, and participants expressed interest in AI integration and mobile compatibility. Deployment models favored cloud-based solutions, and compliance with industry standards was deemed vital. The findings underscore the evolving nature of TMS adoption in Saudi Arabia, with diverse attitudes shaped by cultural influences, technological compatibility and awareness.

This research provides a holistic and profound perspective on the integration of TMS, fostering a more comprehensive understanding of the opportunities, obstacles and potential pathways to success. As the translation landscape continues to evolve, the findings from this study will serve as a valuable compass guiding practitioners and researchers towards effectively harnessing the power of technology for enhanced translation outcomes.

Proponents of micro-credentials envision them as vehicles for upskilling or re-skilling individuals. The study examines how integrating micro-credentials in the higher education ecosystem enhances employability. It aims to offer insights from the perspective of stakeholders who may benefit from these credentials at an institutional or individual level.

Online in-depth interviews are conducted with 65 participants from India, Nigeria, the United Arab Emirates and the United Kingdom to explore how micro-credentials can be a valuable addition to the higher education ecosystem. A multi-stakeholder approach is adopted to collect data.

The analysis highlights two possible methods of integrating micro-credentials into the higher education ecosystem. First, micro-credentials-driven courses can be offered using a blended approach that provides a flexible learning path. Second, there is also the possibility of wide-scale integration of micro-credentials as an outcome of standalone online programs. However, the effectiveness of such programs is driven by enablers like student profiles, standardization and the dynamics of the labor market. Finally, the study stipulates that micro-credentials can enhance employability.

The study's findings suggest that, for successful integration of micro-credentials, an operational understanding of micro-credentials, their enablers and strategic deliberation are critical in higher education. Institutions must identify the determinants, address technological limitations and select a suitable delivery mode to accelerate integration. However, micro-credentials can augment employability, considering the increasing emphasis on lifelong learning. An overview of the findings is presented through a comprehensive framework.

This study provides an overview of the application of sentiment analysis (SA) in exploring social movements (SMs). It also compares different models for a SA task of Algerian Arabic tweets related to early days of the Algerian SM, called Hirak.

Related tweets were retrieved using relevant hashtags followed by multiple data cleaning procedures. Foundational machine learning methods such as Naive Bayes, Support Vector Machine, Logistic Regression (LR) and Decision Tree were implemented. For each classifier, two feature extraction techniques were used and compared, namely Bag of Words and Term Frequency–Inverse Document Frequency. Moreover, three fine-tuned pretrained transformers AraBERT and DziriBERT and the multilingual transformer XLM-R were used for the comparison.

The findings of this paper emphasize the vital role social media played during the Hirak. Results revealed that most individuals had a positive attitude toward the Hirak. Moreover, the presented experiments provided important insights into the possible use of both basic machine learning and transfer learning models to analyze SA of Algerian text datasets. When comparing machine learning models with transformers in terms of accuracy, precision, recall and F1-score, the results are fairly similar, with LR outperforming all models with a 68 per cent accuracy rate.

At the time of writing, the Algerian SM was not thoroughly investigated or discussed in the Computer Science literature. This analysis makes a limited but unique contribution to understanding the Algerian Hirak using artificial intelligence. This study proposes what it considers to be a unique basis for comprehending this event with the goal of generating a foundation for future studies by comparing different SA techniques on a low-resource language.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.