Metaphor and Corpus Linguistics

Growing interest in metaphor over recent decades has led to an increasing need for guidance on how to identify and analyze this linguistic phenomenon in corpus data. While Cognitive Linguistics offers various perspectives on interpreting metaphorical meanings, it lacks the means to document how, where, and with what frequency these meanings are realised textually. Corpus Linguistics can address these issues, but only once one crucial problem has been addressed: how to find metaphorical meanings in data that is typically searched via word forms. This contribution starts with an overview of conceptual and linguistic metaphor, and how metaphors are identified in language. It then dedicates space to issues of specific interest to corpus-based studies of metaphor, particularly regarding how to find metaphors in corpus data. The first method described is the lexicographical approach, which starts with a word form and profiles its lexical environments in order to separate out distinct meanings, metaphorical or otherwise, and to schematize the regular lexicogrammatical patternings associated with each. The other methods focus on how to identify/extract candidate metaphors from corpus data. After identification/extraction, the data has to be examined to verify that each instantiation of a word is indeed metaphorical, following the same principles as in the lexicographical approach. Corpus studies of metaphor can enhance our understanding of what metaphor achieves in text by analyzing the lexicogrammatical patterns, the frequencies, and the distribution of metaphor in natural language data

Single- and multi-task linear models for ATMs fault classification in human-centered predictive maintenance

The recirculator, a complex component within Automated Teller Machines (ATMs) responsible for handling banknotes, poses a challenging task for fault diagnosis due to its intricate nature, which renders it impractical to integrate dedicated sensors and potential multiple faults. This paper presents advanced single-task (STL-LR) and multi-task (MTL-LR) logistic regression models explicitly designed for capturing specific and similar discriminative patterns of multiple faults. Our approach focuses on maintaining the expert human operator at the center of the model checking and development process (human-in-the-loop approach). This objective has been achieved by including training data extracted from the intervention management platform, which collects the annotations of human operators. By leveraging this data, our STL and MTL models enhance generalization performance, especially in cases where discrepancies exist between machine-reported errors and technician-observed anomalies. The results illustrate the potential of the STL-LR and MTL-LR models as the main core of PdM DSS to aid technicians in accurately pinpointing fault-prone areas. This research contributes to Industry 5.0 by presenting a novel predictive maintenance approach that evolves task-specific learning to the generalization advantages of MTL. This evolution holds promise for fostering more efficient and effective maintenance strategies in complex equipment environments

Nostalgia. Una piccola etica

Questo volume intende indagare le implicazioni etiche della nostalgia e valutarne le potenzialità e i limiti dal punto di vista della vita morale, che qualifica la dimensione esistenziale in direzione del bene. Il tema della nostalgia è oggi particolarmente frequentato: esso occupa non solo filosofi, ma anche sociologi, teorici e scienziati politici, antropologi, psicologi, studiosi dei fenomeni e dei processi culturali. Di volta in volta inquadrata come emozione, sentimento, passione, questa modalità di vivere il tempo che passa e, in generale, il cambiamento viene studiata più per i suoi effetti sulle vicende politiche, le strategie di marketing capaci di rendere la nostalgia una merce quantificabile, il benessere psicologico individuale, che per le sue implicazioni etico-filosofiche, quasi a segnalarne una scarsa pregnanza, un’irrilevanza rispetto al modo umano di vivere e abitare il mondo, come se si trattasse di un’emozione effimera o di un orpello estetico dell’esperienza umana. Difficilmente si trovano indicazioni sulla natura della nostalgia in relazione alla temporalità, alle figure del desiderio, alla relazione tra autonomia e nostalgia, che spesso appaiono come due forze vettoriali uguali per intensità e opposte per direzione. Per tale ragione, in questo studio si metterà a tema la dimensione etico-pratica della nostalgia, il suo legame con i vissuti che implicano mutamento; tale dimensione è sempre anche scivolamento immaginativo nel “non più presente”, nell’“assente”, sia esso temporale o spaziale, e ha ricadute eticamente significative nella vita personale e nei legami comunitari, entro l’orizzonte di un “noi” da costruire

Abitare il futuro tecno-digitale: tra spinte, ambivalenze, incertezze

Il contributo mette in luce la portata della rivoluzione digitale nella sua duplicità di pharmakon - rimedio e veleno, in particolare rispetto alle questioni etiche da affrontare perché la trasformazione non comporti una deprivazione dell'esperienza e, complessivamente, dell'umano e del mondo, ma possa invece essere un contributo in direzione di un miglioramento dell'esistenza

Raḍwā ʿĀšūr reads Ġassān Kanafānī’s fiction in al-Ṭarīq ilā al-ḫayma al-uḫrā (‘The way to the other tent,’ 1981) and al-Ṭanṭūriyya (‘The woman from Tantoura,’ 2010)

This article aims to discuss how Raḍwā ʿĀšūr (1946-2014), an Egyptian academic, novelist and activist, critically analyzed and subsequently intertextually engaged with the fiction of Palestinian author Ġassān Kanafānī in two works: the essay alṬarīq ilā al-ḫayma al-uḫrā (‘The Way to the Other Tent,’ 1981) and her novel alṬanṭūriyya (2010; translation: ‘The Woman from Tantoura,’ 2014). I will highlight how the Palestinian author was a reference figure for Raḍwā ʿĀšūr in developing self-exploration and engaging with writing herself. ʿĀšūr’s ThirdWorldist and Marxist reading of Kanafānī allowed her to establish an intertextual dialogue with the iconic author of the Palestinian liberation struggle, which reemerges in her concept of writing and in certain narrative choices within AlṬanṭūriyya (2010), which the author herself defines as her “Palestinian Novel”. On the other hand, I will highlight how the critical essay on Kanafani’s works also presents radical critiques of Kanafānī’s works. The intertextual dialogue between the two authors will be emphasized through Rancière’s concept of “politics of literature” (Rancière 2011), which will be used to shed light on both ʿĀšūr understanding and criticism of literary modernism in Kanafānī’s and her generational novel al-Ṭanṭūriyya

Generative AI in Global Virtual Teams: A Task-Technology Fit Approach

The study investigates the impact of generative AI (GAI) on team performance and individual peer evaluations in global virtual teams completing international business projects. Using a large international sample (N=3,193 people), the study applies the task-technology fit theory to examine how different GAI applications affect team report grades and peer assessments. The findings reveal that using GAI for proofreading and learning enhances both team and individual performance, while recreational use negatively impacts performance. The study highlights the importance of aligning GAI usage with specific task requirements to enhance both individual and team performance in academic settings. It emphasizes that simply using GAI does not guarantee better results, suggesting the need to educate students on how to effectively utilize GAI in a manner consistent with task-technology fit principles and good academic practices

Alien Intelligences and Ancient Cultures: AI in Chinese Language Education

Artificial intelligence (AI) is rapidly revolutionizing many fields, including the teaching of the Chinese language and culture. As biologist E.O. Wilson pointed out, we live in an era dominated by “Paleolithic emotions, medieval institutions, and godlike technology.” This pivotal moment has the potential to transform education and other fields profoundly. Indeed, as some analysts note, the last time humanity invested so heavily in a single technology was during the Apollo missions, underscoring the historical significance of this technological advancement. This raises a critical question: how can AI be effectively integrated into education while preserving the humanity inherent in teaching? In a context where, as essayist Tom Nichols observes, there is a risk that AI could undermine human expertise, technology might hinder rather than enhance the educational process. The teaching of the Chinese language and culture offers a compelling case study for AI’s application. AI has the potential to revolutionize Chinese instruction by providing advanced tools that enhance both linguistic and cultural learning. Through personalized learning pathways, digital tutors can dynamically adapt to the specific needs of individual students, analyze errors, improve tonal pronunciation with real-time feedback, and propose tailored exercises. Moreover, AI can facilitate immediate access to authentic materials and insights about Chinese culture and history. However, the widespread adoption of algorithms, automatic translators, and digital tutors raises important ethical questions: what are the implications of using AI in a field deeply rooted in the transmission of cultural values? Can AI truly replicate a teacher’s ability to interpret and convey cultural nuances? Furthermore, given that the cognitive origins of AI differ significantly from human cognition, we must also consider the possibility of an “alien-cultural” intelligence interacting with humanity in ways that remain elusive. This reflection focuses on the tension between AI’s potential and its limitations in education. How can we ensure that these technologies complement, rather than jeopardize, the teacher’s central role? It is imperative to explore educational strategies that, while leveraging AI’s capabilities, preserve the teacher’s critical and interpretive functions, ensuring a humanistic dimension in education. When properly integrated, AI could open new horizons for language teaching. Nevertheless, a critical evaluation of the risks associated with its indiscriminate use remains essential

Optimizing Performance: Task-Technology Fit of Generative AI in Global Virtual Team Student Projects

The study investigates the impact of generative AI (GAI) on team performance and individual peer evaluations in global virtual teams completing international business projects. Using a large international sample (N=3,193 people), the study applies the task-technology fit theory to examine how different GAI applications affect team report grades and peer assessments. The findings reveal that using GAI for proofreading and learning enhances both team and individual performance, while recreational use negatively impacts performance. The study highlights the importance of aligning GAI usage with specific task requirements to enhance both individual and team performance in academic settings. It emphasizes that simply using GAI does not guarantee better results, suggesting the need to educate students on how to effectively utilize GAI in a manner consistent with task-technology fit principles and good academic practices

Unveiling emotional reaction and satisfaction in e-learning with face tracking

This study explores the user experience (UX) of students on e-learning platforms, emphasizing emotional expression and satisfaction. It enhances existing research by applying usability tests and surveys to assess the emotional impact of online learning. The study’s novelty lies in its focus on emotional expression, which, along with cognitive issues and engagement, plays a vital role in shaping the quality of learning outcomes. It compares the effectiveness of a real-timeface and eye recognition method (MIORA) with a retrospective questionnaire (SAM) for measuring emotional responses.Results indicate that the real-time method is more accurate and reliable, capturing transient emotional states with machine learning. Unlike retrospective assessments, which are prone to memory biases, the real-time method provides immediate, objective data for dynamic understanding and instant feedback. These insights are crucial for improving e-learning platform design, enhancing user engagement, and enabling real-time adaptations, leading to more engaging and rewarding online learning environments