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Linguistic landscape
No full textThis chapter introduces linguistic landscape as the perceived totality of languages on display in space and place. This revised definition seeks to capture the multimodal expansion of the subject in both public and private, offline and online spaces. The chapter begins by tracing the development of the term back to its origin in language policy and planning research in the early 1990s, underlining the emphasis on subjective perception in its original conceptualisation. It then summarises the exponential growth of the field over the past few decades into two main thematic strands: 1. linguistic landscape as an approach to multilingualism and 2. linguistic landscape as a resource for place-making, illustrated with empirical studies from a wide variety of geographic and social contexts. The chapter concludes with a summary of linguistic landscape's interdisciplinary connections with diverse fields such as multilingual education, urban studies, socio-cultural geography, visual art and design, gender studies, and public health, demonstrating its enduring relevance not only as a representation of linguistic diversity but also a form of public discourse
Walking and falling: using robot simulations to model the role of errors in infant walking
Get PDFWhat is the optimal penalty for errors in infant skill learning? Behavioral analyses indicate that errors are frequent but trivial as infants acquire foundational skills. In learning to walk, for example, falling is commonplace but appears to incur only a negligible penalty. Behavioral data, however, cannot reveal whether a low penalty for falling is beneficial for learning to walk. Here, we used a simulated bipedal robot as an embodied model to test the optimal penalty for errors in learning to walk. We trained the robot to walk using 12,500 independent simulations on walking paths produced by infants during free play and systematically varied the penalty for falling—a level of precision, control, and magnitude impossible with real infants.When trained with lower penalties for falling, the robot learned to walk farther and better on familiar, trained paths and better generalized its learning to novel, untrained paths. Indeed, zero penalty for errors led to the best performance for both learning and generalization. Moreover, the beneficial effects of a low penalty were stronger for generalization than for learning. Robot simulations corroborate prior behavioral data and suggest that a low penalty for errors helps infants learn foundational skills (e.g., walking, talking, and social interactions) that require immense flexibility, creativity, and adaptability
Extended reality in STEM: a modernised educational tool for children
Get PDFFor many years, researchers argued that Extended Reality (XR)—an umbrella term that refers to all immersive technologies—has the potential to revolutionise early years education by providing new and innovative ways for children to learn. Specifically, XR is suggested as a powerful tool in Science, Technology, Engineering, and Math (STEM) education. By blending the physical with the virtual, the creation of unique multisensory environments with XR has been shown to deliver hands-on learning experiences that exceed outcomes from traditional teaching. Nevertheless, the high cost, limited content, technical challenges, and lack of teacher training constrain the prevalence of XR in STEM education. In this article, we discuss the key strengths and limits of XR in education, review recent advances in its use in STEM disciplines, and point to future directions for how XR should be integrated into the school curriculum to facilitate children’s outcomes in STEM education
Context-aware code generation with synchronous bidirectional decoder
No full textCode generation aims to map natural language descriptions to code snippets. Recent approaches using sequence-to-tree models have shown promising results. However, they generally adopt an autoregressive way to predict the next token based on previous ones and do not consider potential future tokens. To address this issue, we propose Contextor, a novel context-sensitive model employing a bidirectional decoder to generate tokens in two different orders synchronously and interactively. Specifically, we employ two decoders to generate two sequences of different traversals and share their context knowledge via the attention mechanism. As a result, our model can synthesize both previous and future information simultaneously. To alleviate the information leakage problem caused by the teacher-forcing training strategy and bidirectional decoding, we propose an adapted scheduled sampling technique to prevent the decoders from contacting the actual label. Furthermore, Contextor also features a bidirectional beam search algorithm to better interact with both decoders. Experimental results demonstrate that our approach outperforms the state-of-the-art baselines
Polite littering is a rubbish problem - Here's why the British approach to tackling clean ups is not working
No full textSome littering is conscious, some is created unintentionally. Tackling the reason it was created could make clean-up tactics more effective
John Maiden, Age of the Spirit: Charismatic Renewal, the Anglo-World, and Global Christianity, 1945-1980 (2023)
No full textAn optimal decision of fresh products cold chain considering freshness and carbon emission reduction
Get PDFFresh products cold chain has the characteristics of high energy consumption and high carbon emission. Based on the policy background of carbon cap-and-trade, cold chain decision-makers need to comprehensively consider the relationship between economic and ecological benefits. Therefore, this paper constructs a fresh products cold chain optimal decision game model considering retailers’ fresh-keeping efforts and suppliers’ carbon emission reduction efforts, and compares the optimal decision-making of cold chain under different carbon constraints. Finally, the impact of consumer freshness preference and consumer low-carbon preference on retailers’ fresh-keeping efforts, suppliers’ carbon emission reduction efforts and cold chain system profits are numerically analyzed. The results show that the profits of the fresh products cold chain system under the carbon cap-and-trade policy are higher than those without carbon constraints; Raising the carbon trading price can effectively improve the fresh-keeping level, carbon emission reduction level and system profit of the cold chain of fresh products; The improvement of consumers’ freshness preference and low-carbon preference can increase the profits of the cold chain system, reduce carbon emissions and promote the sustainable development of the cold chain to a certain extent
Taxonomy of the Nicotiana megalosiphon species complex (Solanaceae; Nicotiana section Suaveolentes): analyses of RADseq data identifies a new cryptic species
Get PDFThe Nicotiana megalosiphon Van Heurck & Müll.Arg. species complex has been shown to be composed of several morphologically cryptic species similar to N. simulans N.T.Burb. Using phylogenetic and population genetic approaches (maximum likelihood, co-ancestry, admixture proportions, Bayesian species delimitation and coalescent methods), we demonstrate that there is an additional undescribed species in this complex. The species limits of N. latifolia M.W.Chase & Christenh., N. latzii M.W.Chase, R.W.Jobson & Christenh., N. megalosiphon, N. sessilifolia (P.Horton) M.W.Chase & Christenh. and N. simulans, previously circumscribed based solely on a phylogenetic approach, are confirmed in the new analyses and a new species, N. palssonae M.W.Chase & Christenh., is described. A map of species distributions and a key to the species of the N. megalosiphon species complex are provided
Prioritizing user requirements for digital products using explainable artificial intelligence: a data-driven analysis on video conferencing apps
Get PDFThe advent of Industry 5.0 has brought a wealth of digital information to mobile app stores. With the help of emerging technologies such as machine learning and explainable artificial intelligence (XAI), these large amounts of user-generated data can be efficiently captured and analyzed. In this study, we propose an app store analysis framework and demonstrate the utility of the framework by mining and prioritizing user requirements in three popular video conferencing apps. We used the Sentistrength sentiment analysis tool, structural topic modeling, the Gephi web analysis tool, machine learning, and XAI techniques to conduct an in-depth analysis of user requirements in Microsoft Teams, ZOOM Cloud Meetings, and Google Meet. The findings indicated that Steal data, Audio and video quality, Customer service, Hacker issues, Meeting and account passwords, Mute and unmute, Features, and Office platform were the web conferencing system’s key areas for improvement. The study demonstrated the usability of app store analysis frameworks and the great potential of XAI to provide insights about requirements prioritization by interpreting machine learning models. Additionally, it offered valuable suggestions for app developers on using the massive data in app stores to improve their apps
Weighting of cues to categorization of song versus speech in tone-language and non-tone-language speakers
Get PDFOne of the most important auditory categorization tasks a listener faces is determining a sound's domain, a process which is a prerequisite for successful within-domain categorization tasks such as recognizing different speech sounds or musical tones. Speech and song are universal in human cultures: how do listeners categorize a sequence of words as belonging to one or the other of these domains? There is growing interest in the acoustic cues that distinguish speech and song, but it remains unclear whether there are cross-cultural differences in the evidence upon which listeners rely when making this fundamental perceptual categorization. Here we use the speech-to-song illusion, in which some spoken phrases perceptually transform into song when repeated, to investigate cues to this domain-level categorization in native speakers of tone languages (Mandarin and Cantonese speakers residing in the United Kingdom and China) and in native speakers of a non-tone language (English). We find that native tone-language and non-tone-language listeners largely agree on which spoken phrases sound like song after repetition, and we also find that the strength of this transformation is not significantly different across language backgrounds or countries of residence. Furthermore, we find a striking similarity in the cues upon which listeners rely when perceiving word sequences as singing versus speech, including small pitch intervals, flat within-syllable pitch contours, and steady beats. These findings support the view that there are certain widespread cross-cultural similarities in the mechanisms by which listeners judge if a word sequence is spoken or sung
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