A Survey

Agriculture occupation has been the prime occupation in India since the primeval era. Nowadays, the country is ranked second in the prime occupations threatening global warming. Apart from this, diseases in plants are challenging to this prime source of livelihood. The present research can help in recognition of different diseases among plants and help to find out the solution or remedy that can be a defense mechanism in counter to the diseases. Finding diseases among plant DL is considered to the most perfect and exact paradigms. Four labels are classified as “bacterial spot,” “yellow leaf curl virus,” “late blight,” and “healthy leaf.” An exemplar model of the drone is also designed for the purpose. The said model will be utilized for a live report for extended large crop fields. In this exemplar drone model, a high-resolution camera is attached. The captured images of plants will act as software input. On this basis, the software will immediately tell which plants are healthy and which are diseased.

Modeling artificial category learning from pixels: Revisiting Shepard, Hovland, and Jenkins (1961) with deep neural networks

Recent work has paired classic category learning models with convolutional neural networks (CNNs), allowing researchers to study categorization behavior from raw image inputs. However, this research typically uses naturalistic images, which assess participant responses to existing categories; yet, much of traditional category learning research has focused on using novel, artificial stimuli to examine the learning process behind how people acquire categories. In this work, we pair a CNN with ALCOVE (Kruschke, 1992), a well-known exemplar model of categorization, and attempt to examine whether this model can reproduce the classic type ordering effect from Shepard, Hovland, and Jenkins (1961) on raw images rather than abstract features. We examine this question with a variety of CNN architectures and image datasets and compare ALCOVE-CNN to two other models that lacked certain key features of ALCOVE. We found that our ALCOVE-CNN model could reproduce the type ordering effect more often than the other models we tested, but in limited situations. Our results showed that success varied greatly across the various configurations we tested, suggesting that the feature representations from CNNs provide strong constraints in properly capturing this effect.

Grammaticalization and phonological reidentification in White Hmong

The “dynamic coevolution of meaning and form” of Bybee et al. (1994: 20) has been the subject of significant discussion as regards the languages of Mainland Southeast Asia. However, little work has focused on the mechanisms through which this coevolution occurs when it does surface in these languages. The current work considers phonological reidentification resulting from phonetic reduction in White Hmong (Hmong-Mien, Laos) involving four morphemes, ntshai/ntshe ‘maybe’, saib/seb ‘see if/whether; comp.cfact’, puag/pug ‘locl;ints’, and niaj/nej ‘each, every’. These morphemes exhibit an alternation where a rime is phonologically reidentified in a manner consistent with typical phonetic underarticulation patterns, such that an exemplar-model approach (Pierrehumbert 2001, inter alia) provides a straightforward explanation. Furthermore, the data show that the phonological reidentification patterns found in White Hmong exhibit parallels in other languages in the region, confirming that an areal approach to grammaticalization provides greater descriptive adequacy cross-linguistically as regards this phenomenon.

Emotional Overload! A Dialogic Autoethnography of Scholar-Participant-Consumer Reactions to the Marketing of Thanatourism

The terms “thanatourism” and “dark tourism” relate to visiting places of human tragedy, which are increasingly developed as tourist destinations. There is a need to trouble thanatouristic assumptions through sharing and discussing lived experiences. These challenge the simplistic mechanistic marketing and conventional research practices of thanatourism. This dialogic autoethnographic study responds to this need, addressing thanatourism from the subjective and emotional perspectives of “insider” scholar-participant-consumers. Two interactive dialogic stories are presented by the lead and second authors, with the fourth providing a theoretically informed response. In the final section, the third author, an experienced autoethnographer and outsider to the thanatouristic topic and context, interrogates the lead author on concepts and issues emerging in the autoethnographic dialogue. Through engaging with this study, the reader is offered a multilayered, polysemic, emotionally provocative account of the ethical interface between thanatourism, consumer behaviour and marketing practices, and an exemplar model for future autoethnographic work.

Item-Based Acquisition of Dislocation in Early Child French

This study tests whether the usage-based concept of item-based schema can explain the development of constructions other than verb-argument constructions (VACs). Through a corpus study of 600 dislocations produced by two French children between age 1;7.12 and 2;5.11, and 600 from their input, we show that the concept of item-based schemas can indeed be extended to other types of constructions. We also show that the earliest item-based schemas produced by children are similar to specific syntactic featuress of dislocations in their input, and that the dislocations produced by the adults of our corpus can also be described in terms of item-based schemas. Based on these results, we make the hypothesis that the dislocations of adults may not necessarily be produced based on a more abstract construction, and that the radical exemplar model developed by Ambridge (2019) could also explain our data.

What counts as an exemplar model, anyway? A commentary on Ambridge (2020)

Ambridge (2019) calls for exemplar-based accounts of language acquisition. Do modern neural networks such as transformers or word2vec – which have been extremely successful in modern natural language processing (NLP) applications – count? Although these models often have ample parametric complexity to store exemplars from their training data, they also go far beyond simple storage by processing and compressing the input via their architectural constraints. The resulting representations have been shown to encode emergent abstractions. If these models are exemplar-based then Ambridge’s theory only weakly constrains future work. On the other hand, if these systems are not exemplar models, why is it that true exemplar models are not contenders in modern NLP?

What counts as an exemplar model, anyway? A commentary on Ambridge (2020)

Ambridge calls for exemplar-based accounts of language acquisition. Do modern neural networks such as transformers or word2vec – which have been extremely successful in modern natural language processing (NLP) applications – count? Although these models often have ample parametric complexity to store exemplars from their training data, they also go far beyond simple storage by processing and compressing the input via their architectural constraints. The resulting representations have been shown to encode emergent abstractions. If these models are exemplar-based then Ambridge’s theory only weakly constrains future work. On the other hand, if these systems are not exemplar models, why is it that true exemplar models are not contenders in modern NLP?

An exemplar model should be able to explain all syntactic priming phenomena: A commentary on Ambridge (2020)

The authors argue that Ambridge’s radical exemplar account of language cannot clearly explain all syntactic priming evidence, such as inverse preference effects ( greater priming for less frequent structures), and the contrast between short-lived lexical boost and long-lived abstract priming. Moreover, without recourse to a level of abstract syntactic structure, Ambridge’s account cannot explain abstract priming in amnesia patients or cross-linguistic priming. Instead, the authors argue that abstract representations remain the more parsimonious account for the wide variety of syntactic priming phenomena.

How are exemplar representations transformed by encoding, retrieval, and explicit knowledge? A commentary on Ambridge (2020)

The radical exemplar model resonates with work on perceptual classification and categorization highlighting the role of exemplars in memory representations. Further development of the model requires acknowledgment of both the fleeting and fragile nature of perceptual representations and the gist-based, good-enough quality of long-term memory representations. Retrieval operations potentially serve as a mechanism for abstraction as representations of exemplars are distorted through reconstructive processes. As a framework applicable to both first and second language acquisition, the model needs to account for how explicit knowledge arises and its role in filtering input via selective attention.