Metafora Konseptual Dalam Cerita Pendek Berbahasa Inggris Di Platform The Short Story: Kajian Semantik Kognitif

This research intends to clarify the forms of metaphors, conceptual meaning, and image scheme that appear in the five English short stories named The Short Story online platform by applying cognitive semantics. This is a descriptive qualitative research. The whole result of this research is illustrated by words. The theory used in analyzing the metaphors is from Saeed (2009). Besides, the theory for investigating the scheme is from Cruse and Croft (2004). The result of this research is 12 metaphors that have been classified based on their categories. First, five data of conventional metaphors with conceptual meaning such as life choice, darkness, noise, nervousness, and old. Second, four data of systematic metaphors with conceptual meaning such as fearness, compulsion, fire, and waving. Third, two data of asymmetric metaphors with conceptual meaning such as preparation and persistence. The last, one data of abstraction metaphor with conceptual meaning that is hard working. Those data have 4 image schemes, such as force (restraint, compulsion, and counterforce), existence (space, process, object), identity (matching), and unity/multiplicity (merging).

Managing the economic sustainability of the Belt and Road Initiative by applying Pragmatic Identity Matching PrIM

Achieving and managing economic sustainability is one of the crucial tasks for our globalized world. One of the most ambitious economic projects known to humankind claims to stand for long-term global economic sustainability including ecological, social, and cultural aspects: the Belt and Road Initiative (BRI), in which China invites the world to join its vision of a “peaceful cooperation for the wealth and cultural exchange of all nations” (Xi Jinping 2013 in his famous speech Promote People-to-People Friendship an Create a Better Future). This invitation led to a wide range of responses, from fundamental rejection to supportive participation. Matching the participants in this megaproject—their contributions, particular prerequisites, and development interests—requires a holistic participative planning approach with solutions tailored specifically to the participating partners. This article shows how Pragmatic Identity Matching (PrIM), a scalable integration framework, can be used to meet this requirement. PrIM provides an identity-oriented infrastructure for aligned planning, implementation and communications, acknowledging, and embracing participants from different cultural backgrounds such as Asian, Arabian, African, Russian, and European cultures. As a structural-scientific approach that synthesizes elemental semiotic thinking and research in psychology and the neurosciences, PrIM provides a meta-planning structure beyond any value-driven positions and perspectives, one that imparts equivalence to information. PrIM can help the BRI avoid a spiral of non-coordinated activities, thereby preventing loss of investment. The formation of a transdisciplinary BRI Management Academy that uses PrIM would help create the necessary infrastructure for a successful and transparent implementation of the BRI.

Effects of Limited Hold on Equivalence Class Formation

In an attempt to limit the opportunity to engage in mediating behavior, two groups of adult participants received preliminary training in identity matching with limited hold levels (LH) for responding of 0.7 s for the sample and 1.2 s for the comparisons. The two groups were subsequently trained to form three 5-member classes, using the same LH levels, where the A, B, D, and E stimuli were abstract stimuli, and the C stimuli were meaningful pictures. In two tests for emergent relations, the LH for Group Short was unchanged, whereas 5 s were added to the LH for the comparisons for Group Long. None of the participants in Group Short responded in accordance with stimulus equivalence in either of the two tests. In Group Long, one participant responded in accordance with stimulus equivalence in the first test, and an additional eight participants formed equivalence classes in the second test.

Unimodal and cross-modal identity judgements using an audio-visual sorting task: Evidence for independent processing of faces and voices

Unimodal and cross-modal information provided by faces and voices contribute to identity percepts. To examine how these sources of information interact, we devised a novel audio-visual sorting task in which participants were required to group video-only and audio-only clips into two identities. In a series of three experiments, we show that unimodal face and voice sorting were more accurate than cross-modal sorting: While face sorting was consistently most accurate followed by voice sorting, cross-modal sorting was at chancel level or below. In Experiment 1, we compared performance in our novel audio-visual sorting task to a traditional identity matching task, showing that unimodal and cross-modal identity perception were overall moderately more accurate than the traditional identity matching task. In Experiment 2, separating unimodal from cross-modal sorting led to small improvements in accuracy for unimodal sorting, but no change in cross-modal sorting performance. In Experiment 3, we explored the effect of minimal audio-visual training: Participants were shown a clip of the two identities in conversation prior to completing the sorting task. This led to small, nonsignificant improvements in accuracy for unimodal and cross-modal sorting. Our results indicate that unfamiliar face and voice perception operate relatively independently with no evidence of mutual benefit, suggesting that extracting reliable cross-modal identity information is challenging.

Psychophysical profiles in super-recognizers

Facial identity matching ability varies widely, ranging from prosopagnosic individuals (who exhibit profound impairments in face cognition/processing) to so-called super-recognizers (SRs), possessing exceptional capacities. Yet, despite the often consequential nature of face matching decisions—such as identity verification in security critical settings—ability assessments tendentially rely on simple performance metrics on a handful of heterogeneously related subprocesses, or in some cases only a single measured subprocess. Unfortunately, methodologies of this ilk leave contributions of stimulus information to observed variations in ability largely un(der)specified. Moreover, they are inadequate for addressing the qualitative or quantitative nature of differences between SRs’ abilities and those of the general population. Here, therefore, we sought to investigate individual differences—among SRs identified using a novel conservative diagnostic framework, and neurotypical controls—by systematically varying retinal availability, bandwidth, and orientation of faces’ spatial frequency content in two face matching experiments. Psychophysical evaluations of these parameters’ contributions to ability reveal that SRs more consistently exploit the same spatial frequency information, rather than suggesting qualitatively different profiles between control observers and SRs. These findings stress the importance of optimizing procedures for SR identification, for example by including measures quantifying the consistency of individuals’ behavior.

Identity matching in social media platforms

Identity matching is the process of mapping profile information from disparate data sources to one single entity; this is a crucial task for many businesses and governments. Introduction of Web 2.0 and the ever increasing number of social media platforms has led to an explosive amount of user participation and collaboration on web. An ordinary user has more than one social media profile, each of which has a unique set of properties and features. This thesis proposes a framework that uses syntactic and semantic based identity matching approaches among Facebook, Linkedin and Twitter user profiles. The framework accomplishes this task by collecting available profile data and performing analysis and comparison using a set of methodologies. These methods consist of weighted string matching techniques, Google Maps, YouTube and NLP web APIs. Extracted Profiles with a similarity score above a pre-computed threshold value are considered a match.

Identity matching in social media platforms

Identity matching is the process of mapping profile information from disparate data sources to one single entity; this is a crucial task for many businesses and governments. Introduction of Web 2.0 and the ever increasing number of social media platforms has led to an explosive amount of user participation and collaboration on web. An ordinary user has more than one social media profile, each of which has a unique set of properties and features. This thesis proposes a framework that uses syntactic and semantic based identity matching approaches among Facebook, Linkedin and Twitter user profiles. The framework accomplishes this task by collecting available profile data and performing analysis and comparison using a set of methodologies. These methods consist of weighted string matching techniques, Google Maps, YouTube and NLP web APIs. Extracted Profiles with a similarity score above a pre-computed threshold value are considered a match.

Maritime vessel re-identification: novel VR-VCA dataset and a multi-branch architecture MVR-net

Maritime vessel re-identification (re-ID) is a computer vision task of vessel identity matching across disjoint camera views. Prominent applications of vessel re-ID exist in the fields of surveillance and maritime traffic flow analysis. However, the field suffers from the absence of a large-scale dataset that enables training of deep learning models. In this study, we present a new dataset that includes 4614 images of 729 vessels along with 5-bin orientation and 8-class vessel-type annotations to promote further research. A second contribution of this study is the baseline re-ID analysis of our new dataset. Performances of 10 recent deep learning architectures are quantitatively compared to reveal the best practices. Lastly, we propose a novel multi-branch deep learning architecture, Maritime Vessel Re-ID network (MVR-net), to address the challenging problem of vessel re-ID. Evaluation of our approach on the new dataset yields 74.5% mAP and 77.9% Rank-1 score, providing a performance increase of 5.7% mAP and 5.0% Rank-1 over the best-performing baseline. MVR-net also outperforms the PRN (a pioneering vehicle re-ID network), by 2.9% and 4.3% higher mAP and Rank-1, respectively.