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Belief in a just world for the self and others, Karma, system justification and well-being during COVID-19 pandemic:Evidence from 15 Asian nations
Get PDFOnline processing shows advantages of bimodal listening-while-reading for vocabulary learning:an eye-tracking study
Get PDFUndergraduate Public Relations Education in the United Kingdom:Quo Vadis?
Get PDFIn the current context of the undergraduate PR academic education in the United Kingdom degrees being shut down or merged with other communication disciplines, the present essay represents a timely reflection on the results and internal incoherence of PR education provision in the United Kingdom. Starting from the key idea that public relations is a mature occupation and academic social discipline, we developed a thorough analysis of PR fields, where we analysed the intra- and inter-dynamics between these various types of fields, aiming at identifying the main issues that impact the teaching of PR and its academic expressions. Drawn from the field analysis, we then focused on two key trends which currently shape the undergraduate PR education in the UK to recommend changes to the process of curriculum development that reconstructs the social value of Public Relations
Decomposing drivers in avian insectivory:Large-scale effects of climate, habitat and bird diversity
Get PDFEditorial: Forms and functions of soft norms and informal law-making in international migration law:a different frontier
Get PDFGendered Precarity in Saudi Arabia:Examining the State Policies and Patriarchal Culture in the Labor Market
Get PDFKeeping time with digital technologies:From real-time environments to forest futurisms
Get PDFForests are zones of multiple temporalities. They keep time and are constituted through time-keeping practices. Digital technologies of environmental monitoring and management increasingly organise forest temporalities. This article considers how emerging techno-temporalities measure, pace, and transform forest worlds while reproducing and reconfiguring longer durations of colonial and capitalist technologies. We draw together scholarship on political forests, digital media temporalities, and anti-colonial and Indigenous thinking to analyse the politics of time that materialise through digital technologies and shape what forest pasts, presents, and futures are senseable and possible. In particular, we trace the socio-technical production of the ‘real-time’ as a temporal register of experiencing, knowing, and governing forest environments. Analysing a real-time deforestation alert system in the Amazon, we consider how these temporalities valorise immediate, continuous forest data that can be mobilised for understanding and protecting forests, while simultaneously glossing over durational colonial and capitalist framings of forests that rely on dispossession, extraction, and enclosure. The second half of the article turns to Indigenous futurisms and artistic and socio-political uses of digital platforms that rework forest temporalities. By analysing these multiple and sometimes contradictory temporalities, we suggest that these practices and interventions can challenge dominant timelines and their inequities through pluralistic and redistributive configurations of temporality, land, and data sovereignty
Human Action Recognition Using Multi-Stream Fusion and Hybrid Deep Neural Networks
Get PDFAction Recognition in videos is a topic of interest in the area of computer vision, due to potential applications such as multimedia indexing and surveillance in public areas. In this research, we first propose spatial and temporal Convolutional Neural Network (CNNs), based on transfer learning using ResNet101, GoogleNet and VGG16, for undertaking human action recognition. Besides that, hybrid networks such as CNNRecurrent Neural Network (RNN) models are also exploited as encoder-decoder architectures for video action classification. In particular, different types of RNNs such as Long Short-Term Memory (LSTM), Bidirectional-LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional-GRU (BiGRU), are exploited as the decoders for action recognition. To further enhance performance, diverse aggregation networks of CNN and CNN-RNN models are implemented. Specifically, an Average Fusion method is used to integrate spatial and temporal CNNs trained on images, as well as CNN-RNN trained on videos, where the final classification is formed by combining Softmax scores of these models via a late fusion. A total of 22 models (1 motion CNN, 3 spatial CNNs, 12 CNN-RNNs and 6 fusion networks) are implemented which are evaluated using UCF11, UCF50, and UCF101 datasets for performance comparison. The empirical results indicate the significant efficiency of Average Fusion of multiple Spatial-CNNs with one Motion-CNN, and ResNet101-BiGRU, among all the networks for undertaking realistic video action recognition
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