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Research indicates a long historical connection between racism and nationalist ideologies. This connection has been highlighted in the resurgence of exclusionary nationalism in recent years, across many multicultural societies. This chapter discusses the notions of race, ethnicity and nation, and critically examines how racism shapes contemporary manifestations of nationalist discourse across the world. It explores the historical role of settler-colonialism, imperial expansions and the capitalist development in shaping the racial/ethnic aspect of nationalist development. Moreover, it provides an analysis of the interconnections between the racialisation of minorities, exclusionary ideologies and the consolidation of ethno-nationalist tropes. This chapter further considers the impact of demographic changes in reinforcing anti-migrant exclusionary sentiments. This is examined in connection with emerging nativist discourse, exploring how xenophobic racism has shaped and is shaped by nostalgic nationalism based on the sanitisation of the legacies of Empire and colonialism.

This study aims to propose novel edge device-tailored federated learning algorithms of local classifiers (stochastic gradient descent, support vector machines), namely, FL-lSGD and FL-lSVM. These algorithms are designed to address the challenge of large-scale ImageNet classification.

The authors’ FL-lSGD and FL-lSVM trains in a parallel and incremental manner to build an ensemble local classifier on Raspberry Pis without requiring data exchange. The algorithms load small data blocks of the local training subset stored on the Raspberry Pi sequentially to train the local classifiers. The data block is split into k partitions using the k-means algorithm, and models are trained in parallel on each data partition to enable local data classification.

Empirical test results on the ImageNet data set show that the authors’ FL-lSGD and FL-lSVM algorithms with 4 Raspberry Pis (Quad core Cortex-A72, ARM v8, 64-bit SoC @ 1.5GHz, 4GB RAM) are faster than the state-of-the-art LIBLINEAR algorithm run on a PC (Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores, 32GB RAM).

Efficiently addressing the challenge of large-scale ImageNet classification, the authors’ novel federated learning algorithms of local classifiers have been tailored to work on the Raspberry Pi. These algorithms can handle 1,281,167 images and 1,000 classes effectively.

This chapter presents a research study that examined post-disaster authentic university academic care resilience (PAUACR) at a Historically Black College and University (HBCU). PAUACR is a university’s and its students’ capacity to bounce back from post-disaster educational challenges. PAUACR requires a strong caring response and authentic academic care environments. For the University of the Virgin Islands (UVI), PAUACR following Hurricanes Irma and Maria was crucial to ensure students successfully completed the academic year. To assess UVI’s PAUACR, this study utilized a caring about academic caregiving inventory (CAACI). This 49-item instrument was used to gain students’ discernment of post-disaster authentic university academic care (PAUAC). The research employed a cross-sectional exploratory survey research design. The empirical analysis found associations between the structural workings of UVI’s academic caregiving in the aftermath of hurricanes Irma and Maria. These findings offer distinctive indicators of UVI’s PAUACR. Along with the findings, this chapter offers practical lessons of academic resilience drawn from the experience of conducting post-disaster research.