Pedagogical Relationships in Short-Term Study Abroad Programmes: Exploring the Role of Consumer Identity in Collaborative Learning among Chinese Students in the UK

PurposeThe marketisation of higher education fosters the no/on of students asconsumers, highlighting the shi9ing dynamics of student–teacher relalaonships. This paper aims to contribute to ongoing discussions about students as consumers and their involvement in pedagogical practices. We explore students’ experiences in short-term study abroad (SA) programmes that involve collaborative learning, examining how a consumerism-oriented approach affects students’ perceptions of their pedagogical identities and student–teacher pedagogical relationships. MethodologyA qualitative exploratory study was conducted to capture students’ rich and subjective perceptions and experiences. The data were gathered through semi-structured interviews with 15 Chinese undergraduate students who participated in a short-term SA programme at a UK university. Following data translation and transcription, a thematic analysis approach facilitated our exploration.Findings Chinese students engage in SA programmes as a strategic investment in personal growth and transformation, with their consumer-oriented identity fostering a mutually beneficial relationship with educators and group members. This consumer mindset appears to enhance active student engagement and, to some extent, create reciprocal student–teacher interactions through power sharing and collaborative involvement.OriginalityThis study presents empirical data exploring the impact of consumer identity on the dynamics of student–teacher relationships in the SA context. It provides recommendations for implementing pedagogical approaches designed to mediate the influence of consumerism on student engagement, particularly in shaping collaborative student–teacher relationships. This study offers insights for future research on the effects of consumerism in higher education within cross-cultural contexts

Heuristic Algorithms for Energy and Performance Dynamic Optimization in Cloud Computing

Cloud computing becomes increasingly popular for hosting all kinds of applications not only due to their ability to support dynamic provisioning of virtualized resources to handle workload fluctuations but also because of the usage based on pricing. This results in the adoption of data centers which store, process and present the data in a seamless, efficient and easy way. Furthermore, it also consumes an enormous amount of electrical energy, then leads to high using cost and carbon dioxide emission. Therefore, we need a Green computing solution that can not only minimize the using costs and reduce the environment impact but also improve the performance. Dynamic consolidation of Virtual Machines (VMs), using live migration of the VMs and switching idle servers to sleep mode or shutdown, optimizes the energy consumption. We propose an adaptive underloading detection method of hosts, VMs migration selecting method and heuristic algorithm for dynamic consolidation of VMs based on the analysis of the historical data. Through extensive simulation based on random data and real workload data, we show that our method and algorithm observably reduce energy consumption and allow the system to meet the Service Level Agreements (SLAs)

A White-Box False Positive Adversarial Attack Method on Contrastive Loss-Based Offline Handwritten Signature Verification Models

In this paper, we tackle the challenge of white-box false positive adversarial attacks on contrastive loss-based offline handwritten signature verification models. We propose a novel attack method that treats the attack as a style transfer between closely related but distinct writing styles. To guide the generation of deceptive images, we introduce two new loss functions that enhance the attack success rate by perturbing the Euclidean distance between the embedding vectors of the original and synthesized samples, while ensuring minimal perturbations by reducing the difference between the generated image and the original image. Our method demonstrates state-of-the-art performance in white-box attacks on contrastive loss-based offline handwritten signature verification models, as evidenced by our experiments. The key contributions of this paper include a novel false positive attack method, two new loss functions, effective style transfer in handwriting styles, and superior performance in white-box false positive attacks compared to other white-box attack methods.Comment: 8 pages, 3 figure