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In this paper we present a novel algorithm for concurrent lock-free internal binary search trees (BST) and implement a Set abstract data type (ADT) based on that. We show that in the presented lock-free BST algorithm the amortized step complexity of each set operation -Add, Remove and Contains -is O(H(n) + c), where, H(n) is the height of BST with n number of nodes and c is the contention during the execution. Our algorithm adapts to contention measures according to read-write load. If the situation is read-heavy, the operations avoid helping pending concurrent Remove operations during traversal, and, adapt to interval contention. However, for write-heavy situations we let an operation help pending Remove, even though it is not obstructed, and so adapt to tighter point contention. It uses single-word compare-and-swap (CAS) operations. We show that our algorithm has improved disjoint-access-parallelism compared to similar existing algorithms. We prove that the presented algorithm is linearizable. To the best of our knowledge this is the first algorithm for any concurrent tree data structure in which the modify operations are performed with an additive term of contention measure.

The COVID-19 pandemic has generally destroyed the global tourism industry and threatened the recovery of destinations in developing countries facing more challenges from increasingly serious waves of the pandemic. Although many studies have attempted to measure the general impacts of COVID-19, very little research has been conducted to assess its overall impact on specific tourism destinations throughout many waves of the pandemic. This research aims to explore how a tourism economy in a developing country context has been damaged after many waves of COVID-19. A typical emerging city in Vietnam experiencing three waves of the COVID-19 pandemic was selected as a case study. The study recruited 40 representatives of tourism-related organizations for in-depth interviews, while 280 questionnaires were distributed to participants from different tourism organizations. The findings indicate that the majority of tourism businesses in the examined case study seriously suffered from the pandemic, and very few tourism-related enterprises were able to recover after the first wave of infection. Unfortunately, the tourism business sectors were found to be on the brink of bankruptcy or facing permanent shutdown after the third wave. All tourism enterprises generally appeared to experience a sharp drop in the number of customers, tourism revenue, service facilities and exploitation, as well as employee downsizing, but the degree of downturn differed among the examined enterprises. Among the tourism enterprises, travel agencies and the accommodation sector were found to suffer the greatest economic losses compared to other stakeholders. In general, the COVID-19 pandemic’s impact on the tourism business in Vietnam is a big concern, which may require a timely economic policy response and financial scheme to better support local enterprises in coping with the challenges during post-pandemic recovery.

Abstract-Why do bilingual language teachers and students switch between the two languages in their language classrooms? On the evidence of current research findings in relation to English-Vietnamese codeswitching in the educational contexts of Vietnam, this article identifies that classroom code-switching between the second language and the first language has its own pedagogic functions and it can be a valuable language classroom resource to both teachers and learners. In Vietnam, the implementation of the monolingual approach of teaching English-through-English-only faces many challenges such as inadequate classroom resources, students' low levels of English competence, motivation and autonomy, teachers' limited English abilities, and inappropriate teaching methods. Many Vietnamese teachers of English support code-switching in the classroom and they teach English through the bilingual approach. English-Vietnamese code-switching is reported not to be a restriction on the acquisition of English; rather, it can facilitate the teaching and learning of general English in Vietnam. This practice of code-switching is not just due to a lack of sufficient proficiency to maintain a conversation in English; rather, it serves a number of pedagogic functions such as explaining new words and grammatical rules, giving feedback, checking comprehension, making comparison between English and Vietnamese, establishing good rapport between teachers and students, creating a friendly classroom atmosphere and supporting group dynamics.

Online service providers often use challenge questions (a.k.a. knowledge-based authentication) to facilitate resetting of passwords or to provide an extra layer of security for authentication. While prior schemes explored both static and dynamic challenge questions to improve security, they do not systematically investigate the problem of designing challenge questions and its effect on user recall performance. Interestingly, as answering different styles of questions may require different amount of cognitive effort and evoke different reactions among users, we argue that the style of challenge questions itself can have a significant effect on user recall performance and usability of such systems. To address this void and investigate the effect of question types on user performance, this paper explores location-based challenge question generation schemes where different types of questions are generated based on users' locations tracked by smartphones and presented to users. For evaluation, we deployed our location tracking application on users' smartphones and conducted two real-life studies using four different kinds of challenge questions. Each study was approximately 30 days long and had 14 and 15 users respectively. Our findings suggest that the question type can have a significant effect on user performance. Finally, as individual users may vary in terms of performance and recall rate, we investigate and present a Bayesian classifier based authentication algorithm that can authenticate legitimate users with high accuracy by leveraging individual response patterns while reducing the success rate of adversaries.