130 research outputs found
Current trends in the socio-economic development of small island developing countries
Taken as a whole, the economic performance of small states (defined here as those with populations of less than five million) has over the years been a good one. The GNP per capita of 34 of them grew at an overall annual average rate of over one per cent during the period 1980-91, more than twice that of developing countries as a whole. Their overall income (GDP per capita) is also well above the developing country average. But such figures can be deceptive, and for a variety of reasons the economies of small states are often fragile and show few of the linkages necessary for sustainable development. There are a variety of reasons for this. The small size of their domestic markets and resulting difficulty in generating scale economies mean a high degree of openness and vulnerability to external economic events; their narrow resource base encourages specialisation in a few products; their history has led to dependence on a few markets, accentuated by preferential trade agreements; their particular circumstances have resulted to reliance on concessional external financial flows; and their small populations have meant high per capita costs of providing government services, and shortages of certain skills. The impact of these characteristics on development can at times be pernicious and, therefore, some have called for the economies concerned to be treated as a special case of current development paradigms.peer-reviewe
A Deep Reinforcement Learning based Algorithm for Time and Cost Optimized Scaling of Serverless Applications
Serverless computing has gained a strong traction in the cloud computing
community in recent years. Among the many benefits of this novel computing
model, the rapid auto-scaling capability of user applications takes prominence.
However, the offer of adhoc scaling of user deployments at function level
introduces many complications to serverless systems. The added delay and
failures in function request executions caused by the time consumed for
dynamically creating new resources to suit function workloads, known as the
cold-start delay, is one such very prevalent shortcoming. Maintaining idle
resource pools to alleviate this issue often results in wasted resources from
the cloud provider perspective. Existing solutions to address this limitation
mostly focus on predicting and understanding function load levels in order to
proactively create required resources. Although these solutions improve
function performance, the lack of understanding on the overall system
characteristics in making these scaling decisions often leads to the
sub-optimal usage of system resources. Further, the multi-tenant nature of
serverless systems requires a scalable solution adaptable for multiple
co-existing applications, a limitation seen in most current solutions. In this
paper, we introduce a novel multi-agent Deep Reinforcement Learning based
intelligent solution for both horizontal and vertical scaling of function
resources, based on a comprehensive understanding on both function and system
requirements. Our solution elevates function performance reducing cold starts,
while also offering the flexibility for optimizing resource maintenance cost to
the service providers. Experiments conducted considering varying workload
scenarios show improvements of up to 23% and 34% in terms of application
latency and request failures, while also saving up to 45% in infrastructure
cost for the service providers.Comment: 15 pages, 22 figure
What Makes a Review Credible? Heuristic and Systematic Factors for the Credibility of Online Reviews
In the digital transformation era, online reviews have become an important source of information for decisions about purchases. Research shows that online reviews influence users’ behaviors and product sales. However, questions remain about how and why users assess the credibility of online reviews for different products/services on different websites. Using semi-structured interviews as a way of understanding how users assess the credibility of online reviews, we propose a comprehensive credibility analysis model for online reviews. The proposed model extends a model we previously proposed; and uses the Heuristic Systematic Model (HSM) as a theoretical lens, which helps us to understand different features that impact the credibility of online reviews. Our findings reveal several factors which impact the credibility of online reviews that have not been identified in the previous literature
Are Online Consumer Reviews Credible? A Predictive Model based on Deep Learning
As the importance of online consumer reviews has grown, the concerns about their credibility being damaged by the presence of fake reviews have also grown. Extant literature reveals the importance of online reviews for consumers. Yet, there is a lack of research in the literature that considers consumer perception while developing a predictive model for the credibility of online reviews. This research aims to fill this gap by combining two different streams in the literature namely human-driven and data-driven approaches. To do so, we use two datasets with different labelling approaches to develop a predictive model, the first one is labelled based on the Yelp filtering algorithm and the second one is labelled based on the crowd’s perception towards credibility. Results from our predictive model reveal that it can predict credibility with a performance of 82% AUC, using reviews’ attributes namely, length, subjectivity, readability, extremity, external and internal consistency
A Twitter narrative of the COVID-19 pandemic in Australia
Social media platforms contain abundant data that can provide comprehensive
knowledge of historical and real-time events. During crisis events, the use of
social media peaks, as people discuss what they have seen, heard, or felt.
Previous studies confirm the usefulness of such socially generated discussions
for the public, first responders, and decision-makers to gain a better
understanding of events as they unfold at the ground level. This study performs
an extensive analysis of COVID-19-related Twitter discussions generated in
Australia between January 2020, and October 2022. We explore the Australian
Twitterverse by employing state-of-the-art approaches from both supervised and
unsupervised domains to perform network analysis, topic modeling, sentiment
analysis, and causality analysis. As the presented results provide a
comprehensive understanding of the Australian Twitterverse during the COVID-19
pandemic, this study aims to explore the discussion dynamics to aid the
development of future automated information systems for epidemic/pandemic
management.Comment: Accepted to ISCRAM 202
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