3,702 research outputs found
Empirical Evaluation of Mutation-based Test Prioritization Techniques
We propose a new test case prioritization technique that combines both
mutation-based and diversity-based approaches. Our diversity-aware
mutation-based technique relies on the notion of mutant distinguishment, which
aims to distinguish one mutant's behavior from another, rather than from the
original program. We empirically investigate the relative cost and
effectiveness of the mutation-based prioritization techniques (i.e., using both
the traditional mutant kill and the proposed mutant distinguishment) with 352
real faults and 553,477 developer-written test cases. The empirical evaluation
considers both the traditional and the diversity-aware mutation criteria in
various settings: single-objective greedy, hybrid, and multi-objective
optimization. The results show that there is no single dominant technique
across all the studied faults. To this end, \rev{we we show when and the reason
why each one of the mutation-based prioritization criteria performs poorly,
using a graphical model called Mutant Distinguishment Graph (MDG) that
demonstrates the distribution of the fault detecting test cases with respect to
mutant kills and distinguishment
Personalized Resource Allocation in Wireless Networks: An AI-Enabled and Big Data-Driven Multi-Objective Optimization
The design and optimization of wireless networks have mostly been based on
strong mathematical and theoretical modeling. Nonetheless, as novel
applications emerge in the era of 5G and beyond, unprecedented levels of
complexity will be encountered in the design and optimization of the network.
As a result, the use of Artificial Intelligence (AI) is envisioned for wireless
network design and optimization due to the flexibility and adaptability it
offers in solving extremely complex problems in real-time. One of the main
future applications of AI is enabling user-level personalization for numerous
use cases. AI will revolutionize the way we interact with computers in which
computers will be able to sense commands and emotions from humans in a
non-intrusive manner, making the entire process transparent to users. By
leveraging this capability, and accelerated by the advances in computing
technologies, wireless networks can be redesigned to enable the personalization
of network services to the user level in real-time. While current wireless
networks are being optimized to achieve a predefined set of quality
requirements, the personalization technology advocated in this article is
supported by an intelligent big data-driven layer designed to micro-manage the
scarce network resources. This layer provides the intelligence required to
decide the necessary service quality that achieves the target satisfaction
level for each user. Due to its dynamic and flexible design, personalized
networks are expected to achieve unprecedented improvements in optimizing two
contradicting objectives in wireless networks: saving resources and improving
user satisfaction levels
Learning to Walk Autonomously via Reset-Free Quality-Diversity
Quality-Diversity (QD) algorithms can discover large and complex behavioural
repertoires consisting of both diverse and high-performing skills. However, the
generation of behavioural repertoires has mainly been limited to simulation
environments instead of real-world learning. This is because existing QD
algorithms need large numbers of evaluations as well as episodic resets, which
require manual human supervision and interventions. This paper proposes
Reset-Free Quality-Diversity optimization (RF-QD) as a step towards autonomous
learning for robotics in open-ended environments. We build on Dynamics-Aware
Quality-Diversity (DA-QD) and introduce a behaviour selection policy that
leverages the diversity of the imagined repertoire and environmental
information to intelligently select of behaviours that can act as automatic
resets. We demonstrate this through a task of learning to walk within defined
training zones with obstacles. Our experiments show that we can learn full
repertoires of legged locomotion controllers autonomously without manual resets
with high sample efficiency in spite of harsh safety constraints. Finally,
using an ablation of different target objectives, we show that it is important
for RF-QD to have diverse types solutions available for the behaviour selection
policy over solutions optimised with a specific objective. Videos and code
available at https://sites.google.com/view/rf-qd
Therapeutic Adherence of People with Mental Disorders: An Evolutionary Concept Analysis
Patient therapeutic adherence lies at the core of mental health care. Health Care professionals and organizations play a major role in promoting adherence among people with mental disorders. However, defining therapeutic adherence remains complex. We used Rodgers’ evolutionary concept analysis to explore the concept of therapeutic adherence in the context of mental health. We conducted a systematic literature search on Medline/PubMed and CINAHL for works published between January 2012 and December 2022. The concept analysis showed that major attributes of therapeutic adherence include patient, microsystem and meso/exosystem-level factors. Antecedents are those related to patients, such as their background, beliefs and attitudes, and acceptance of mental illness–and those related to patient-HCP therapeutic engagement. Lastly, three different consequences of the concept emerged: an improvement in clinical and social outcomes, commitment to treatment, and the quality of healthcare delivery. We discuss an operational definition that emerged from the concept analysis approach. However, considering the concept has undergone evolutionary changes, further research related to patient adherence experiences in an ecological stance is needed.info:eu-repo/semantics/publishedVersio
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