Browsing Computer Science by Title
Now showing items 501-520 of 549
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Towards a testing framework with visual feedback for actor-based systems
(IEEE, 2018-08-02)We introduce a prototype testing framework as an extension of JUnit for testing actor-based systems. Our framework runs a given JUnit test in various schedules and records the execution trace for each run. In case a failure ... -
Towards an efficient anomaly-based intrusion detection for software-defined networks
(Institution of Engineering and Technology, 2018-08-24)Software-defined networking (SDN) is a new paradigm that allows developing more flexible network applications. A SDN controller, which represents a centralised controlling point, is responsible for running various network ... -
Towards automated aircraft maintenance inspection. A use case of detecting aircraft dents using mask r-cnn
(American Institute of Aeronautics and Astronautics Inc, AIAA, 2020)Deep learning can be used to automate aircraft maintenance visual inspection. This can help increase the accuracy of damage detection, reduce aircraft downtime, and help prevent inspection accidents. The objective of this ... -
Towards automatic selection of direct vs. iterative solvers for cloud-based finite element analysis
(Civil-Comp, 2015)The new trend in engineering is to solve complex computational problems in the cloud using high performance computing (HPC) services provided by different vendors. In this paper, we compare performances of direct vs. ... -
Towards interactive explanation-based nutrition virtual coaching systems
(Springer, 2024-01)The awareness about healthy lifestyles is increasing, opening to personalized intelligent health coaching applications. A demand for more than mere suggestions and mechanistic interactions has driven attention to nutrition ... -
Towards low cost and smart load testing as a service using containers
(Springer, 2021)Providing end-users with high quality e-commerce, online communication, education services requires careful performance monitoring, tuning and prediction under heavy traffic loads. To address this issue, we propose and ... -
Towards subtyped program generation in F#
(ACM, 2011)Program Generation is the technique of combining code fragments to construct a program. In this work we report on our progress to extend F# with program generation constructs. Our prototype implementation uses a translation ... -
Towards test automation for certification tests in the banking domain
(IEEE, 2023)Software systems in the banking domain are business-critical applications that provide financial services. These systems are subject to rigorous certification tests, which are performed manually, and take weeks to complete. ... -
Tractable policy management framework for IoT
(SPIE, 2017)Due to the advancement in the technology, hype of connected devices (hence forth referred to as IoT) in support of automating the functionality of many domains, be it intelligent manufacturing or smart homes, have become ... -
TRIBE: Trust revision for information based on evidence
(IEEE, 2013)In recent years, the number of information sources available to support decision-making has increased dramatically. However, more information sources do not always mean higher precision in the fused information. This is ... -
TruSD: Trust framework for service discovery among IoT devices
(Elsevier, 2020-09-04)IoT provides an environment which enables access to a plethora of different services. In order to reach these services, devices need to decide if the providers are trustable or not. The decision to trust a node with whom ... -
Trust estimation and fusion of uncertain information by exploiting consistency
(IEEE, 2014)Agents may cooperate by communicating their opinions about various phenomena. These opinions are then fused by agents and used for informed decision-making. However, fusing opinions from diverse sources is not trivial - ... -
Trust estimation of sources over correlated propositions
(IEEE, 2018-09-05)This work analyzes the impact of correlated propositions when estimating the reporting behavior of information sources. These behavior estimates are critical for fusion, and traditional methods assume the propositions are ... -
Trust in robot–robot scaffolding
(IEEE, 2023-12-01)The study of robot trust in humans and other agents is not explored widely despite its importance for the near future human-robot symbiotic societies. Here, we propose that robots should trust partners that tend to reduce ... -
Trust me! I am a robot: an affective computational account of scaffolding in robot-robot interaction
(IEEE, 2021-08-08)Forming trust in a biological or artificial interaction partner that provides reliable strategies and employing the learned strategies to scaffold another agent are critical problems that are often addressed separately in ... -
Trust-based fusion of classifiers for static code analysis
(IEEE, 2014)Static code analysis tools automatically generate alerts for potential software faults that can lead to failures. However, developers are usually exposed to a large number of alerts. Moreover, some of these alerts are ... -
Trustworthiness assessment in multimodal human-robot interaction based on cognitive load
(IEEE, 2022)In this study, we extend our robot trust model into a multimodal setting in which the Nao robot leverages audio-visual data to perform a sequential multimodal pattern recalling task while interacting with a human partner ... -
Turkish WordNet KeNet
(Global WordNet Association, 2021)Currently, there are two available wordnets for Turkish: TR-wordnet of BalkaNet and KeNet. As the more comprehensive wordnet for Turkish, KeNet includes 76,757 synsets. KeNet has both intralingual semantic relations and ... -
Uncertainty assessment for detection of spoofing attacks to speaker verification systems using a Bayesian approach
(Elsevier, 2022-02)There has been tremendous progress in automatic speaker verification systems over the last decade. Still, spoofing attacks pose a significant challenge to their deployment. Even though there are various attack techniques ... -
Uncertainty-aware deep classifiers using generative models
(Association for the Advancement of Artificial Intelligence, 2020)Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to ...
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