Browsing Computer Science by Title
Now showing items 513-532 of 549
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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 ... -
Uncertainty-aware situational understanding
(SPIE, 2019)Situational understanding is impossible without causal reasoning and reasoning under and about uncertainty, i.e. prob-abilistic reasoning and reasoning about the confidence in the uncertainty assessment. We therefore ... -
Use of line spectral frequencies for emotion recognition from speech
(IEEE, 2010)We propose the use of the line spectral frequency (LSF) features for emotion recognition from speech, which have not been been previously employed for emotion recognition to the best of our knowledge. Spectral features ... -
Using artificial neural networks to provide guidance in extending PL/SQL programs
(Springer, 2022-12)Extending legacy systems with new objects for contemporary functionality or technology can lead to architecture erosion. Misplacement of these objects gradually hampers the modular structure, of which documentation is ... -
Using convolutional neural networks to automate aircraft maintenance visual inspection
(MDPI, 2020-12)Convolutional Neural Networks combined with autonomous drones are increasingly seen as enablers of partially automating the aircraft maintenance visual inspection process. Such an innovative concept can have a significant ... -
Using different loss functions with YOLACT++ for real-time instance segmentation
(IEEE, 2023)In this paper, we study and analyze the performance of various loss functions on a recently proposed real-time instance segmentation algorithm, YOLACT++. In particular, we study the loss functions, including Huber Loss, ... -
Using eigenvoices and nearest-neighbors in HMM-based cross-lingual speaker adaptation with limited data
(IEEE, 2017-04)Cross-lingual speaker adaptation for speech synthesis has many applications, such as use in speech-to-speech translation systems. Here, we focus on cross-lingual adaptation for statistical speech synthesis systems using ... -
Using high-level synthesis for rapid design of video processing pipes
(IEEE, 2016)In this work, we share our experience in using High-Level Synthesis (HLS) for rapid development of an optical flow design on FPGA. We have performed HLS using Vivado HLS as well as a HLS tool we have developed for the ... -
Using hypergraph clustering for software architecture reconstruction of data-tier software
(Springer International Publishing, 2016)Software architecture reconstruction techniques aim at recovering software architecture documentation regarding a software system. These techniques mainly analyze coupling/dependencies among the software modules to group ... -
Utilizing clone detection for domain analysis of simulation systems
(IEEE, 2012)This paper presents a case study on utilizing a clone detection technique for deriving commonalities among four different industrial simulation software systems. We have examined cloning both within each system and across ... -
Üniversite dış mekânları i̇çin zaman-mekânsal haritalama yöntemine dayanan bir kullanım sonrası değerlendirme modeli
(Yildiz Technical University Faculty Of Architecture, 2020)Üniversite yerleşkeleri yalnızca çeşitli sosyal ve eğitsel binalardan oluşmakla kalmaz, dış mekânları ve donatıları, rekreasyon ve peyzaj alanları ile bütünleşik bir kurgu oluştururlar. Dış mekânlar, yerleşke kullanıcılarının ... -
Variability viewpoint for introducing variability in software architecture viewpoints
(ACM, 2012)Variability is the ability of a software system to be changed for a specific context, in a preplanned manner. As such, to facilitate the instantiation of a software architecture the variability concern needs to be explicitly ... -
Variational bayesian multiple instance learning with gaussian processes
(IEEE, 2017)Gaussian Processes (GPs) are effective Bayesian predictors. We here show for the first time that instance labels of a GP classifier can be inferred in the multiple instance learning (MIL) setting using variational Bayes. ...
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