Browsing Computer Science by Title
Now showing items 2-14 of 14
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A benchmark for inpainting of clothing images with irregular holes
(Springer, 2020)Fashion image understanding is an active research field with a large number of practical applications for the industry. Despite its practical impacts on intelligent fashion analysis systems, clothing image inpainting has ... -
Centrality and scalability analysis on distributed graph of large-scale e-mail dataset for digital forensics
(IEEE, 2020-12-10)Today's digital forensics software tools mostly do not offer automatic analysis methods to reveal evidences among huge amounts of digital files within hard disk images. It is important that finding evidence in digital and ... -
Context based echo state networks for robot movement primitives
(IEEE, 2023)Reservoir Computing, in particular Echo State Networks (ESNs) offer a lightweight solution for time series representation and prediction. An ESN is based on a discrete time random dynamical system that is used to output a ... -
Deep learning-based expressive speech synthesis: a systematic review of approaches, challenges, and resources
(Springer, 2024-02-12)Speech synthesis has made significant strides thanks to the transition from machine learning to deep learning models. Contemporary text-to-speech (TTS) models possess the capability to generate speech of exceptionally high ... -
Deterministic neural illumination mapping for efficient auto-white balance correction
(IEEE, 2023)Auto-white balance (AWB) correction is a critical operation in image signal processors for accurate and consistent color correction across various illumination scenarios. This paper presents a novel and efficient AWB ... -
Effects of agent's embodiment in human-agent negotiations
(ACM, 2023-09-19)Human-agent negotiation has recently attracted researchers’ attention due to its complex nature and potential usage in daily life scenarios. While designing intelligent negotiating agents, they mainly focus on the interaction ... -
EPIoT: Enhanced privacy preservation based blockchain mechanism for internet-of-things
(Elsevier, 2024-01)With the increasing popularity of the Internet of things (IoT) and giving the end users the opportunity of collecting and analyzing the data by these IoT devices give rise to ultimate privacy concern and is attracting ... -
Exploring scaling efficiency of intel loihi neuromorphic processor
(IEEE, 2023)In this paper, we focus on examining how scaling efficiency evolves in winner-take-all (WTA) network models on Intel Loihi neuromorphic processor, as network-related features such as network size, neuron type, and connectivity ... -
International roaming traffic optimization with call quality
(SciTePress, 2019)In this study we focus on a Steering International Roaming Traffic (SIRT) problem with single service that concerns a telecommunication’s operators’ agreements with other operators in order to enable subscribers access ... -
Provenance aware run-time verification of things for self-healing Internet of Things applications
(Wiley, 2019-02-10)We propose a run-time verification mechanism of things for self-healing capability in the Internet of Things domain. We discuss the software architecture of the proposed verification mechanism and its prototype implementations. ... -
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 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. ... -
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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