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An in-depth understanding of uncertainty is the first step to making effective decisions under uncertainty. Machine/deep learning (ML/DL) has been hugely leveraged to solve complex problems involved with processing high-dimensional data. However, ...

• Identifying the causes and types of uncertainty in various belief models.
• Uncertainty reasoning and quantification of belief models for decision-making.
• Applying uncertainty quantification of belief models into deep learning.
• ...

Domain adaptation involves adapting a model trained on one domain to work effectively on another, which can have different statistical properties, such as distributions, correlations, and relationships between features. These heterogeneities can ...

• A new dual-domain adaptation method is introduced that can be used in noisy and uncertain environments.
• By incorporating a belief theory, a weighting strategy has been introduced to quantify the uncertainty in the domains.
• Noise ...

Domain adaptation is a representative problem in transfer learning, which aims to tackle the problem of insufficient labeled data in a target domain by exploiting discriminant information from a labeled source domain. Since the source and target ...

Unified Modeling Language (UML) is not a design method but rather a graphic language to represent and communicate the different aspects of an information system. Among the most important research areas in information systems is the enrichment of ...

Automatic deception detection is an important task that has gained a huge interest in different fields due to its potential applications. Particularly, it can improve justice and security in society by helping in detecting deceivers in high-stakes ...

First order logic lies at the core of many methods in mathematics, philosophy, linguistics, and computer science. Although important efforts have been made to extend first order logic to the task of handling uncertainty, existing solutions are sometimes ...

Building a safety case is a common approach to make expert judgement explicit about safety of a system. The issue of confidence in such argumentation is still an open research field. Providing quantitative estimation of confidence is an interesting ...

A major challenge in real world object detection for Video Surveillance (VS) is the dynamic nature of environmental conditions with respect to illumination, visibility, weather change, etc. With increasing availability of cameras and other sensor ...

The problem of fusing beliefs in the Dempster-Shafer belief theory has attracted considerable attention over the last two decades. The classical Dempster's rule has often been criticised, and many alternative rules for belief fusion have been proposed ...

The technology exists for the migration of healthcare data from its archaic paper-based system to an electronic one, and, once in digital form, to be transported anywhere in the world in a matter of seconds. The advent of universally accessible ...

Based on the neurobiological and cognitive principles of human information processing, we develop a system for the automatic visual identification and exploration of scenes. The system architecture consists of three layers: a bottom-up feature ...

Merging and integrating different conceptual models which have been collaboratively developed by domain experts and analysts with dissimilar perspectives on the same issue has been the subject of tremendous amount of research. In this paper, we focus on ...

This paper presents a road selection strategy for novel road-matching methods that are designed to support real-time navigational features within Advanced Driving-Assistance Systems (ADAS). Selecting the most likely segment(s) is a crucial issue for the ...