Videos

How to do research

Introduction to the course
Research Methodology Practice (20:59)
09/02/2022
The KnowDive starts a new course, Research Methodology Practice, given by Prof. Fausto Giunchiglia. The first lecture introduced the course organization and the “WHAT, HOW, and WHY” in research.
Find the slide

More videos on this topic

Computer Vision

Xiaolei Diao – Incremental image labeling via Human-in-the-loop iterative refinement (40:07)
23/11/2022
Recent work in data-driven machine learning has discussed various types of systematic flaws in image benchmark datasets. In particular, the existence of the semantic gap problem leads to a many-to-many mapping between the information extracted from an image and its linguistic description. This unavoidable bias further leads to poor performance on current computer vision tasks. To address this issue, we introduce a Knowledge Representation (KR)-based methodology to provide guidelines driving the labeling process, thereby indirectly introducing intended semantics in ML models. Specifically, a novel human-in-the-loop iterative refinement process is proposed to optimize data labeling by organizing objects in a classification hierarchy according to their visual properties, ensuring that they are aligned with their linguistic descriptions. Preliminary results verify the effectiveness of the proposed method.
Slides

More videos on this topic

Computational Linguistics

Nandu Chandran Nair – IndoUKC: a Concept-Centered Indian Multilingual Lexical Resource
The presentation was given by Nandu Chandrannair at the LREC 2022 conference(16:27)
11/05/2022

More videos on this topic

Human Machine Symbiosis

Xiaoyue Li-A Context Model for Personal Data Streams(09:31)
The Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (APWeb-WAIM)
27/11/2022 Venue: Nanjing China
We propose a model of the situational context of a person and show how it can be used to organize and, consequently, reason about massive streams of sensor data and annotations, as they can be collected from mobile devices, e.g. smartphones, smartwatches or fitness trackers. The proposed model is validated on a very large dataset about the everyday life of one hundred and fifty-eight people over four weeks, twenty-four hours a day.
About APWeb-WAIM
Slides
More videos on this topic

Knowledge Representation

Mayukh Bagchi – Representation Heterogeneity
The First International Workshop on Formal Models of Knowledge Diversity (FMKD), Joint Ontology WOrkshops (JOWO) (29:34)
19/08/2022
(About FMKD)
Slides
More videos on this topic

AI for Healthcare

Gábor Bella-Sharing health data for research: Technical perspective
InteropEHRate Final Conference (14:48)
28/09/2022 Venue:Université de Liège
In the context of the InteropEHRate project, the presentation provided the description of a novel data sharing protocol focused on health data for research purposes. The presentation gives an overview of the whole protocol steps, and it explains how it is supported by a dedicated technological infrastructure. In the end the presentation reports how the Research Data Sharing protocol has been concretely implemented during the InteropEHRate project’s pilots.
About InteropEHRate
Check more IEHR information on Knowdive website
More videos on this topic

Wenet

Fausto Giunchiglia – Internet of us
Wenet Project (1:32)
12/06/2019
Visit Wenet here

More videos on this topic

Other topics

Velu Kumaravel – Adaptable and Robust EEG Bad Channel Detection using Local Outlier Factor (LOF) (29:29)
12/10/2022
Electroencephalogram (EEG) data from the scalp is often contaminated with non-neural artifacts. Bad channel/electrode detection is one of the preliminary steps in cleaning the data. The definition of artifacts differs according to the EEG acquisition settings (i.e., experimental paradigm, population under study, etc.). Recently, we introduced a robust measure based on LOF that handles such inherent differences in the EEG, providing a versatile solution to this problem. In this talk, I will discuss the algorithm and its performance compared to the existing bad channel detection features upon validation in real EEG acquired from newborns, infants, and adults.
Slides
More videos on this topic