Publications related to Semantic network

A semantic model-based systems engineering approach for assessing the operational performance of metal forming process

Dimitrios Kyritsis, Jinzhi Lu, Xiaochen Zheng

Metal Forming is a basic and essential industrial process to provide materials for constructing complex products. To design an efficient metal forming process, the functional requirements and operational performance are two important aspects to be consider ...

Pergamon-Elsevier Science Ltd2024

An aircraft assembly process formalism and verification method based on semantic modeling and MBSE

Dimitrios Kyritsis, Jinzhi Lu, Xiaochen Zheng

The aircraft assembly system is highly complex involving different stakeholders from multiple domains. The design of such a system requires comprehensive consideration of various industrial scenarios aiming to optimize key performance indicators. Tradition ...

Elsevier Sci Ltd2024

Driving and suppressing the human language network using large language models

Martin Schrimpf

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

Nature Portfolio2024

Design ontology for cognitive thread supporting traceability management in model-based systems engineering

Dimitrios Kyritsis, Jinzhi Lu, Yan Yan

Industrial information integration engineering (IIIE) is an interdisciplinary field to facilitate the industrial information integration process. In the age of complex and large-scale systems, model-based systems engineering (MBSE) is widely adopted in ind ...

Elsevier2024

Querying the Digital Archive of Science: Distant Reading, Semantic Modelling and Representation of Knowledge

Alina Volynskaya

The archive of science is a place where scientific practices are sedimented in the form of drafts, protocols of rejected hypotheses and failed experiments, obsolete instruments, outdated visualizations and other residues. Today, just as science goes more a ...

EPFL2024

Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework

Daniel Gatica-Perez

This paper examines how the European press dealt with the no-vax reactions against the Covid-19 vaccine and the dis- and misinformation associated with this movement. Using a curated dataset of 1786 articles from 19 European newspapers on the anti-vaccine ...

ASSOC COMPUTING MACHINERY2023

Class Specific Feature Disentanglement and Text Embeddings for Multi-label Generalized Zero Shot CXR Classification

Seyedbehzad Bozorgtabar

Robustness of medical image classification models is limited by its exposure to the candidate disease classes. Generalized zero shot learning (GZSL) aims at correctly predicting seen and unseen classes and most current GZSL approaches have focused on the s ...

Springer International Publishing Ag2023

KNNs of Semantic Encodings for Rating Prediction

Léo Jules Laugier

This paper explores a novel application of textual semantic similarity to user-preference representation for rating prediction. The approach represents a user's preferences as a graph of textual snippets from review text, where the edges are defined by sem ...

IEEE2023

Detecting Anomalies and Obstacles in Road Scenes

Krzysztof Maciej Lis

We address the problem of segmenting anomalies and unusual obstacles in road scenes for the purpose of self-driving safety. The objects in question are not present in the common training sets as it is not feasible to collect and annotate examples for every ...

EPFL2023

Modeling Structured Data in Attention-based Models

Alireza Mohammadshahi

Natural language processing has experienced significant improvements with the development of Transformer-based models, which employ self-attention mechanism and pre-training strategies. However, these models still present several obstacles. A notable issue ...

EPFL2023

Dense Multitask Learning to Reconfigure Comics

Sabine Süsstrunk, Mathieu Salzmann, Deblina Bhattacharjee

In this paper, we develop a MultiTask Learning (MTL) model to achieve dense predictions for comic panels to, in turn, facilitate the transfer of comics from one publication channel to another by assisting authors in the task of reconfiguring their narrativ ...

IEEE2023

A theory of memory consolidation and synaptic pruning in cortical circuits

Georgios Iatropoulos

Over the course of a lifetime, the human brain acquires an astonishing amount of semantic knowledge and autobiographical memories, often with an imprinting strong enough to allow detailed information to be recalled many years after the initial learning exp ...

EPFL2023

Visual Scene Understanding for Transportation: From Detecting Objects To Relationships

George Adaimi

Transportation, which deals with moving people and goods around, has a clear impact on the economic development of our society and our well-being. Traditionally, transportation was studied and analyzed using expensive sensors, such as induction loops, that ...

EPFL2022

Cognitive twin construction for system of systems operation based on semantic integration and high-level architecture

Dimitrios Kyritsis, Jinzhi Lu, Han Li

With the increasing complexity of engineered systems, digital twins (DTs) have been widely used to support integrated modeling, simulation, and decision-making of the system of systems (SoS). However, when integrating DTs of each constituent system, it is ...

IOS PRESS2022

Attribute Prediction as Multiple Instance Learning

Devis Tuia, Diego Marcos Gonzalez

Attribute-based representations help machine learning models perform tasks based on human understandable concepts, allowing a closer human-machine collaboration. However, learning attributes that accurately reflect the content of an image is not always str ...

2022

Kinesthetic motor-imagery training improves performance on lexical-semantic access

Sylvain Jean-François Harquel, Camille Bonnet

The objective of this study was to evaluate the effect of Motor Imagery (MI) training on language comprehension. In line with literature suggesting an intimate relationship between the language and the motor system, we proposed that a MI-training could imp ...

PUBLIC LIBRARY SCIENCE2022

Semantics-Aware Spatial-Temporal Binaries for Cross-Modal Video Retrieval

Jie Luo, Mengshi Qi, Yiyu Wang

With the current exponential growth of video-based social networks, video retrieval using natural language is receiving ever-increasing attention. Most existing approaches tackle this task by extracting individual frame-level spatial features to represent ...

2021

The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing

Peter Clemens Schlicht

Enabling autonomous driving (AD) can be considered one of the biggest challenges in today?s technology. AD is a complex task accomplished by several functionalities, with environment perception being one of its core functions. Environment perception is usu ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Context-Aware Image Super-Resolution Using Deep Neural Networks

Mohammad Saeed Rad

Image super-resolution is a classic ill-posed computer vision and image processing problem, addressing the question of how to reconstruct a high-resolution image from its low-resolution counterpart. Current state-of-the-art methods have improved the perfor ...

EPFL2021

The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task

James Henderson, Alireza Mohammadshahi

We describe the DCU-EPFL submission to the IWPT 2021 Parsing Shared Task: From Raw Text to Enhanced Universal Dependencies. The task involves parsing Enhanced UD graphs, which are an extension of the basic dependency trees designed to be more facilitative ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021