Zamzam won the award for the third place in the Best Student Paper category at the World Congress on Medical Informatics in Sydney for the paper “Baseline Survey on Referrals and Healthcare Provider Needs in View for an Electronic Referral System” authored by Zamzam Kalume, Bart Jansen, Marc Nyssen, Jan Cornelis and Frank Verbeke .

On October 25th 2024 at 16:00, Yuqing Yang will defend their PhD entitled “CRAFTING EFFECTIVE VISUAL EXPLANATIONS BY ATTRIBUTING THE IMPACT OF DATASETS, ARCHITECTURES AND DATA COMPRESSION TECHNIQUES”.

Everybody is invited to attend the presentation in room D.2.01 or online via this link.

Explainable Artificial Intelligence (XAI) plays an important role in modern AI research, motivated by the desire for transparency and interpretability within AI-driven decision-making. As AI systems become more advanced and complicated, it becomes increasingly important to ensure they are reliable, responsible, and ethical. These imperatives are particularly acute in domains where stakes are high, such as medical diagnostics, autonomous driving, and security frameworks.

In computer vision, XAI aims to provide understandable, straightforward explanations for AI model predictions, allowing users to grasp the decision-making processes of these complex systems. Visualizations such as saliency maps are frequently employed to identify input data regions significantly impacting model predictions, thus enhancing user understanding of AI visual data analysis. However, there are still concerns about the effectiveness of visual explanations, especially regarding their robustness, trustworthiness, and human-friendliness.

Our research aims to advance this field by evaluating how various factors—such as the diversity of datasets, the architecture of models, and techniques for data compression—influence the effectiveness of visual explanations in AI applications. Through thorough analysis and careful refinement, we strive to enhance these explanations, ensuring they are both highly informative and accessible to users in diverse XAI applications.

During our evaluation process, we conduct a detailed investigation using both automatic metrics and subjective evaluation methods to assess the effectiveness of visual explanations thoroughly. Automatic metrics, such as task performance and localization accuracy, provide quantifiable measures of the effectiveness of these explanations in real-world scenarios. For subjective evaluation, we have developed a framework named SNIPPET, which enables a detailed and user-oriented assessment of visual explanations. Additionally, our research explores how these objective metrics correlate with subjective human judgments, aiming to integrate quantitative data with the more nuanced, qualitative feedback from users. Ultimately, our goal is to provide comprehensive insights into the practical aspects of XAI methodologies, particularly focusing on their implementation in the field of computer vision.

Looking for ways to scale your current and future IT needs? Thoughts about Cloud migration and how to best balance pros and cons? Eager to learn how open-source technology fits in?

Join the TETRA OpenCloudEdge seminar, which will take place Thursday afternoon, 24 February, in a hybrid physical & online format.

Industry and academic speakers will present experience with OpenStack, Kubernetes and related open-source cloud technologies. They will discuss advantages and pitfalls as well as presenting opportunities.

The physical venue location is DSP Valley, Esperantolaan 4, 3001 Leuven. A link for online participation will be sent to registrants.

Find out more about the program and register: https://share.hsforms.com/1YhpS0Nt_RpmUxV1Zzh2XAw42q9f

AIOTI is organising a Web3 Hackathon on 21/22 September 2023 in Brussels and online.

Please feel free to register and share this information further within your communities.

About the Hackathon:

The WEB3 HACKATHON is a collaborative event with a mixed crowd of students, professionals and authorities, moderated by domain experts. The event is accessible for every individual, public or private organisations willing to enter the new digital era.

The Hackathon target is to solve a series of challenges, using WEB3 Technology:

• Industry  – Challenge: Use WEB3 to TACKLE CLIMATE EMMERGENCY
• Authorities  – Challenge: Use WEB3 to SOLVE A GOVERNANCE PROBLEM
• Media – Challenge: Use WEB3 to TACKLE FAKE NEWS
• Education – Challenge: Use WEB3 to INNOVATE EDUCATION

• Foundation of IMEC – spirit at that time “If you cannot beat them join them”.
• The year of the first IBM PC, the first Apple Macintosh and the period of the floppy disc.
• Second PhD in semiconductor technology on ii-V transistors in collaboration with Bell Laboratories USA (Roger Vounckx).

On March 12 2021 at 16.00 Evgenia Papavasileiou will defend her PhD entitled “Towards more Efficient NeuroEvolution: Application on Feature Selection and Classification Problems”.

NeuroEvolution (NE) is a sub-field of Artificial Intelligence whose purpose is to optimize Artificial Neural Networks (ANNs) by modeling the biological evolutionary process. NeuroEvolution of Augmenting Topologies (NEAT) that evolves the topology and the connectivity weights of the ANNs, is one of the most influential algorithms in the field. This PhD performs different studies on NEAT extensions, namely FD-NEAT, FS-NEAT and HA-NEAT and proposes new extensions so that the resulting methods could require fewer generations, evolve smaller and less complex networks and scale on complex problems.
After the publication of NEAT in 2002 many methods have appeared that extend its functionality in various ways. In this PhD, a systematic review is performed to identify and categorize the NEAT’s successors. The proposed clustering scheme can support researchers 1) understanding the current state of the art that will enable them 2) exploring new research directions or 3) benchmarking their proposed method to the state of the art, if they are interested in comparing, and 4) positioning themselves in the domain or 5) selecting a method that is most appropriate for their problem. In addition, different studies are conducted to achieve important intermediate stepping stones. The first set of investigations concern design choices in the initial topologies of two NEAT extensions, namely FD-NEAT and FS-NEAT. These include the introduction of a hidden layer in the initial topologies, the initialization of the topologies with a different connectivity setting and the employment of different activation functions in the output layer. Additionally, BS-HA-NEAT and BS-NEAT are proposed as new extensions of HA-NEAT and NEAT, that perform speciation in the behavioral rather than in the genotypic space. It is found that BS-HA-NEAT and BS-NEAT outperform HA-NEAT and NEAT solving previously unsolvable problems or improving the accuracy and reducing the complexity of the evolved networks. Furthermore, HA-FD-NEAT, extending both HA-NEAT and FD-NEAT is proposed. This is able to evolve the topology, the connectivity weights and the activation functions of ANNs while identifying the relevant features. HA-FD-NEAT outperforms HA-NEAT and performs as good as FD-NEAT. Also, BS-HA-FD-NEAT is proposed as an extension to HA-FD-NEAT by performing speciation in the behavioral space. BS-HA-FD-NEAT outperforms its ancestor by evolving significantly smaller networks. In overall, the resulting algorithm outperforms its ancestors, NEAT, FD-NEAT, and HA-NEAT achieving better accuracy, in fewer generations and evolving smaller and less complex networks. Finally, BS-HA-FD-NEAT is tested on a complex, real world application of reducing the false positives outputed from a detector of abnormal COVID-19 related findings from lung Computer Tomography (CT) images.