Pan-African Artificial Intelligence, Communication, Computing, and Smart Systems Conference
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    • PAAISS 2024, Durban, South Africa
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Panel Discussions and Presentations

GROWING AND MENTORING EARLY CAREER ACADEMICS IN AFRICAN UNIVERSITIES

Attahiru S. Alfa

University of Manitoba and University of Pretoria

 

ABSTRACT

African universities are extremely rich in top quality human academic resources.  For African universities to remain and continue to be very competitive internationally, and for their own local growth, several aspects need to be addressed with regards to growing and mentoring their early career academics (ECA).  One of the aims of this workshop is to discuss the extent to which their needs are currently been met and suggest how to facilitate and strengthen how the systems can improve their abilities to meet them at very high level.  Most ECAs would be reasonably comfortable in a system that lets them grow professionally in a direction they please, while meeting the needs of the universities.  The second aim of this workshop is to determine how African universities should structure or restructure themselves in order to make their universities attractive for ECAs and students.  Finally, a mentoring system that helps the ECAs grow and enrich their experience which is key to a successful university system will be discussed in depth.  This workshop would address these issues and related issues that arise from the workshop.  Dr. Alfa, the chair of this workshop, will introduce the subject and give a general overview of the issues and how to proceed.  He will then invite the panel members to discuss the following topics:

  • The current situation of the ECA in African universities
  • How to attract ECAs to African universities and grow them to international levels
  • How to mentor the ECAs in order to make them very successful so they feel fulfilled
  • What incentives are needed for the ECAs in order to achieve the identified goals
  • How to foster research collaboration among all levels of academics locally, within the continent, and internationally.

 

DURATION OF WORKSHOP:  3 hours to Half day

TARGET GROUP:  Senior academic administrators and early career academics

SPEAKERS:

  1. Main speaker is Attahiru Alfa, and other speakers who would like to contribute to this workshop are strongly encouraged to contact the organizers or  Attahiru Alfa at Attahiru.alfa@umanitoba.ca
  2. Panel members:
    1. Professor Jules-Raymond Tapamo, University of Kwazulu-Natal, Durban, South Africa. Email: tapamo@ukzn.ac.za.
    2. Professor Telex Ngatched, Memorial University, Grenfell Campus, Canada. Email: tngatched@grenfell.mun.ca.
    3. Professor Moussa LO, Universite Virtuelle du Senegal – UVS, Dakar, Senegal. Email: moussa.lo@uvs.edu.sn

 

 

 

 


 

ADVANCES IN NLP AND THEIR APPLICATIONS TO HEALTHCARE

Prof. Ndapa Nakashole

ABSTRACT

Recent advances in Natural Language Processing (NLP) have propelled the state of the art to new highs. One such advance is the use of external memory to support reasoning in deep learning models such as Transformers. Without external memory to store sufficient background knowledge, reasoning in NLP systems must be performed based on limited information leading to poor performance on knowledge-rich tasks. Conversely, NLP systems with access to external memory have resulted in significant performance gains on many important tasks including question answering (QA) and other tasks associated with QA such as fact verification, and entity linking. This talk will present: 1) an overview of state-of-the-art approaches for representing background knowledge in addressable memory, and 2) applications in the healthcare domain.

 

 


 

ULTRASOUND IMAGE FORMATION IN THE DEEP LEARNING AGE

Prof. Muyinatu Bel

Associate Professor of Electrical and Computer Engineering, Biomedical Engineering, and Computer Science

Johns Hopkins University

 

ABSTRACT

The success of diagnostic and interventional medical procedures is deeply rooted in the ability of modern imaging systems to deliver clear and interpretable information. After raw sensor data is received by ultrasound and photoacoustic imaging systems in particular, the beamforming process is often the first line of software defense against poor quality images. Yet, with today’s state-of-the-art beamformers, ultrasound and photoacoustic images remain challenged by channel noise, reflection artifacts, and acoustic clutter, which combine to complicate segmentation tasks and confuse overall image interpretation. These challenges exist because traditional beamforming and image formation steps are based on flawed assumptions in the presence of significant inter- and intrapatient variations.

 In this talk, I will introduce the PULSE Lab’s novel alternative to beamforming, which improves ultrasound and photoacoustic image quality by learning from the physics of sound wave propagation. We replace traditional beamforming steps with deep neural networks that only display segmented details, structures, and physical properties of interest. I will then transition to describing a new resource for the entire community to standardize and accelerate research at the intersection of ultrasound beamforming and deep learning. This resource is a direct outcome of the 2020 Challenge on Ultrasound Beamforming with Deep Learning, with key landmarks that include the first internationally crowd-sourced database of raw ultrasound channel data and integrated beamforming and evaluation code.

 

 


 

BIO-INSPIRED SMART INFRARED IMAGE SENSOR FOR TIME-CRITICAL APPLICATIONS

Prof. Christophe Bobda

ABSTRACT

We present a novel architecture of an image sensor that enables sensor-level knowledge inference. The architecture addresses the challenge of future gigapixel image sensors by pushing knowledge inference at the source of image data. Region-based processing is performed to address the backend reconstruction and inference challenges of event cameras. Instead of a stream of events being sent to the back-end for reconstruction, events are not computed at pixel-level as difference of intensity like in traditional event cameras, but as regional level, as a results of information relevance. With the level-based information processing of the brain, the ideal technology for the proposed approach is a 3-D stacked silicon image sensor, where the top layer illuminates the imager, and the bottom layer contains the readout electronics. A multi-layer computational structure before the readout electronics to make the imager an active device. This structure identifies the high-level information of an image and transfers only the resulting salient information to the inference engine that can reside on the same device, or on a separated device in stacked silicon technology.

 

@ 2025 Pan-African Artificial Intelligence and Smart Systems Conference