South African Tuberculosis Vaccine Initiative
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Using computational tools and a corpus lexicography framework in developing an isiZulu LSP Dictionary.
Full text linkWe propose compiling an isiZulu Language for Special Purposes (LSP) dictionary to be deployed and made available through an online software interface. This study develops alongside the much needed studies on open-source code for creating dictionaries and on the work of writing online dictionaries for languages like isiZulu
Logics for Conceptual Data Modelling: A Review
Full text linkInformation modelling for databases and object-oriented information systems avails of conceptual data modelling languages such as EER and UML Class Diagrams. Many attempts exist to add logical rigour to them, for various reasons and with disparate strengths. In this paper we aim to provide a structured overview of the many efforts. We focus on aims, approaches to the formalisation, including key dimensions of choice points, popular logics used, and the main relevant reasoning services. We close with current challenges and research directions
DeadWood: Including Disturbance and Decay in the Depiction of Digital Nature
Full text linkThe creation of truly believable simulated natural environments remains an unsolved problem in Computer Graphics. This is, in part, due to a lack of visual variety. In nature, apart from variation due to abiotic and biotic growth factors, a significant role is played by disturbance events, such as fires, windstorms, disease, and death and decay processes, which give rise to both standing dead trees (snags) and downed woody debris (logs). For instance, snags constitute on average 10% of unmanaged forests by basal area, and logs account for 2 (frac12) times this quantity.While previous systems have incorporated individual elements of disturbance (e.g., forest fires) and decay (e.g., the formation of humus), there has been no unifying treatment, perhaps because of the challenge of matching simulation results with generated geometric models.In this paper, we present a framework that combines an ecosystem simulation, which explicitly incorporates disturbance events and decay processes, with a model realization process, which balances the uniqueness arising from life history with the need for instancing due to memory constraints. We tested our hypothesis concerning the visual impact of disturbance and decay with a two-alternative forced-choice experiment (n = 116). Our findings are that the presence of dead wood in various forms, as snags or logs, significantly improves the believability of natural scenes, while, surprisingly, general variation in the number of model instances, with up to 8 models per species, and a focus on disturbance events, does not
A Systematic Analysis of Subwords and Cross-Lingual Transfer in Multilingual Translation
Full text linkMultilingual modelling can improve machine translation for low-resource languages, partly through shared subword representations. This paper studies the role of subword segmentation in cross-lingual transfer. We systematically compare the efficacy of several subword methods in promoting synergy and preventing interference across different linguistic typologies. Our findings show that subword regularisation boosts synergy in multilingual modelling, whereas BPE more effectively facilitates transfer during cross-lingual fine-tuning. Notably, our results suggest that differences in orthographic word boundary conventions (the morphological granularity of written words) may impede cross-lingual transfer more significantly than linguistic unrelatedness. Our study confirms that decisions around subword modelling can be key to optimising the benefits of multilingual modelling
O-Antigen decorations in Salmonella enterica play a key role in eliciting functional immune responses against heterologous serovars in animal models
Full text linkIntroduction: Different serovars of Salmonella enterica cause systemic diseases in humans including enteric fever, caused by S. Typhi and S. Paratyphi A, and invasive nontyphoidal salmonellosis (iNTS), caused mainly by S. Typhimurium and S. Enteritidis. No vaccines are yet available against paratyphoid fever and iNTS but different strategies, based on the immunodominant O-Antigen component of the lipopolysaccharide, are currently being tested. The O-Antigens of S. enterica serovars share structural features including the backbone comprising mannose, rhamnose and galactose as well as further modifications such as O-acetylation and glucosylation. The importance of these O-Antigen decorations for the induced immunogenicity and cross-reactivity has been poorly characterized.
Methods: These immunological aspects were investigated in this study using Generalized Modules for Membrane Antigens (GMMA) as delivery systems for the different O-Antigen variants. This platform allowed the rapid generation and in vivo testing of defined and controlled polysaccharide structures through genetic manipulation of the O-Antigen biosynthetic genes.
Results: Results from mice and rabbit immunization experiments highlighted the important role played by secondary O-Antigen decorations in the induced immunogenicity. Moreover, molecular modeling of O-Antigen conformations corroborated the likelihood of cross-protection between S. enterica serovars.
Discussion: Such results, if confirmed in humans, could have a great impact on the design of a simplified vaccine composition able to maximize functional immune responses against clinically relevant Salmonella enterica serovars
FastFlow: GPU Acceleration of Flow and Depression Routing for Landscape Simulation
Full text linkTerrain analysis plays an important role in computer graphics, hydrology and geomorphology. In particular, analyzing the path of material flow over a terrain with consideration of local depressions is a precursor to many further tasks in erosion, river formation, and plant ecosystem simulation. For example, fluvial erosion simulation used in terrain modeling computes water discharge to repeatedly locate erosion channels for soil removal and transport. Despite its significance, traditional methods face performance constraints, limiting their broader applicability. In this paper, we propose a novel GPU flow routing algorithm that computes the water discharge in O(log n) iterations for a terrain with n vertices (assuming n processors). We also provide a depression routing algorithm to route the water out of local minima formed by depressions in the terrain, which converges in O(log 2 n) iterations. Our implementation of these algorithms leads to a 5× speedup for flow routing and 34 × to 52 × speedup for depression routing compared to previous work on a 10242 terrain, enabling interactive control of terrain simulation
Deep Learning Classification for Encrypted Botnet Traffic: Optimising Model Performance and Resource Utilisation
Full text linkDetection of malicious traffic on a network is critical to
ensuring the safety and security of internet systems. Classical approaches
to this task increasingly struggle with modern networking procedures,
like encryption. Deep learning (DL) offers an alternative approach to
traffic classification problems. We address two major problem classes: (1)
botnet detection and (2) botnet family classification. For each problem,
we explore five implementations of DL architectures: a multi-layer perceptron
(MLP), shallow and deep convolutional neural network (CNN v1
and CNN v2), an autoencoder (AE) and an autoencoder + convolutional
neural network (AE+CNN). Our evaluation of models for each respective
problem class is based on the classification performance and computational
requirements of each model. We further investigate the effect of
training the models on an input with a reduced feature space, where we
evaluate the impact this has in terms of a trade-off between computational
and classification performance. For botnet detection, we find that
all models attain good (≥0.979 accuracy) classification performance on
a normal testing set; however, this performance drops fairly substantially
when evaluated on a set of unknown botnet families. Furthermore,
we observed a clear trend between increased feature space and memory
utilisation, while finding no evidence of a trend between inference
time and feature space. For botnet classification, we found that models
which implement CNN architectures outperform others by a substantial
margin (≈6 percentage points). We observe the same trend between feature
space and memory utilisation, and absence of apparent relationship
between feature space and inference time
Name2Cat: A Lightweight Autonomous Systems Classifier Using Organization Names
Full text linkAbstract—The Internet’s backbone consists of Autonomous
Systems (ASes), each typically managed by a single organisation
and providing a related set of services. Accurate AS classification
is crucial for understanding various aspects of Internet
infrastructure, including network performance and the economic
behaviours of the organisations that manage them. This granular
insight is invaluable for network operators and researchers alike.
In this paper, we propose a lightweight method for classifying
ASes based solely on the names of the ASes and their owning
organisations. By employing text feature extraction techniques,
we convert these names into numerical features suitable for
machine learning models. Our approach achieves an overall
accuracy of 80%, with F1-scores ranging from 70% to 92% across six different categories. The method performs particularly well in categories with distinctive naming conventions, which aid classification while facing challenges in categories like Transit that have less distinctive naming patterns. Although our approach uses fewer categories than the 17 found in the state-of-the-art ASdb system, which relies on a mix of public and proprietary datasets to achieve accuracies between 75% and 93%, it offers a quick and resource-efficient solution for AS classification when detailed AS information is unavailable
Understanding How Parents Deal With the Health Advice They Receive: A Qualitative Study and Implications for the Design of Message-based Health Dissemination Systems for Child Health
Full text linkMessage-based health information dissemination systems can potentially improve maternal and child health (MCH). By conveying health information to parents, SMS- and chatbot-based systems can support parents’ learning and empower them to make better health decisions for their children. However, there is limited design advice for creating message-based dissemination systems for MCH. To help address this gap, we conducted 14 participatory workshops with 42 parents from Portugal and South Africa, exploring how parents learned to care for their children’s health. Our findings showed how parents reflected on the health advice they received, by assessing the fit of the advice to their child’s characteristics, their values and beliefs, the advice’s feasibility, or the intention and competence of the advice giver. Based on these insights, we propose four design implications for creating message-based health information dissemination systems tailored to parents and their children
Body and Brain Quality-Diversity in Robot Swarms
Full text linkIn biological societies, complex interactions between the behavior and morphology of evolving organisms and their environment have given rise to a wide range of complex and diverse social structures. Similarly, in artificial counterparts such as swarm-robotics systems, collective behaviors emerge via the interconnected dynamics of robot morphology (sensorymotor configuration), behavior (controller), and environment (task). Various studies have demonstrated morphological and behavioral diversity enables biological groups to exhibit adaptive, robust, and resilient collective behavior across changing environments. However, in artificial (swarm robotic) systems there is little research on the impact of changing environments on morphological and behavioral (body-brain) diversity in emergent collective behavior, and the benefits of such diversity. T his study uses evolutionary collective robotics as an experimental platform to investigate the impact of increasing task environment complexity (collective behavior task difficulty) on the evolution and benefits of morphological and behavioral diversity in robotic swarms. Results indicate that body-brain evolution using coupled behavior and morphology diversity maintenance yields higher behavioral and morphological diversity, which is beneficial for collective behavior task performance across task environments. Results also indicate that such behavioral and morphological diversity maintenance coupled with body-brain evolution produces neuro-morpho complexity that does not increase concomitantly with task complexity