Enhancing home based care for HIV patients using an advisory expert system

South Africa has one of the highest Human Immunodeficiency Virus (HIV) prevalence rates in the world. People living with HIV/AIDS experience many unrelieved symptoms. Nutritional care and support are important in preventing development of nutritional deficiencies. Home remedies can extend and improve the quality of their lives. Home remedies treatment involves eating healthy food, avoiding certain types of foods, psychological and emotional support and practicing hygiene to avoid skin infections (Sizani, Bandile; Nikiwe 2012). HIV/AIDS treatment and management strategies require ongoing management and support. In this research, we work with people from a clinic in Gugulethu Township in Western Cape, South Africa. The area has high prevalence of HIV (Ministry of health South Africa 2011). Most of the HIV patients in this area access medical information by walking long distances to the clinic. Most of these patients are poor and sometimes cannot afford to visit the clinic regularly for medical advice. In this township there is scarcity of health care workers (HCWs). The HCWs toil on many fronts to meet the enormous demand for the HIV/AIDS services but they are not able to meet the patients' needs. The aim of this research is to empower HIV-patients to self-manage the HIV-related symptoms which they experience. We investigated the way in which the HCWs deliver information to the patients. We interviewed the patients to understand what measures they take to manage the symptoms which they experienced. Consequently, we developed an advisory expert system to enhance Home-Based Care for HIV patients. An advisory expert system is defined as a computing system which is capable of representing and reasoning about some knowledge–rich domain, with a view to solving problems and giving advice (Gustafson et al. 1994). Since South Africa has high mobile phone penetration and most of the patients own them, we opted to use mobile phone as a tool to access the information provided by the advisory expert system. The system was then deployed at the clinic. We trained both HCWs and patients how to use the system. The findings were captured and reported after a six month deployment of the system. The results show that our system can be used as an effective tool to disseminate nutritional and psychological support information to HIV- patients in Gugulethu. The system is simple, yet practical. It helps the patients to self-manage the HIV-related symptoms which they experienced and at the same time, saves time and cost for both HCWs and the patients

Producing Decisions and Explanations: A Joint Approach Towards Explainable CNNs

Deep Learning models, in particular Convolutional Neural Networks, have become the state-of-the-art in different domains, such as image classification, object detection and other computer vision tasks. However, despite their overwhelming predictive performance, they are still, for the most part, considered black-boxes, making it difficult to understand the reasoning behind their outputted decisions. As such, and with the growing interest in deploying such models into real world scenarios, the need for explainable systems has arisen. Therefore, this dissertation tries to mitigate this growing need, by proposing a novel CNN architecture, composed of an explainer and a classifier. The network, trained end-to-end, constitutes an in-model explainability method, that not only outputs decisions as well as visual explanations of what the network is focusing on to produce such decisions

Protocol for a Systematic Literature Review on Security-related Research in Ubiquitous Computing

Context: This protocol is as a supplementary document to our review paper that investigates security-related challenges and solutions that have occurred during the past decade (from January 2003 to December 2013). Objectives: The objective of this systematic review is to identify security-related challenges, security goals and defenses in ubiquitous computing by answering to three main research questions. First, demographic data and trends will be given by analyzing where, when and by whom the research has been carried out. Second, we will identify security goals that occur in ubiquitous computing, along with attacks, vulnerabilities and threats that have motivated the research. Finally, we will examine the differences in addressing security in ubiquitous computing with those in traditional distributed systems. Method: In order to provide an overview of security-related challenges, goals and solutions proposed in the literature, we will use a systematic literature review (SLR). This protocol describes the steps which are to be taken in order to identify papers relevant to the objective of our review. The first phase of the method includes planning, in which we define the scope of our review by identifying the main research questions, search procedure, as well as inclusion and exclusion criteria. Data extracted from the relevant papers are to be used in the second phase of the method, data synthesis, to answer our research questions. The review will end by reporting on the results. Results and conclusions: The expected results of the review should provide an overview of attacks, vulnerabilities and threats that occur in ubiquitous computing and that have motivated the research in the last decade. Moreover, the review will indicate which security goals are gaining on their significance in the era of ubiquitous computing and provide a categorization of the security-related countermeasures, mechanisms and techniques found in the literature. (authors' abstract)Series: Working Papers on Information Systems, Information Business and Operation

Contributions to speech processing and ambient sound analysis

We are constantly surrounded by sounds that we continuously exploit to adapt our actions to situations we are facing. Some of the sounds like speech can have a particular structure from which we can infer some information, explicit or not. This is one reason why speech is possibly that is the most intuitive way to communicate between humans. Within the last decade, there has been significant progress in the domain of speech andaudio processing and in particular in the domain of machine learning applied to speech and audio processing. Thanks to these progresses, speech has become a central element in many human to human distant communication tools as well as in human to machine communication systems. These solutions work pretty well on clean speech or under controlled condition. However, in scenarios that involve the presence of acoustic perturbation such as noise or reverberation systems performance tends to degrade severely. In this thesis we focus on processing speech and its environments from an audio perspective. The algorithms proposed here are relying on a variety of solutions from signal processing based approaches to data-driven solutions based on supervised matrix factorization or deep neural networks. We propose solutions to problems ranging from speech recognition, to speech enhancement or ambient sound analysis. The target is to offer a panorama of the different aspects that could improve a speech processing algorithm working in a real environments. We start by describing automatic speech recognition as a potential end application and progressively unravel the limitations and the proposed solutions ending-up to the more general ambient sound analysis.Nous sommes constamment entourés de sons que nous exploitons pour adapter nos actions aux situations auxquelles nous sommes confrontés. Certains sons comme la parole peuvent avoir une structure particulière à partir de laquelle nous pouvons déduire des informations, explicites ou non. C’est l’une des raisons pour lesquelles la parole est peut-être le moyen le plus intuitif de communiquer entre humains. Au cours de la décennie écoulée, des progrès significatifs ont été réalisés dans le domaine du traitement de la parole et du son et en particulier dans le domaine de l’apprentissage automatique appliqué au traitement de la parole et du son. Grâce à ces progrès, la parole est devenue un élément central de nombreux outils de communication à distance d’humain à humain ainsi que dans les systèmes de communication humain-machine. Ces solutions fonctionnent bien sur un signal de parole propre ou dans des conditions contrôlées. Cependant, dans les scénarios qui impliquent la présence de perturbations acoustiques telles que du bruit ou de la réverbération les performances peuvent avoir tendance à se dégrader gravement. Dans cette HDR, nous nous concentrons sur le traitement de la parole et de son environnement d’un point de vue audio. Les algorithmes proposés ici reposent sur une variété de solutions allant des approches basées sur le traitement du signal aux solutions orientées données à base de factorisation matricielle supervisée ou de réseaux de neurones profonds. Nous proposons des solutions à des problèmes allant de la reconnaissance vocale au rehaussement de la parole ou à l’analyse des sons ambiants. L’objectif est d’offrir un panorama des différents aspects qui pourraient être améliorer un algorithme de traitement de la parole fonctionnant dans un environnement réel. Nous commençons par décrire la reconnaissance automatique de la parole comme une application finale potentielle et analysons progressivement les limites et les solutions proposées aboutissant à l’analyse plus générale des sons ambiants

Enhanced Capsule-based Networks and Their Applications

Current deep models have achieved human-like accuracy in many computer vision tasks, even defeating humans sometimes. However, these deep models still suffer from significant weaknesses. To name a few, it is hard to interpret how they reach decisions, and it is easy to attack them with tiny perturbations. A capsule, usually implemented as a vector, represents an object or object part. Capsule networks and GLOM consist of classic and generalized capsules respectively, where the difference is whether the capsule is limited to representing a fixed thing. Both models are designed to parse their input into a part-whole hierarchy as humans do, where each capsule corresponds to an entity of the hierarchy. That is, the first layer finds the lowest-level vision patterns, and the following layers assemble the larger patterns till the entire object, e.g., from nostril to nose, face, and person. This design enables capsule networks and GLOM the potential of solving the above problems of current deep models, by mimicking how humans overcome these problems with the part-whole hierarchy. However, their current implementations are not perfect on fulfilling their potentials and require further improvements, including intrinsic interpretability, guaranteed equivariance, robustness to adversarial attacks, a more efficient routing algorithm, compatibility with other models, etc. In this dissertation, I first briefly introduce the motivations, essential ideas, and existing implementations of capsule networks and GLOM, then focus on addressing some limitations of these implementations. The improvements are briefly summarized as follows. First, a fast non-iterative routing algorithm is proposed for capsule networks, which facilitates their applications in many tasks such as image classification and segmentation. Second, a new architecture, named Twin-Islands, is proposed based on GLOM, which achieves the many desired properties such as equivariance, model interpretability, and adversarial robustness. Lastly, the essential idea of capsule networks and GLOM is re-implemented in a small group ensemble block, which can also be used along with other types of neural networks, e.g., CNNs, on various tasks such as image classification, segmentation, and retrieval

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

Migrating Words, Migrating Merchants, Migrating Law

Migrating Words, Migrating Merchants, Migrating Law, edited by S. Gialdroni, A. Cordes, S. Dauchy, D. De ruysscher and H. Pihlajamäki, offers a transdisciplinary account of the connections between merchants’ journeys, the languages they used and the development of commercial law. ; Readership: Scholars interested in commercial law history, economic history, history of linguistics, translation and, more generally, in the lives and travels of merchants and the impact they had on the development of law