Depuis quelques décennies, de nombreux immigrants s’établissent au Canada ; une partie d’entre eux s’installent au Québec, surtout à Montréal. Certains de ces immigrants s’orientent vers la profession enseignante, sur la base de l’évaluation comparative de leurs diplômes. En raison de la pénurie d’enseignants à Montréal et de la pluralité ethnoculturelle du territoire, les commissions scolaires situées sur l’île de Montréal font souvent appel à des enseignants formés à l’étranger (EFÉ), notamment en classes d’accueil où ils sont jugés particulièrement pertinents compte tenu de leur connaissance de la migration. Cependant, l’intégration des EFÉ de classe d’accueil soulèverait des défis particuliers étant donné qu’ils n’ont pas toujours pour langue maternelle le français et qu’ils sont peu familiers avec le système éducatif québécois, alors que la transmission de la langue et de la culture constitue les objectifs prioritaires de leur mandat (Grégoire-Labrecque, 2014). Dans ce contexte, leur intégration socioprofessionnelle demande une attention. Néanmoins, la recherche sur cette question a surtout documenté les difficultés qu’ils rencontrent lors de leurs premières années d’exercice dans le milieu d’accueil, de même que les stratégies adoptées pour les surmonter ; peu d’attention a été portée au contexte spécifique dans lequel ils enseignent, tel que celui de la classe d’accueil qui présente des défis particuliers. Ainsi, ce mémoire vise à comprendre comment les EFÉ s’ajustent au travail de la classe d’accueil montréalaise. Les théories du constructivisme de Pépin (1994), de la dramaturgie de Goffman (1973a, 1973b) et de l’action collective de Becker (1982) sont mobilisées pour éclairer ce processus d’ajustement alors conçu comme la reconstruction de leur savoir-faire au coeur de leurs interactions au travail. La recherche sur le terrain s’est réalisée au moyen de neuf entretiens individuels virtuels auprès de trois EFÉ travaillant dans les classes d’accueil montréalaises. Après avoir réalisé une analyse thématique des transcriptions des entretiens, trois processus de reconstruction de leur savoir-faire en classe d’accueil ont été identifiés : la remobilisation des pratiques connues, l’adaptation des pratiques connues mais écartées et l’apprentissage des pratiques inconnues. Une lecture transversale de ces résultats a suscité le besoin d’un éclairage théorique complémentaire pour enrichir l’interprétation de ces processus et du rôle que tiennent les interactions au travail. Ainsi, une théorie interactionniste de la socialisation au travail (Demazière et al., 2019) a permis d’affiner la compréhension par la mise en lumière de quatre modes d’interaction par lesquels s’accomplissent les processus de reconstruction du savoir-faire des participants : la validation, l’ajustement, la confrontation et la tractation. Cette nouvelle analyse met en relief que ces modes d’interaction, qui se déploient autant en classe d’accueil que dans l’école, constituent le moteur de l’adaptation des pratiques des participants en regard des valorisations de la culture de travail dans les écoles montréalaises. / In recent decades, many immigrants have settled in Canada; some of them settle in Quebec, especially in Montreal. Some of these immigrants turn to the teaching profession, based on the comparative evaluation of their diplomas. Due to the shortage of teachers in Montreal and the ethnocultural plurality of the territory, the school boards located on the island of Montreal often call on internationally trained teachers (IETs), particularly in welcoming classes where they are considered particularly relevant given their knowledge of migration. However, the integration of IETs in welcoming class would raise particular challenges given that they do not always have French as their mother tongue and are unfamiliar with the Quebec school system, while the transmission of language and culture constitute the priority objectives of their mandate (Grégoire-Labrecque, 2014). In this context, their socio-professional integration requires attention. However, research on this issue has mostly documented the difficulties that they encounter during their first few years of practice in the host setting, as well as the strategies adopted to overcome them; little attention has been paid to the specific context in which they teach, such as the welcoming class which presents particular challenges. Thus, this research aims to understand how IETs adjust themselves to the work of Montreal welcoming class. Pépin’s theory of constructivism (1994), Goffman’s theory of dramaturgy (1973a, 1973b) and Becker’s theory of collective action (1982) are mobilized to shed light on this adjustment process then conceived as the reconstruction of their expertise at the centre of their interactions at work. The field research was carried out by means of nine virtual individual interviews with three IETs working in Montreal welcoming classes. After carrying out a thematic analysis of the interview transcriptions, three reconstruction processes of their expertise in welcoming class were identified: remobilization of known practices, adaptation of known but deviant practices and learning of unknown practices. A transversal reading of these results aroused the need for additional theoretical insight to enrich the interpretation of these processes and the role that interactions played in the work. Thus, an interactionist theory of socialization at work (Demazière et al., 2019) allowed to refine the understanding by highlighting four modes of interaction through which the reconstruction processes of the participants’ expertise are accomplished: validation, adjustment, confrontation and negotiation. This new analysis emphasizes that these modes of interaction, which appear both in welcoming class and in school, constitute the driving force behind the adaptation of participants’ practices with regard to the valuation of the work culture in Montreal schools.

The interest in using State-Of-The-Art (SOTA) Pre-Trained Language Model (PLM) in product development is growing. The fact that developers can use PLM has changed the way to build reliable models, and it is the go-to method for many companies and organizations. Selecting the Natural Language Processing (NLP) model with the highest accuracy is the usual way of deciding which PLM to use. However, with growing concerns about negative climate changes, we need new ways of making decisions that consider the impact on our future needs. The best solution with the highest accuracy might not be the best choice when other parameters matter, such as sustainable development. This thesis investigates how to calculate an approximate total cost considering Operating Expenditure (OPEX) and CO2~emissions for a deployed NLP solution over a given period, specifically the inference phase. We try to predict the total cost with Floating Point Operation (FLOP) and test NLP models on a classification task. We further present the tools to make energy measurements and examine the metric FLOP to predict costs. Using a bottom-up approach, we investigate the components that affect the cost and measure the energy consumption for different deployed models. By constructing this cost model and testing it against real-life examples, essential information about a given NLP implementation and the relationship between monetary and environmental costs will be derived. The literature studies reveal that the derival of a cost model is a complex area, and the results confirm that it is not a straightforward procedure to approximate energy costs. Even if a cost model was not feasible to derive with the resources given, this thesis covers the area and shows why it is complex by examine FLOP. / Intresset att använda State-Of-The-Art (SOTA) Pre-Trained Language Model (PLM) i produktutveckling växer. Det faktum att utvecklare kan använda PLM har förändrat sättet att träna tillförlitliga modeller på och det är den bästa metoden för många företag och organisationer att använda SOTA Naturlig Språkbehandling (NLP). Att välja NLP-modellen med högsta noggrannhet är det vanliga sättet att bestämma vilken PLM som ska användas. Men med växande oro för miljöförändringar behöver vi nya sätt att fatta beslut som kommer att påverka våra framtida behov. Denna avhandling undersöker hur man beräknar en ungefärlig totalkostnad med hänsyn till Operating Expenditure (OPEX) och CO2~utsläpp för en utplacerad NLP-lösning under en given period, dvs slutledningsfasen. Vi försöker förutspå den totala kostnaden med flyttalsoperationer och testar mot en klassificerings uppgift. Vi undersöker verktygen för att göra mätningar samt variabeln Flyttalsoperationer för att förutspå energiförbrukning.

<p>The most important networks from different domains—such as Computing, Organization, Economic, Social, Academic, and Biology—are networks that change over time. For example, in an organization there are email and collaboration networks (e.g., different people or teams working on a document). Apart from the connectivity of the networks changing over time, they can contain attributes such as the topic of an email or message, contents of a document, or the interests of a person in an academic citation or a social network. Analyzing these dynamic networks can be critical in decision-making processes. For instance, in an organization, getting insight into how people from different teams collaborate, provides important information that can be used to optimize workflows.</p> <p><br></p> <p>Network generative models provide a way to study and analyze networks. For example, benchmarking model performance and generalization in tasks like node classification, can be done by evaluating models on synthetic networks generated with varying structure and attribute correlation. In this work, we begin by presenting our systemic study of the impact that graph structure and attribute auto-correlation on the task of node classification using collective inference. This is the first time such an extensive study has been done. We take advantage of a recently developed method that samples attributed networks—although static—with varying network structure jointly with correlated attributes. We find that the graph connectivity that contributes to the network auto-correlation (i.e., the local relationships of nodes) and density have the highest impact on the performance of collective inference methods.</p> <p><br></p> <p>Most of the literature to date has focused on static representations of networks, partially due to the difficulty of finding readily-available datasets of dynamic networks. Dynamic network generative models can bridge this gap by generating synthetic graphs similar to observed real-world networks. Given that motifs have been established as building blocks for the structure of real-world networks, modeling them can help to generate the graph structure seen and capture correlations in node connections and activity. Therefore, we continue with a study of motif evolution in <em>dynamic</em> temporal graphs. Our key insight is that motifs rarely change configurations in fast-changing dynamic networks (e.g. wedges intotriangles, and vice-versa), but rather keep reappearing at different times while keeping the same configuration. This finding motivates the generative process of our proposed models, using temporal motifs as building blocks, that generates dynamic graphs with links that appear and disappear over time.</p> <p><br></p> <p>Our first proposed model generates dynamic networks based on motif-activity and the roles that nodes play in a motif. For example, a wedge is sampled based on the likelihood of one node having the role of hub with the two other nodes being the spokes. Our model learns all parameters from observed data, with the goal of producing synthetic graphs with similar graph structure and node behavior. We find that using motifs and node roles helps our model generate the more complex structures and the temporal node behavior seen in real-world dynamic networks.</p> <p><br></p> <p>After observing that using motif node-roles helps to capture the changing local structure and behavior of nodes, we extend our work to also consider the attributes generated by nodes’ activities. We propose a second generative model for attributed dynamic networks that (i) captures network structure dynamics through temporal motifs, and (ii) extends the structural roles of nodes in motifs to roles that generate content embeddings. Our new proposed model is the first to generate synthetic dynamic networks and sample content embeddings based on motif node roles. To the best of our knowledge, it is the only attributed dynamic network model that can generate <em>new</em> content embeddings—not observed in the input graph, but still similar to that of the input graph. Our results show that modeling the network attributes with higher-order structures (e.g., motifs) improves the quality of the networks generated.</p> <p><br></p> <p>The generative models proposed address the difficulty of finding readily-available datasets of dynamic networks—attributed or not. This work will also allow others to: (i) generate networks that they can share without divulging individual’s private data, (ii) benchmark model performance, and (iii) explore model generalization on a broader range of conditions, among other uses. Finally, the evaluation measures proposed will elucidate models, allowing fellow researchers to push forward in these domains.</p>

PURPOSE: The purpose of the study was to explore the perception of the R48 trained professional nurses with regard to implementation of the EPI in Tshwane District Gauteng Province. METHODOLOGY: A qualitative research design was followed. Data were collected through in-depth qualitative interviews from a purposive sample of eight (8) R48 trained professional nurses. Eight steps of Tech’s inductive, descriptive open coding technique was followed. FINDINGS: The study established that EPI programme is important in R48 training. The implementation of integrated approach was viewed as beneficial leading to the reduction of diseases. The successful implementation would be more beneficial if it is consistently coupled with support from programme managers and regular in-service on up-dates of new vaccines and technologies are introduced within the programme / Health Studies / M.A. (Nursing Science)

Serious and violent crime in South Africa (SA) has been on the rise in the last few years. The SAPS has been stretched to the maximum in its efforts of trying to stabilise the situation, and has applied various tactics and strategies to eradicate crime. This included, among other things, changes to the basic training programme and reintroduction of specialised units. The success or failure of any criminal investigation will still often depend on the detection and analysis of physical evidence found on the crime scene. Crimes such as rape will always leave behind physical evidence in the form of body-fluids. The detectives need not be experts in order to detect or analyse this physical evidence, but should be experienced enough to know which experts or investigative aids must be used to ensure maximum recovery of the evidence. The purpose of this study was to determine how the Biological Body-fluid Dog (BBFD) can assist detectives in the investigation of rape cases. The researcher has chosen a unique investigative aid in the form of man’s best friend, namely “The Police K9” (canine). The literature shows that trained police dogs have achieved outstanding success in numerous fields around the world, i.e. from narcotic busts, detection of explosives, to countering terrorist threats, to the most chilling search-and-rescue operations. The SAPS has sent its search-and-rescue dogs to many countries abroad to assist in natural disasters. Dogs have been trained by various police agencies for various purposes, but the BBFD dog is unique to the SAPS, and is trained to detect only human blood and semen. The use of K9s in the complex forensic science environment can never be doubted or overlooked. The main problem facing the Booysens SAPS was the low arrest and conviction rate in rape cases, due to a lack of evidence. The researcher hopes to broaden the detectives’ knowledge and skills regarding the objectives of crime investigation, with special focus on detection of physical evidence at rape crime scenes. The BBFD dog is trained to detect minute amounts of body fluid on any type or size of surface, including veld, bush areas, vehicles, carpets, grass, bedrooms, etc. / Police Practice / M.A. (Criminal Justice)

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