Developing and testing a framework for understanding refugees' job search processes

PurposeThis article outlines the development of the Refugee Job Search Process Framework (RJSPF), which was created to help identify barriers that refugees face when trying to find employment. The framework incorporates an interdisciplinary, multi-level approach to the job search, delving into research from migration studies and Industrial/Organizational psychology to outline factors that exist on both the side of the refugee applicant and the organization at each stage of the RJSPF. The authors also tested the RJSPF with Syrian refugees and service providers in Canada to examine the validity of each component of the model.Design/methodology/approachThe authors used a semi-structured format to interview refugees and service providers on their experiences in either trying to find employment or helping their refugee clients with the job search process. After transcribing the interviews, the data were independently coded, quantified, and analysed using Nvivo software to validate the RJSPF.FindingsThe majority of the RJSPF either had high or moderate support from the interviews. The authors also identified 6 broader themes using thematic analysis, which include language fluency, credential recognition, Canadian experience “catch 22”, cultural incongruencies, employer exploitation, and mental health for successful employment.Originality/valueThe RJSPF is a new integration of disparate theories of job search experiences in a literature that lacks an organizing framework and perspective on the unique challenges refugees face in this area compared to other newcomers. In doing so, the authors use an interdisciplinary, multi-level approach that extends the nomological network of barriers facing refugees, therefore informing future research and practice.

Neural Architecture Search for 1D CNNs—Different Approaches Tests and Measurements

In the field of sensors, in areas such as industrial, clinical, or environment, it is common to find one dimensional (1D) formatted data (e.g., electrocardiogram, temperature, power consumption). A very promising technique for modelling this information is the use of One Dimensional Convolutional Neural Networks (1D CNN), which introduces a new challenge, namely how to define the best architecture for a 1D CNN. This manuscript addresses the concept of One Dimensional Neural Architecture Search (1D NAS), an approach that automates the search for the best combination of Neuronal Networks hyperparameters (model architecture), including both structural and training hyperparameters, for optimising 1D CNNs. This work includes the implementation of search processes for 1D CNN architectures based on five strategies: greedy, random, Bayesian, hyperband, and genetic approaches to perform, collect, and analyse the results obtained by each strategy scenario. For the analysis, we conducted 125 experiments, followed by a thorough evaluation from multiple perspectives, including the best-performing model in terms of accuracy, consistency, variability, total running time, and computational resource consumption. Finally, by presenting the optimised 1D CNN architecture, the results for the manuscript’s research question (a real-life clinical case) were provided.

EXPRESS: Sources of Variation In Search and Foraging A Theoretical Perspective

Search – the problem of exploring a space of alternatives in order to identify target goals – is a fundamental behaviour for many species. Although its foundation lies in foraging, most studies of human search behaviour have been directed towards understanding the attentional mechanisms that underlie the efficient visual exploration of two-dimensional scenes. With this review, we aim to characterise how search behaviour can be explained across a wide range of contexts, environments, spatial scales, and populations, both typical and atypical. We first consider the generality of search processes across psychological domains. We then review studies of interspecies differences in search. Finally, we explore in detail the individual and contextual variables that affect visual search and related behaviours in established experimental psychology paradigms. Despite the heterogeneity of the findings discussed, we identify that variations in control processes, along with the ability to regulate behaviour as a function of the structure of search space and the sampling processes adopted, to be central to explanations of variations in search behaviour. We propose a tentative theoretical model aimed at integrating these notions and close by exploring questions that remain unaddressed.

Tailored performance dashboards—an evaluation of the state of the art

Context Tailoring mechanisms allow performance dashboards to vary their appearance as a response to changing requirements (e.g., adapting to multiple users or multiple domains). Objective We analyze existing research on tailored dashboards and investigate different proposed approaches. Methodology We performed a systematic literature review. Our search processes yielded a total of 1,764 papers, out of which we screened 1,243 and ultimately used six for data collection. Results Tailored dashboards, while being introduced almost thirty years ago, did not receive much research attention. However, the area is expanding in recent years and we observed common patterns in novel tailoring mechanisms. Since none of the existing solutions have been running for extended periods of time in real-world scenarios, this lack of empirical data is a likely cause of vaguely described research designs and important practical issues being overlooked. Implications Based on our findings we propose types of tailoring mechanisms taking into account the timing and nature of recommendations. This classification is grounded in empirical data and serves as a step ahead to a more unifying way of looking at tailoring capabilities in the context of dashboards. Finally, we outline a set of recommendations for future research, as well as a series of steps to follow to make studies more attractive to practitioners.

Experion: A framework for contextualizing evidence in expert finding

Expert finding is traditionally related to a subject of research in information retrieval and, often, is taken to mean "expertise retrieval within a specific organization". The task involves finding an expert in an expertise topic. Even though there are interesting proposals in the literature, they do not consider the context in which a given expertise is bound. This Ph.D. thesis introduces the concept of a framework that chronologically contextualizes search results in expert finding. Our motivation is to provide more accurate results of search processes related to finding experts in a given topic, contextualizing the expertise on professional/academic activities, an open research topic. In this paper, we present the main concepts of the framework we are developing and a general overview of its operation. At the moment, we are using the Lattes platform as a data source, for which we developed a process to extract expertise evidence, supported by the Crossref database.

Skilled Migrant Women’s Experiences of the Job Search Process

In this article, we address how skilled migrant women experience job search processes in Finland, and the expectations and emotions that arise from these workforce encounters, which we explore through unique qualitative data. Although Finland relies strongly on principles of equality and inclusion, highly educated migrant women face major difficulties in job application processes. The employment level of migrant women in Finland is low compared to other Nordic countries, and even though migrant women are more educated than migrant men and their Finnish language skills are better, they encounter many hurdles in employment. As a result of the Covid-19 pandemic, the situation is getting more difficult for many women with non-Finnish background.There are multiple hurdles in highly educated women workers’ employment, which relate to structural and cultural aspects and which end up in discrimination in recruitment processes.

An overview of machine learning techniques in constraint solving

Constraint solving is applied in different application contexts. Examples thereof are the configuration of complex products and services, the determination of production schedules, and the determination of recommendations in online sales scenarios. Constraint solvers apply, for example, search heuristics to assure adequate runtime performance and prediction quality. Several approaches have already been developed showing that machine learning (ML) can be used to optimize search processes in constraint solving. In this article, we provide an overview of the state of the art in applying ML approaches to constraint solving problems including constraint satisfaction, SAT solving, answer set programming (ASP) and applications thereof such as configuration, constraint-based recommendation, and model-based diagnosis. We compare and discuss the advantages and disadvantages of these approaches and point out relevant directions for future work.

Parallel exploration via negatively correlated search

Effective exploration is key to a successful search process. The recently proposed negatively correlated search (NCS) tries to achieve this by coordinated parallel exploration, where a set of search processes are driven to be negatively correlated so that different promising areas of the search space can be visited simultaneously. Despite successful applications of NCS, the negatively correlated search behaviors were mostly devised by intuition, while deeper (e.g., mathematical) understanding is missing. In this paper, a more principled NCS, namely NCNES, is presented, showing that the parallel exploration is equivalent to a process of seeking probabilistic models that both lead to solutions of high quality and are distant from previous obtained probabilistic models. Reinforcement learning, for which exploration is of particular importance, are considered for empirical assessment. The proposed NCNES is applied to directly train a deep convolution network with 1.7 million connection weights for playing Atari games. Empirical results show that the significant advantages of NCNES, especially on games with uncertain and delayed rewards, can be highly owed to the effective parallel exploration ability.

Analisa Dan Pengembangan Sistem Informasi Distribusi Dan Penjualan Cv Jogja Konveksi

The system applied by CV Jogja Convection which is one of the distributors in the city of Padang. In the data processing information system that supports sales which includes goods data, customer or customer data, and others. The design of this system is one of the choices to help the distribution of goods such as sales at CV Jogja Convection. The information system created is based on a website and has a function as a medium for the distribution of goods CV Jogja Convection. This web-based information system can speed up accurate data entry and fast search processes in sales information systems in particular.