Miracle-dz/UniFAR-FTUs huggingface dataset:
Chinese Undocumented Migrants in The Hague: Precarity, Work and the Search for Autonomy

This study is about Chinese undocumented migrant workers living in The Hague. The study looks at their living conditions and working situation, and how their ‘illegal’ stay in the country influences their ‘precarity’ and sense of autonomy. Rooted in the belief that more research is needed on undocumented migrants in particular, the study is also aimed at trying to find out how the daily lives of undocumented Chinese migrants exhibit ‘precarity’, in relation to their own experiences of balancing risks with the search for autonomy. The study was based on informal conversations with undocumented migrant workers, most of whom were contacted through Wereldhuis in The Hague, a church-run advisory NGO operates for undocumented people in the city. Through the interviews, and secondary literature, the study was able to reconstruct the historical phases of migration from China to The Netherlands. In addition, informal conversations allowed participants to give feedback on the scope of the study and the study focus. This contextualised my own research on the present-day situation of the undocumented. The key findings were that although undocumented migrants do experience precarity at work, they can help themselves towards greater autonomy through acquiring language skills, and through creating wider social networks. One obstacle they face is that being concentrated in the catering industry, there were few contacts with trade unions for most of the research participants.
New attacks and efficient countermeasures for multicast AODV

Security in multicast routing in MANETs is crucial in order to enable effective and efficient multicast-based applications. Multicast Ad-hoc On-demand Distance Vector (MAODV) protocol is one of the well-known multicast routing protocol in MANET. In this paper, we identify and describe, for the first time, three new protocol-dependant attacks on multicast operations of MAODV namely, Group Leader Selection (GLS); False Link Breakage (FLB) and Group Leader Pruning (GLP) attack. For each attack, we implement a scenario to demonstrate the impact of the attack on the performance of the network. Implemented scenarios show a considerable degradation in the Packet Delivery Ratio (PDR) of the protocol. Accordingly, we propose security countermeasures for the identified attacks. Simulation results of the secured and unsecured MAODV show that the proposed countermeasures are effective and efficient. In particular, compared to unsecured MAODV, the proposed countermeasures improve the PDR with 85%
Beliefs associated with support for child-centred learning environment among Hong Kong pre-service early childhood teachers

Abstract The physical, social and temporal dimensions of the classroom environment have an important role in children’s learning. This study examines the level of support for child-centred learning, and its associated beliefs, that is provided by Hong Kong’s pre-service early childhood teachers. Two hundred and seventy-five students from a pre-service early childhood teacher training programme completed a questionnaire; in general, these students believed that teachers should create physical, social and temporal environments which are child-centred in early childhood education settings. Linear regression analyses showed that the students’ perceived importance of fostering children’s social and communication competence in early childhood programmes, and their belief in children’s competence in self-learning, were significant positive correlates of their level of support for all dimensions of child-centred learning environment. Their self-perceived competence in managing children’s behaviours was, however, a significant negative correlate of their level of support for child-centred social and temporal environment. This study then discusses the implications of these findings for pre-service early childhood teacher education.
Integration of Data Mining and Hybrid Expert System

In the latest decades a great growth in the capacity of generating and collecting data has happened. The progresses in the data collection and storage, combined with the extensive use of the Database Management System and Data Warehousing technology, have contributed to this growth. However, the traditional methods used to manipulate these data can generate informative reports, but they cannot analyze the content of the data to point out what knowledge interests most. These difficulties contributed to the arising of intelligent tools and techniques to analyze data resultant from the emergent field of the Knowledge Discovery in Databases (KDD). KDD is not a trivial process and it is used to identify valid, potentially useful and comprehensible patterns in the database. The main goals of this work are to apply some of these tools and techniques to a medical database of breast cancer, so that detection and prediction patterns are discovered, and use the database resulting of the mining process in a hybrid expert system that will help in medical diagnosis.
Psychometric Properties of the Online Arabic Versions of BDI-II, HSCL-25, and PDS

The Beck Depression Inventory (BDI-II), Hopkins Symptom Checklist (HSCL-25), and Posttraumatic Diagnostic Scale (PDS) are three widely applied clinical instruments for assessing depression, anxiety, and posttraumatic stress symptoms, respectively. Use of online-based psychological help and assessment is rapidly growing which necessitates the need for the validation of online assessment. To address these needs, data from 1,544 Arabic mother tongue treatment-seeking participants, who filled in the Arabic versions of these instruments online, was analyzed in two steps. In the first step, exploratory structural equation modeling (ESEM) was used to scrutinize factorial validity and eliminate items. In the second step, we examined the interrelationships between the latent factors (dimensions) using confirmatory factor analysis (CFA) of multitrait-multimethod (MTMM) data. Results show an acceptable to good fit of the hypothesized model, providing some first insights into the factorial and construct validity of the Arabic versions of BDI-II, HSCL-25, and PDS under consideration of cultural-specific aspects. Present evidence speaks for construct validity of the three instruments and the reliability and usefulness of online assessment.
Analysis of FM signals affected by Gaussian AM using the reduced Wigner-Ville trispectrum

A novel time-frequency analysis method for FM signals affected by multiplicative and additive noise is proposed. The method, referred to as the reduced Wigner-Ville trispectrum (RWVT), is a member of a class of time-varying polyspectra based on polynomial Wigner-Ville distributions (WVD) (B. Boashash and P. O'Shea, 1991). It is shown that the RWVT outperforms the Wigner-Ville distribution, and therefore any other method based on the Cohen's class of bilinear time frequency distributions (TFDs), for the class of signals considered. In addition, the authors present the statistical properties of an instantaneous frequency estimator based on the peak of the RWVT, and compare them with the properties of the estimator based on the peak of the WVD. <<ETX>>
Some Compact Classes of Open Sets under Hausdorff Distance and Application to Shape Optimization

In this paper, we introduce three new classes of open sets in a general Euclidean space $\mathbb{R}^N$. It is shown that every class of open sets is compact under the Hausdorff distance. The result is then applied to a shape optimization problem of elliptic equation. The existence of the optimal solution is presented.
Moving beyond SameAs with PLATO: partonomy detection for linked data

The Linked Open Data (LOD) Cloud has gained significant traction over the past few years. With over 275 interlinked datasets across diverse domains such as life science, geography, politics, and more, the LOD Cloud has the potential to support a variety of applications ranging from open domain question answering to drug discovery. Despite its significant size (approx. 30 billion triples), the data is relatively sparely interlinked (approx. 400 million links). A semantically richer LOD Cloud is needed to fully realize its potential. Data in the LOD Cloud are currently interlinked mainly via the owl:sameAs property, which is inadequate for many applications. Additional properties capturing relations based on causality or partonomy are needed to enable the answering of complex questions and to support applications. In this paper, we present a solution to enrich the LOD Cloud by automatically detecting partonomic relationships, which are well-established, fundamental properties grounded in linguistics and philosophy. We empirically evaluate our solution across several domains, and show that our approach performs well on detecting partonomic properties between LOD Cloud data.
Correlations

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Spatio-Temporal Analysis on FEMA Situation Updates with Automated Information Extraction

With the advent of the World-Wide-Web, there is an over-abundance of textual information. Information present in digital documents can be utilized better if it can be extracted automatically and scalably from text and visualized using visualization tools. In this paper, we present an automated information extraction and visualization tool for human sensor data. Our system consists of three main components: FactXtractor, GeoTagger, and FEMARepViz. Named entities and entity relations are extracted using FactXtractor. We have proposed a novel stripped dependency tree kernel for a Support Vector Machine (SVM) based classifier to identify semantic relationships among entities. GeoTagger disambiguates location entities. Built on top of the first two system components, FemaRepViz is an application that segments text documents, identifies the topic of the segments, extracts location entities, disambiguates them, and visualizes the extracted information on Google Earth or Google Map. Our empirical evaluation shows that the system achieves reasonable accuracy.
Cheating the CHA2DS2-VASc Score: Thromboembolism in Apical Hypertrophic Cardiomyopathy

Atrial fibrillation increases the risk of systemic thromboembolism in general and stroke in particular. Not all patients who develop atrial fibrillation are at significantly heightened risk of thromboembolic complications, however, with the development of risk scoring systems aiding clinicians in determining whether formal anticoagulation is mandated. The most commonly used contemporary scoring systems—CHADS2 and CHA2DS2-VASc—provide a reliable means of assessing stroke risk, but certain cardiac conditions are associated with an increased incidence of thromboembolism without impacting on these risk scores. Hypertrophic cardiomyopathy, with its apical variant, is such a condition. We present a case of a patient with apical hypertrophic cardiomyopathy and atrial fibrillation who suffered dire thromboembolic consequences despite a reassuringly low CHA2DS2-VASc score and suggest that this scoring system is modified to incorporate the thromboembolic risk inherent to certain cardiomyopathies irrespective of impairment of left ventricular systolic dysfunction or clinical heart failure.
An Object-Relational Data Warehouse Modelig for Complex Data

A data warehouse is an essential component to the decision support system. The traditional data warehouse provides only numeric and character data analysis. But as information technologies progress, complex data such as semi-structured and unstructured data become vastly used. Developing new data warehouse technologies to accommodate the demand of integrating complex data is important. In this paper, we propose a data model for the complex data warehouse. This model incorporates the well-known star schema with object-relational concepts, inheriting the easy-understanding and multidimensional characteristic, and providing mechanisms to integrate complex data in a data warehouse model.
Modeling Core Metabolism in Cancer Cells: Surveying the Topology Underlying the Warburg Effect

Background Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anti-cancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority. Methodology/Principal Findings This work presents a constraint-base modeling of the most experimentally studied metabolic pathways supporting cancer cells: glycolysis, TCA cycle, pentose phosphate, glutaminolysis and oxidative phosphorylation. To evaluate its predictive capacities, a growth kinetics study for Hela cell lines was accomplished and qualitatively compared with in silico predictions. Furthermore, based on pure computational criteria, we concluded that a set of enzymes (such as lactate dehydrogenase and pyruvate dehydrogenase) perform a pivotal role in cancer cell growth, findings supported by an experimental counterpart. Conclusions/Significance Alterations on metabolic activity are crucial to initiate and sustain cancer phenotype. In this work, we analyzed the phenotype capacities emerged from a constructed metabolic network conformed by the most experimentally studied pathways sustaining cancer cell growth. Remarkably, in silico model was able to resemble the physiological conditions in cancer cells and successfully identified some enzymes currently studied by its therapeutic effect. Overall, we supplied evidence that constraint-based modeling constitutes a promising computational platform to: 1) integrate high throughput technology and establish a crosstalk between experimental validation and in silico prediction in cancer cell phenotype; 2) explore the fundamental metabolic mechanism that confers robustness in cancer; and 3) suggest new metabolic targets for anticancer treatments. All these issues being central to explore cancer cell metabolism from a systems biology perspective.
A New Pre-initializing Strategy: Multi-Period Particle Swarm Optimization

A more efficient pre-initializing strategy of PSO algorithm: Multi-Period Particle Swarm Optimization (MP-PSO) is proposed. The process is divided into two periods: pre-initialization and post-optimization. The former is determined to find a better local solution to initialize the next period instead of standard uniform randomness. In order to explore further, adaptive escaping weight is adopted to avoid premature convergence during post-optimization. The results of benchmark test show that performance of MP-PSO is much more effective than that of standard PSO, especially in higher dimensional problems.
Influence of the Neutron Star 1E 161348–5055 in RCW 103 on the Surrounding Medium

Abstract We have carried out a study of the neutral hydrogen in the direction of the X-ray source 1E 161348–5055, a compact central object (CCO) located in the interior of the supernova remnant (SNR) RCW 103. The Hi 21 cm line observations were carried out using the Australia Telescope Compact Array, complemented with single dish data from the Parkes radio telescope to recover information at all spatial scales. We derive a distance to RCW 103 of 3.1 kpc, in agreement with previous distance measurements. We have also detected a small hole in the Hi emission which is positionally and kinematically coincident with the location of the CCO which confirms the association between the SNR and the CCO. This is the third case of a depression in Hi emission seemingly associated with CCOs in SNRs. The characteristic parameters of the holes such as their size, eccentricity and evacuated mass are similar in all three cases. We estimate the absorbing HI column density towards 1E 161348–5055 to be ∼6 × 1021 cm–2, a value compatible with a blackbody solution for the CCO X-ray emission. However, the implied temperature and luminosity are very high compared to most neutron stars. Moreover, the strong long-term variability in X-rays favours the hypothesis that 1E 161348–5055 is an accreting binary source rather than an isolated, cooling neutron star. An analysis of the continuum image obtained at 1.4 GHz from these observations shows no trace of a pulsar wind nebula around 1E 161348–5055, in spite of it being a young object.
Preoperative Plasma HER2 and Epidermal Growth Factor Receptor for Staging and Prognostication in Patients with Clinically Localized Prostate Cancer

Purpose: Human epidermal growth factor receptor-2 (HER2) and epidermal growth factor receptor (EGFR) expression have been associated with disease progression in patients with prostate cancer. We tested the hypothesis that plasma levels of HER2 and/or EGFR are associated with prostate cancer stage and prognosis in patients with clinically localized disease. Experimental Design: We measured preoperative plasma HER2 and EGFR levels using commercially available ELISAs on banked plasma from 227 patients treated with radical prostatectomy and bilateral lymphadenectomy for clinically localized prostate adenocarcinoma. Results: Median preoperative plasma EGFR and HER2 levels were 31.4 ng/mL (interquartile range, 19.2 ng/mL) and 10.0 ng/mL (interquartile range, 2.7 ng/mL), respectively. HER2 was elevated in patients with seminal vesicle invasion (P = 0.033). In separate multivariate analyses that adjusted for the effects of standard preoperative predictors, lower EGFR, higher HER2, and higher HER2/EGFR ratio were associated with prostate-specific antigen (PSA) progression (P = 0.003, P < 0.001, and P < 0.001, respectively). In separate multivariate analyses that adjusted for the effects of standard postoperative predictors, lower EGFR and higher HER2/EGFR ratio were associated with PSA progression (P = 0.027 and P < 0.001, respectively). Among the patients who experienced PSA progression, HER2 was significantly higher (P = 0.023) and EGFR was lower (P = 0.04) in those with features of aggressive disease (i.e., development of metastasis, PSA doubling time <10 months, and/or failure to respond to local salvage radiation therapy). Conclusion: Preoperative plasma HER2 and EGFR were associated with prostate cancer progression after radical prostatectomy. Plasma HER2 and EGFR may provide a tool for predicting long-term recurrence-free survival and early metastasis.
Offline driving pattern detection and identification under usage disturbances

Optimizing the configuration of a wheel loader to customer needs can lead to a significant increase in efficiency with respect to fuel economy, cost, component dimensioning etc. Experience show that even modest customer adaptation can save around 20% of fuel cost. A key motivator for this work is that wheel loader manufacturers in general does not have full information about customer usage of the machine and the main objective here is to develop an algorithm that automatically, using only production sensors, extracts information about the usage of a machine at a specific customer site. Two main challenges are that sensors are not located with respect to this task and the significant usage disturbances that typically occur during operation. The proposed solution is a robust method, based on a mix of techniques using basic signal processing, state automaton techniques, and parameter estimation algorithms. A key property of the method is the method of combining, individually very simple, basic techniques in a scheme where robustness are introduced. The approach is evaluated on measured data of a wheel loader loading gravel and shot rock.
A Primer on the Signature Method in Machine Learning

In these notes, we wish to provide an introduction to the signature method, focusing on its basic theoretical properties and recent numerical applications. The notes are split into two parts. The first part focuses on the definition and fundamental properties of the signature of a path, or the path signature. We have aimed for a minimalistic approach, assuming only familiarity with classical real analysis and integration theory, and supplementing theory with straightforward examples. We have chosen to focus in detail on the principle properties of the signature which we believe are fundamental to understanding its role in applications. We also present an informal discussion on some of its deeper properties and briefly mention the role of the signature in rough paths theory, which we hope could serve as a light introduction to rough paths for the interested reader. The second part of these notes discusses practical applications of the path signature to the area of machine learning. The signature approach represents a non-parametric way for extraction of characteristic features from data. The data are converted into a multi-dimensional path by means of various embedding algorithms and then processed for computation of individual terms of the signature which summarise certain information contained in the data. The signature thus transforms raw data into a set of features which are used in machine learning tasks. We will review current progress in applications of signatures to machine learning problems.