Miracle-dz/UniFAR-FTUs huggingface dataset:
Do Chinese Teachers Perform Emotional Labor Equally? Multi-Group Comparisons Across Genders, Grade Levels and Regions

The emotional aspects of teaching are important and teachers’ emotional labor, or, how teachers manage emotions at school, has been attracting more and more attention recently. Using multi-group structural equation modeling, this study investigated the measurement invariance of, and the relationships between, teachers’ emotional labor strategies and teaching satisfaction. Participants included teachers from primary and secondary schools in Hong Kong and mainland China. Three sets of group comparisons have been made between female and male teachers, between primary and secondary school teachers, and between teachers in Hong Kong and mainland China. The multi-group invariance tests showed no significant subgroup differences in the measurement and structural models. Thus, there was no difference of ‘kind. ’ However, some differences of ‘degree’ were observed across genders, grade levels and regions. These differences in the relationship between surface/deep acting and teaching satisfaction can be attributed to the possible influence of some cognitive factors and socio-cultural contexts. With due methodological rigor, the results of this study provide deeper understanding of teachers’ emotional labor and its relationship with teaching satisfaction.
Executive Functions and Language: Their Differential Influence on Mono- vs. Multilingual Spelling in Primary School

We aimed at unveiling the role of executive functions (EFs) and language-related skills in spelling for mono- versus multilingual primary school children. We focused on EF and language-related skills, in particular lexicon size and phonological awareness (PA), because these factors were found to predict spelling in studies predominantly conducted with monolinguals, and because multilingualism can modulate these factors. There is evidence for (a) a bilingual advantage in EF due to constant high cognitive demands through language control, (b) a smaller mental lexicon in German and (c) possibly better PA. Multilinguals in Germany show on average poorer German language proficiency, what can influence performance on language-based tasks negatively. Thus, we included two spelling tasks to tease apart spelling based on lexical knowledge (i.e., word spelling) from spelling based on non-lexical strategies (i.e., non-word spelling). Our sample consisted of heterogeneous third graders from Germany: 69 monolinguals (age: M = 108 months) and 57 multilinguals (age: M = 111 months). On less language-dependent tasks (e.g., non-word spelling, PA, intelligence, short-term memory (STM) and three EF tasks testing switching, inhibition, and working memory) performance of both groups did not differ significantly. However, multilinguals performed significantly more poorly on tasks measuring German lexicon size and word spelling than monolinguals. Regression analyses revealed that for multilinguals, inhibition was related to spelling, whereas switching was the only EF component to influence word spelling in monolinguals and non-word spelling performance in both groups. By adding lexicon size and other language-related factors to the regression models, the influence of switching was reduced to insignificant effects, but inhibition remained significant for multilinguals. Language-related skills best predicted spelling and both language groups shared those variables: PA for word spelling, and STM for non-word spelling. Additionally, multilinguals’ word spelling performance was also predicted by their German lexicon size, and non-word spelling performance by PA. This study offers an in-depth look at spelling acquisition at a certain point of literacy development. Mono- and multilinguals have the predominant factors for spelling in common, but probably due to superior language knowledge, monolinguals were already able to make use of EF during spelling. For multilinguals, German lexicon size was more important for spelling than EF. For multilinguals’ spelling these functions might come into play only at a later stage.
GANAB and PKD1 Variations in a 12 Years Old Female Patient With Early Onset of Autosomal Dominant Polycystic Kidney Disease

Autosomal Dominant Polycystic Kidney Disease (ADPKD) typically results from a mutation in the PKD1 and PKD2 genes, which code for polycystin-1 (PC1) and polycystin-2 (PC2), respectively. Mutations in these genes promote renal cystic dysplasia and are a significant cause of End-Stage Kidney Disease (ESKD). Polycystic kidney disease-3 (PKD3), another form of ADPKD, is caused by mutations in glucosidase II alpha subunit (GANAB) gene and present in mid- and late adulthood. We report a description of an ADPKD case in a 12-year-old female presented bilateral renal cysts in adolescence. Two mutations in two genes PKD1 and GANAB were identified by targeted capture and next-generation sequencing (NGS) on an Illumina sequencing system. The identified PKD1 mutation p.Pro61Leu: c.182C > T (CCC > CTC) a missense type of uncertain clinical significance. However, the identified PKD1 mutation can alter transcription factors motifs and consequently disturb the transcription process. The second mutation identified in GANAB locus, p.Arg61Ter: c.181C > T, a nonsense type, CGA > TGA. The mutation is unreported pathogenic variant can cause loss of the glucosidase II alpha subunit normal protein function. Both the patient father and paternal grandmother had a history of ADPKD but never were tested. This case is the first case of combine presentation on PKD1 and PKD3 in a pediatric patient with nephrolithiasis.
Directional Visual Motion Is Represented in the Auditory and Association Cortices of Early Deaf Individuals

Individuals who are deaf since early life may show enhanced performance at some visual tasks, including discrimination of directional motion. The neural substrates of such behavioral enhancements remain difficult to identify in humans, although neural plasticity has been shown for early deaf people in the auditory and association cortices, including the primary auditory cortex (PAC) and STS region, respectively. Here, we investigated whether neural responses in auditory and association cortices of early deaf individuals are reorganized to be sensitive to directional visual motion. To capture direction-selective responses, we recorded fMRI responses frequency-tagged to the 0.1-Hz presentation of central directional (100% coherent random dot) motion persisting for 2 sec contrasted with nondirectional (0% coherent) motion for 8 sec. We found direction-selective responses in the STS region in both deaf and hearing participants, but the extent of activation in the right STS region was 5.5 times larger for deaf participants. Minimal but significant direction-selective responses were also found in the PAC of deaf participants, both at the group level and in five of six individuals. In response to stimuli presented separately in the right and left visual fields, the relative activation across the right and left hemispheres was similar in both the PAC and STS region of deaf participants. Notably, the enhanced right-hemisphere activation could support the right visual field advantage reported previously in behavioral studies. Taken together, these results show that the reorganized auditory cortices of early deaf individuals are sensitive to directional motion. Speculatively, these results suggest that auditory and association regions can be remapped to support enhanced visual performance.
Discovering and Comparing Relational Knowledge, the Example of Pharmacogenomics

Pharmacogenomics (PGx) studies the influence of the genome in drug response, with knowledge units of the form of ternary relationships genomic variation – drug – phenotype. State-of-the-art PGx knowledge is available in the biomedical literature as well as in specialized knowledge bases. Additionally, Electronic Health Records of hospitals can be mined to discover such knowledge units that can then be compared with the state of the art, in order to confirm or temper relationships lacking validation or clinical counterpart. However, both discovering and comparing PGx relationships face multiple challenges: heterogeneous descriptions of knowledge units (languages, vocabularies and granularities), missing values and importance of the time dimension. In this research, we aim at proposing a framework based on Semantic Web technologies and Formal Concept Analysis to discover, represent and compare PGx knowledge units. We present the first results, consisting of creating an integrated knowledge base of PGx knowledge units from various sources and defining comparison methods, as well as the remaining issues to tackle.
Do Competency Questions for Alignment Help Fostering Complex Correspondences?

The Linked Open Data is composed linked knowledge bases. Most of these links are still limited to simple correspondences. As a complement, complex correspondences bring more expressiveness to bridge the heterogeneity of knowledge bases. Finding correspondences (simple or complex) is the purpose of ontology matching. Existing matching solutions mainly focus on establishing as many correspondences as possible given two knowledge bases. On the one hand, this has the effect of neglecting the user needs. On the other hand, when dealing with large knowledge bases, this may impact the performance of the matching task. In response to this observation, we introduce Competency Questions for Alignment (CQAs) to express the needs of a user with respect an ontology alignment. We present our work on how CQAs can help ontology matching, and in particular complex matching.
Gender Violence's Models and Discrimination-aware Data Mining

The violence against women, bases in the inequity of opportunities in every social stratum. According to national surveys, women and girls are considered a vulnerable population due to the inequalities in access to essential services such as education, economic independence and technology. Currently, governments show interest to address this problem and provide parity of opportunities to contribute to the development of society. Therefore, several social studies analysed the situation of women and their impact on welfare indicators regarding the development of the population, in this context, some techniques propose solutions through Data Mining to measure and recognise possible discrimination and violence. The thesis work intends to develop data mining models to identified discrimination mainly towards women and girls, taking into account the environmental factors such as individual, community, social and institutions.
Innovation and Employment: An Agent-Based Approach

While the effects of innovation on employment have been a controversial issue in economic literature for several years, this economic puzzle is particularly relevant nowadays. We are witnessing tremendous technological developments which threaten to disrupt the labour market, due to their potential for significantly automating human labour. As such, this paper presents a qualitative study of the dynamics underlying the relationship between innovation and employment, using an agent-based model developed in Python. The model represents an economy populated by firms able to perform either Product Innovation (leading to the discovery of new tasks, which require human labour) or Process Innovation (leading to the automation of tasks previously performed by humans). The analysis led to three major conclusions, valid in this context. The first takeaway is that the Employment Rate in a given economy is dependent on the automation potential of the tasks in that economy and dependent on the type of innovation performed by firms in that economy (with Product Innovation having a positive effect on employment and Process Innovation having a negative effect). Second, in any given economy, if firmsâ€™ propensity for product and process innovation, as well as the automation potential of their tasks are stable over time, the Employment Rate in that economy will tend towards stability over time. The third conclusion is that higher levels of Process Innovation and lower levels of Product Innovation, lead to a more intense decline of wage shares and to a wider gap between employee productivity growth and wage growth.
Towards Knowledge Graph Construction from Entity Co-occurrence

The construction of knowledge graphs from resources on the Web is a topic that gained a lot of attention in recent years, especially with the uprising of large-scale cross-domain knowledge graphs like DBpedia and YAGO. Their successful exploitation of Wikipedia’s structural elements like infoboxes and categories gives way to the thought that there is still a huge potential for approaches focused on structural elements of web documents. In this work, we present our research idea towards further exploitation of semi-structured data with a focus on entity cooccurrence. We want to explore the potential of co-occurrence patterns in varying contexts and test their generality when applying them to the Document Web. An overview of the state of the art is given and we show how our three-phased approach for the extraction of co-occurrence patterns fits in. Two planned experiments for the construction of knowledge graphs based on Wikipedia and the Document Web are sketched. Finally, potentials and limitations of the approach are discussed.
Upward social comparison and depression in social network settings: The roles of envy and self-efficacy

Purpose Previous research has linked upward social comparison on social network sites (SNSs) to depressive symptoms; however, the mechanism underlying this relationship remains unclear. The purpose of this paper is to explore the roles of envy and self-efficacy in the relationship between upward social comparison on SNSs and depressive symptoms. Design/methodology/approach Based on the social comparison theory and previous related literature, a moderated mediation model integrating upward social comparison on SNSs, depressive symptoms, envy and self-efficacy was developed and empirically examined based on the data collected from 934 Chinese high school students. Findings The structural equation modeling analysis shows that envy partially mediates the relationship between upward social comparison on SNSs and depressive symptoms, whereas self-efficacy moderated both the direct effect of upward social comparison on SNSs on depressive symptoms and the mediating effect of envy in the relationship between upward social comparison on SNSs and depressive symptoms. Practical implications The findings offer interesting implications for guiding adolescents to use SNSs properly. This study found that envy and self-efficacy act as a mediator and moderator, respectively, between upward social comparison on SNSs and depressive symptoms, indicating that reducing envy and enhancing self-efficacy should be feasible to alleviate the negative effect of SNSs use. Social implications In order to alleviate the negative effect of SNSs use, parents and educators should direct adolescents to view others’ achievements and happiness properly and manage to improve self-efficacy among adolescents with poor self-efficacy through effective training. Originality/value Through building and examining a moderated mediation model integrating envy and self-efficacy into the relationship between upward social comparison on SNSs and depressive symptoms, the present study advances our understanding of how and when upward social comparison on SNSs augments the risk of depressive symptoms among adolescents.
Sum-Product Network structure learning by efficient product nodes discovery

Sum-Product Networks (SPNs) are recently introduced deep probabilistic models providing exact and tractable inference. Even if SPNs have been successfully employed in several application domains, learning their structure from high dimensional data still poses a challenge in terms of time complexity. Classical SPNs structure learning algorithms work by exploiting two high cost operations repeated several times: determining independencies among random variables (RVs)–introducing product nodes–and finding sub-populations among samples–introducing sum nodes. Even one of the simplest greedy structure learner, LearnSPN, scales quadratically in the number of the variables to determine RVs independencies. In this work we investigate some approximate but fast procedures to determine independencies among RVs whose time complexity scales sub-quadratically. In particular, we propose two procedures based on a random subspace approach, and a third one adopting entropy as a criterion to split RVs. Experimental results, on many state-of-the-art datasets for density estimation, prove that LearnSPN equipped by our splitting procedures is able to reduce learning and/or inference times while preserving comparable inference accuracy.
Holistic Multiple Ontologies Merging

Ontologies as the main infrastructure to represent data in the Semantic Web are widely developed independently in each area. When it comes to finding a suitable ontology for a given application, two problems occur: Often, an ontology will cover just a part of the domain of interest or competing ontologies modeling the domain from different viewpoints exist. Thus, before being able to leverage the power of ontologies, they themselves need to be integrated. This is a challenging task. The existing approaches are mostly limited to a binary merge. However, by the large availability of the relevant ontologies in the desired domain, an efficient multiple ontologies merging technique is often a necessity to overcome the scalability problem. This research thus advocates for the development of a holistic, efficient multiple ontologies merging method called CoMerger, to satisfy the scalability issue. For efficient processing, rather than merging a large number of ontologies, we merge a small number of clusters. To approve the feasibility of our approach, we will run CoMerger on real-life datasets. Further, our platform will be freely accessible through a live portal.
Collecting event‐related tweets from twitter stream

Twitter provides a channel of collecting and publishing instant information on major events like natural disasters. However, information flow on Twitter is of great volume. For a specific event, messages collected from the Twitter Stream based on either location constraint or predefined keywords would contain a lot of noise. In this article, we propose a method to achieve both high‐precision and high‐recall in collecting event‐related tweets. Our method involves an automatic keyword generation component, and an event‐related tweet identification component. For keyword generation, we consider three properties of candidate keywords, namely relevance, coverage, and evolvement. The keyword updating mechanism enables our method to track the main topics of tweets along event development. To minimize annotation effort in identifying event‐related tweets, we adopt active learning and incorporate multiple‐instance learning which assigns labels to bags instead of instances (that is, individual tweets). Through experiments on two real‐world events, we demonstrate the superiority of our method against state‐of‐the‐art alternatives.
Efficient modularity density heuristics in graph clustering and their applications

Modularity Density Maximization is a graph clustering problem which avoids the resolution limit degeneracy of the Modularity Maximization problem. This thesis aims at solving larger instances than current Modularity Density heuristics do, and show how close the obtained solutions are to the expected clustering. Three main contributions arise from this objective. The first one is about the theoretical contributions about properties of Modularity Density based prioritizers. The second one is the development of eight Modularity Density Maximization heuristics. Our heuristics are compared with optimal results from the literature, and with GAOD, iMeme-Net, HAIN, BMD-λ heuristics. Our results are also compared with CNM and Louvain which are heuristics for Modularity Maximization that solve instances with thousands of nodes. The tests were carried out by using graphs from the “Stanford Large Network Dataset Collection”. The experiments have shown that our eight heuristics found solutions for graphs with hundreds of thousands of nodes. Our results have also shown that five of our heuristics surpassed the current state-of-the-art Modularity Density Maximization heuristic solvers for large graphs. A third contribution is the proposal of six column generation methods. These methods use exact and heuristic auxiliary solvers and an initial variable generator. Comparisons among our proposed column generations and state-of-the-art algorithms were also carried out. The results showed that: (i) two of our methods surpassed the state-of-the-art algorithms in terms of time, and (ii) our methods proved the optimal value for larger instances than current approaches can tackle. Our results suggest clear improvements to the state-of-the-art results for the Modularity Density Maximization problem.
Multi-scale Hierarchical Residual Network for Dense Captioning

Recent research on dense captioning based on the recurrent neural network and the convolutional neural network has made a great progress. However, mapping from an image feature space to a description space is a nonlinear and multimodel task, which makes it difficult for the current methods to get accurate results. In this paper, we put forward a novel approach for dense captioning based on hourglass-structured residual learning. Discriminant feature maps are obtained by incorporating dense connected networks and residual learning in our model. Finally, the performance of the approach on the Visual Genome V1.0 dataset and the region labelled MS-COCO (Microsoft Common Objects in Context) dataset are demonstrated. The experimental results have shown that our approach outperforms most current methods.
Robustness enhancement of IDA-PBC controller in stabilising the inertia wheel inverted pendulum: theory and real-time experiments

ABSTRACT Improving the robustness, vis-à-vis matched input disturbances of interconnection and damping assignment, passivity based control (IDA-PBC) for a class of underactuated mechanical systems is addressed in this paper. The characterised class of systems is described by a Port Controlled Hamiltonian (PCH) model which represents another alternative to the classical Euler–Lagrange models for which IDA–PBC yields a smooth stabilising controller. Our main contribution consists of combining the so-called IDA-PBC controller with an adaptive control technique. Some sufficient stability conditions on matched input disturbances are given. In order to estimate the stability and performance robustness of both controllers, a stochastic robustness analysis was used. Indeed, we used the Monte Carlo simulation (MCS) based on uncertainties to analyse the behaviour of the closed-loop system. The comparison of the stability robustness between the classical IDA-PBC controller and the proposed one is then provided. As an illustration, we proposed to revisit the application of IDA-PBC controller to the inertia wheel inverted pendulum in the presence of matched disturbances. Simulation and real-time experimental results mirror the theoretical results and prove the efficiency of the proposed controller.