Number Sense Development During the Preschool Years: Relations Within and Between Key Skill Indicators

Early numeracy skills are predictive of later mathematics achievement; therefore, technically adequate screening tools are needed to identify young children who may be at risk for developing mathematics difficulties. The Individual Growth and Development Indicators - Early Numeracy (myIGDI-EN) is a curriculum-based measure of four key early numeracy skills: quantity comparison fluency (QCF), oral counting fluency (OCF), one-to-one correspondence counting fluency (OOCCF), and number naming fluency (NNF). MyIGDI-EN yields scores found to be technically adequate at one point in time and sensitive to growth across the preschool year in mixed-age samples of preschool children. However, age-based developmental trajectories of numeracy skills have yet to be modeled and are needed to inform assessment schedules and expectations for growth in the context of educational decision-making. Using an accelerated longitudinal design, this study sought to evaluate the developmental progression within and between the four skills measured by myIGDI-EN. Utilizing data from 408 preschool children, linear and latent basis growth curve models were evaluated in a structural equation modeling framework. Results indicated growth was represented nonlinearly across all myIGDI-EN tasks. Each task demonstrated significant age-based sensitivity to growth over the measured developmental period with the most growth evident on OCF and OOCCF. Significant variation in initial level of performance at 45 months was evident across tasks, as was significant variation in slope for all tasks except QCF. Intercept values suggest QCF is an earlier emerging skill and NNF a later emerging skill. Results strengthen and advance what is known about patterns of early numeracy growth and the suitability of myIGDI-EN for repeated measurement across the preschool years. Implications for practice and future research are discussed

Preface to the Sixth Workshop on Natural Language for Artificial Intelligence (NL4AI)

Natural Language Processing (NLP) is an important research topic in Artificial Intelligence (AI), as it is the target of different scientific and industrial interests. Natural Language is at the crossroad of Learning, Knowledge Representation, and Cognitive Modeling. Several recent AI achievements have repeatedly shown their beneficial impact on complex inference tasks, with huge application perspectives in linguistic modeling, processing, and inferences. However, Natural Language Understanding is still a rich research topic, whose cross-fertilization spans a number of independent areas such as Cognitive Computing, Robotics as well as HumanComputer Interaction. For AI, Natural Languages are the research focus of paradigms and applications but, at the same time, they act as cornerstones of automation, autonomy, and learnability for most intelligent phenomena ranging from Vision to Planning and Social Behaviors. A reflection about such diverse and promising interactions is an important target for current AI studies, fully in the core mission of AI*IA. This workshop, supported by the Special Interest Group on NLP of AI*IA1 and by the Italian Association of Computational Linguistics (AILC)2, aims at providing a broad overview of recent activities in the eld of Human Language Technologies (HLT) in Italy. In this context, the organization of NL4AI 2021 [1] provided researchers with the opportunity to share experiences and insights about AI applications focused on NLP in several domains. The 2022 edition of NL4AI is co-located with the 21th International Conference of the Italian Association for Artificial Intelligence (AIxIA 2022), taking place on November 30th in Udine, Italy. The program of the meeting is available on the official workshop website3. We received 17 submissions, 14 of which were accepted after peer-review

Analogies between Internal Model Control and Predictive Control algorithms

Internal Model Control (IMC) is a well-known control strategy provided with simple tuning rules requiring a model in order to control a given single-input single-output plant; furthermore, it allows an easy and straightforward closed-loop analysis. However, it has some limitations. For instance, it cannot be applied to open-loop unstable plants, it does not cope easily with constraints, and disturbance rejection may be sluggish for disturbances other than output steps. On the other hand, Model Predictive Control (MPC), that still requires the definition of a model, has not limitation from the point of view of the nature of the plant, but it does not give allows simple CL analysis. IMC and MPC have many common features but, at the same time, they are also quite different control strategies: the goal we want to achieve in this work is to find a compromise between them that should have advantages of both control structures. In this work a Disturbance Observer Based Internal Model Control (DOB-IMC) is proposed: it works with an augmented model, classical IMC controller design is left unchanged, while the block standing for the model has been replaced by an observer block, where predicted states are ”filtered” through a Luenberger observer, known to deal better with dynamic disturbances rather than classic IMC deadbeat observer. Afterwards, this structure has been extended to open-loop unstable plants through application of the Q parametrization, and to integrating plants as well. The effectiveness of this control scheme has been validated through several simulations: first, different kind of Single-Input Single-Output linear systems have been tested; then, as a pratical application, the multivariable ”Shell oil fractionator” case study has been simulated with unmeasured disturbance and with saturated inputs

Observer-based offset-free internal model control

A linear feedback control structure is proposed that allows internal model control design principles to be applied to unstable and marginally stable plants. The control structure comprises an observer using an augmented plant model, state estimate feedback and disturbance estimate feedback. Conditions are given for both nominal internal stability and offset-free action even in the case of plant-model mismatch. The Youla parameterization is recovered as a limiting case with reduced order observers. The simple design methodology is illustrated for a marginally stable plant with delay

Comparing Transformer-based NER approaches for analysing textual medical diagnoses

The automated analysis of medical documents has grown in research interest in recent years as a consequence of the social relevance of the thematic and the difficulties often encountered with short and very specific documents. In particular, this fervent area of research has stimulated the development of several techniques of automatic document classification, question answering, and name entity recognition (NER). Nevertheless, many open issues must be addressed to obtain results that are satisfactory for a field in which the effectiveness of predictions is a fundamental factor in order not to make mistakes that could compromise people’s lives. To this end, we focused on the name entity recognition task from medical documents and, in this work, we will discuss the results we obtained by our hybrid approach. In order to take advantage of the most relevant findings in the field of natural language processing, we decided to focus on deep neural network models. We compared several configurations of our model by varying the transformer architecture, such as BERT, RoBERTa and ELECTRA, until we obtained a configuration that we considered the best for our goals. The most promising model was used to participate in the SpRadIE task of the annual CLEF (Conference and Labs of the Evaluation Forum). The obtained results are encouraging and can be of reference for future studies on the topic

From sensibility to signification: a poetic view of photography

Orientador: Ernesto Giovanni BoccaraDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ArtesResumo: Este trabalho propõe uma abordagem conceitual, dos diversos conteúdos da imagem fotográfica, a sua leitura e decodificação, que vai da sensibilidade aos significados. Apresenta uma proposta de análise que busca justificar e demonstrar a condição da poética (Arte) da fotografia. Nesse sentido, mostra o que de incomum pode deter o olhar que eterniza e o olhar que ressuscita, dando um real valor às imagens fotográficas, sejam elas quais forem e mostrem o que de mais importante possam mostrar, mantendo as informações ao longo do tempo. Desse modo, busca contribuir para uma melhor compreensão da época em que as fotografias foram feitas, dos cenários que as mesmas registram, de seus contextos, assim como das implicações e relações que tenham com as formas de expressão diferenciadas, que chamamos: ArteAbstract: This work proposes an conceptual approach to the various contents of photographic image, its interpretation, and decoding, which goes from sensibility to signification; it brings a proposal for an analysis that can effectively justify and demonstrate the condition of poetry (art) of photography, showing which of its uncommon aspects can capture the point of view that makes it eternal and the point of view that resuscitates it, bringing the real value to photographic images, being them whatever they are, showing whatever most important subject they can hold, maintaining their image content throughout time, and serving for a better understanding of the time in which photographs were taken, their scenarios, contexts, implications, and relationships that they have with the different forms of expression that we cal!: ArtMestradoMestre em Arte

GM-CTSC at SemEval-2020 Task 1: Gaussian Mixtures Cross Temporal Similarity Clustering

This paper describes the system proposed for the SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection. We focused our approach on the detection problem. Given the semantics of words captured by temporal word embeddings in different time periods, we investigate the use of unsupervised methods to detect when the target word has gained or loosed senses. To this end, we defined a new algorithm based on Gaussian Mixture Models to cluster the target similarities computed over the two periods. We compared the proposed approach with a number of similarity-based thresholds. We found that, although the performance of the detection methods varies across the word embedding algorithms, the combination of Gaussian Mixture with Temporal Referencing resulted in our best system

Aspectos Relevantes sobre a Organização do Poder Judiciário Espanhol: Seleção e Formação de Magistrados, a Reforma da Secretaria Judicial e a Figura do Secretário Judicial

El presente artículo, a fin de contribuir a los estudios sobre la política judicial, la gestión y la administración de la justicia en Brasil, ofrece una visión general de algunos aspectos relevantes de la organización del Poder Judicial español a saber, la selección y formación de los magistrados, la reforma de la secretaria judicial y la figura del secretario judicial, que son temas de interés actual en vista de las similitudes socio-culturales y jurídicas, y la coincidencia entre los problemas que enfrentan los poderes judiciales de Brasil y España. Aunque no contenga una sugestión de "importación" de soluciones acríticas extranjeras, propone una profundización del estudio de estos puntos, con el fin de evaluar los posibles impactos positivos que tuvieron en el caso español y verificar si no serían medidas interesantes a aplicarse en el escenario brasileño, especialmente en el caso de la secretaria judicial, cuya función consiste en aliviar el juez de la gestión de la unidad administrativa y de trabajos "burocráticos", para centrarse en la función judicial, al mismo tiempo en que permitiría la centralización y la especialización de la gestión y del trabajo administrativo.O presente artigo, com vistas a contribuir para os estudos sobre política judiciária, gestão e administração da justiça no Brasil, traça um panorama de alguns aspectos relevantes da organização do Poder Judiciário espanhol quais sejam, a seleção e formação de magistrados, a reforma da secretaria judicial e a figura do secretário judicial, que são temas de interesse na atualidade, em vista das similitudes sócio-culturais e jurídicas, e da coincidência entre os problemas enfrentados pelos Poderes Judiciários de Brasil e Espanha. Embora não contenha uma sugestão de importação acrítica de soluções estrangeiras, propõe um aprofundamento do estudo destes pontos, com o intuito de avaliar os possíveis impactos positivos que tiveram no caso espanhol e verificar se não seriam medidas interessantes a serem aplicadas no cenário brasileiro, sobretudo no caso do secretário judicial, cuja função é desafogar o juiz da gestão da  unidade  administrativa  e  de  trabalhos  burocráticos,  para  concentrar-se  na  função jurisdicional, ao mesmo tempo em que permitira a centralização e especialização da gestão e do trabalho administrativo