Developing an Assessment Checklist

The goal of this project was to prepare an assessment checklist for the Information Retrieval (IR) course at the department of Computer Science in the academic year 2019/2020. This project was motivated by some observations regarding the previous edition of the IR course: in the 2018/2019 edition there was a clear mismatch between students and teachers expectations regarding the assignment. The students were struggling in understanding how to structure and write a good quality assignment. Furthermore, even if the students were instructed with guidelines on how to give feedback, they were struggling also in providing useful feedback to their peers. With the proposed assessment checklist, we aimed at guiding and helping students in structuring their assignment and peer reviews. This paper is organised as follows: Section 1 describes the course during which the project was carried out; Section 2 presents the project goals and motivations; Section 3 carefully describes how the project was conducted; Section 4 reports some analysis about the project results; and Section 5 presents conclusions and future challenges

Challenges and Perspectives in Lunar Communication and Navigation Systems for Future Cis-lunar Space Exploration Missions

openWith the increasing interest in lunar exploration and potential for human habitation, the need for accurate and reliable navigation systems on the Moon has become more crucial than ever. The absence of a well-established lunar navigation infrastructure poses significant challenges to future space missions, including safe landing, trajectory planning, and autonomous operations. In this context, the development of a Lunar communication and navigation system (LCNS) capable of providing precise and real-time navigation services is of utmost importance. This thesis initially presents European space agency (ESA)’s Moonlight mission, and then goes on to describe various proposed implementations of LCNS, considering its technical feasibility, performance, and potential applications. The first stage involves Global navigation satellite system (GNSS)-only technologies and architectures, then moves into the second one where the proposals are GNSS-aided, and finally in the third one, we move on to Lunar-only implementations, thus totally independent of GNSS. Ultimately, the thesis concludes that while significant challenges exist, innovative solutions can ensure the development of robust communication and navigation systems for future cis-lunar space exploration missions.With the increasing interest in lunar exploration and potential for human habitation, the need for accurate and reliable navigation systems on the Moon has become more crucial than ever. The absence of a well-established lunar navigation infrastructure poses significant challenges to future space missions, including safe landing, trajectory planning, and autonomous operations. In this context, the development of a Lunar communication and navigation system (LCNS) capable of providing precise and real-time navigation services is of utmost importance. This thesis initially presents European space agency (ESA)’s Moonlight mission, and then goes on to describe various proposed implementations of LCNS, considering its technical feasibility, performance, and potential applications. The first stage involves Global navigation satellite system (GNSS)-only technologies and architectures, then moves into the second one where the proposals are GNSS-aided, and finally in the third one, we move on to Lunar-only implementations, thus totally independent of GNSS. Ultimately, the thesis concludes that while significant challenges exist, innovative solutions can ensure the development of robust communication and navigation systems for future cis-lunar space exploration missions

Learning Recommendations from User Actions in the Item-poor Insurance Domain

While personalised recommendations are successful in domains like retail, where large volumes of user feedback on items are available, the generation of automatic recommendations in data-sparse domains, like insurance purchasing, is an open problem. The insurance domain is notoriously data-sparse because the number of products is typically low (compared to retail) and they are usually purchased to last for a long time. Also, many users still prefer the telephone over the web for purchasing products, reducing the amount of web-logged user interactions. To address this, we present a recurrent neural network recommendation model that uses past user sessions as signals for learning recommendations. Learning from past user sessions allows dealing with the data scarcity of the insurance domain. Specifically, our model learns from several types of user actions that are not always associated with items, and unlike all prior session-based recommendation models, it models relationships between input sessions and a target action (purchasing insurance) that does not take place within the input sessions. Evaluation on a real-world dataset from the insurance domain (ca. 44K users, 16 items, 54K purchases, and 117K sessions) against several state-of-the-art baselines shows that our model outperforms the baselines notably. Ablation analysis shows that this is mainly due to the learning of dependencies across sessions in our model. We contribute the first ever session-based model for insurance recommendation, and make available our dataset to the research community

A educação sexual no PIBID/Biologia

A Educação Sexual é necessária no processo formativo dos jovens, masmuitos professores não estão preparados para lidar com a temática. Considerando a importância de se formar educadores sexuais e a necessidade de levar discussões e reflexões sobre sexualidade para o contexto escolar, bolsistas do curso de Ciências Biológicas, participantes do Programa Institucional de Bolsa de Iniciação à Docência(PIBID)realizaram atividades sobre sexualidade com alunos de escolaspúblicas.Por meio de entrevistas, buscamos compreender o que a realização dessas atividades significou para eles.Para esses licenciandos, trabalhar a Educação Sexual na escola pública representou a superação de dificuldades com a temática na prática e também colaborou na formação dos futuros professores

Genotoxicity assessment of the antimalarial compound artesunate in somatic cells of mice

Artesunate is a derivate of artemisinin that is both an antimalarial agent and acts cytotoxically on tumor cells. Despite its therapeutic use, its in vivo genotoxic potential has still not been evaluated. This study, therefore, was an investigation into the effects of a single oral administration of artesunate with an in vivo comet assay that analyzed leukocytes from peripheral blood and liver cells, and a micronucleus (MN) assay of bone marrow cells from male Swiss mice. the artesunate was administered by oral gavage at doses of 5, 50 and 100 mg/kg. Cytotoxicity was assessed by scoring 200 consecutive polychromatic (PCE) and normochromatic (NCE) erythrocytes (PCE/NCE ratio). the results demonstrate that artesunate induced significant DNA damage only in liver cells and that high doses of artesunate caused an increase in the mean number of micronucleated polychromatic erythrocytes (MNPCE). Under our experimental conditions, artesunate showed weak genotoxic effects at low doses and clastogenic effects at high doses. the PCE/NCE ratio indicated no cytotoxicity. the data obtained suggest caution about either continuous or high-dose use of artesunate by humans. (C) 2011 Elsevier B.V. All rights reserved.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Univ Estadual Paulista, UNESP, Fac Filosofia & Ciencias, Dept Fonoaudiol, BR-17525900 Marilia, SP, BrazilUniv Estadual Paulista, UNESP, Inst Biociencias, Programa Posgrad Biol Geral & Aplicada, BR-18618970 Botucatu, SP, BrazilUniversidade Federal de São Paulo Unifesp, Dept Ciencias Exatas & Terra, BR-09972270 Diadema, SP, BrazilUniversidade Federal de São Paulo Unifesp, Dept Ciencias Exatas & Terra, BR-09972270 Diadema, SP, BrazilCNPq: 306544/2006-7FAPESP: 2008/51175-7Web of Scienc

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Exploiting user signals and stochastic models to improve information retrieval systems and evaluation

The leitmotiv throughout this thesis is represented by IR evaluation. We discuss different issues related to effectiveness measures and novel solutions that we propose to address these challenges. We start by providing a formal definition of utility-oriented measurement of retrieval effectiveness, based on the representational theory of measurement. The proposed theoretical framework contributes to a better understanding of the problem complexities, separating those due to the inherent problems in comparing systems, from those due to the expected numerical properties of measures. We then propose AWARE, a probabilistic framework for dealing with the noise and inconsistencies introduced when relevance labels are gathered with multiple crowd assessors. By modeling relevance judgements and crowd assessors as sources of uncertainty, we directly combine the performance measures computed on the ground-truth generated by each crowd assessor, instead of adopting a classification technique to merge the labels at pool level. Finally, we investigate evaluation measures able to account for user signals. We propose a new user model based on Markov chains, that allows the user to scan the result list with many degrees of freedom. We exploit this Markovian model in order to inject user models into precision, defining a new family of evaluation measures, and we embed this model as objective function of an LtR algorithm to improve system performances