dataesr/scientific-paragraphs-categorization huggingface dataset:
Personalized medicine Personalized medicine is an overarching approach to medicine where diagnostics, prognostics and prediction of treatment response considers individual-specific factors, rather than those derived from patient populations. Its future role in clinical practice is widely accepted, where it has the potential to streamline and enhance the quality of patient management by improving on the 'one-size fits all/average patient' philosophy. The focus is on the individual patient: considering their genotype, phenotype, epigenetics, lifestyle, environmental exposures, etc. With the expanding volume and complexity of medical databases that characterize patients, their diseases and responses, precision medicine is becoming an increasingly viable premise to augment traditional methods  [120].
In medical imaging, it has already demonstrated high potential, powered by the availability of networked architectures and comprehensive labelled datasets  [123]. State-of-the-art applications of DL models in SM include tailored treatment plans, drug discovery and development, and accurate disease characteristic identification  [118, 123]. For example, Liu et al. [124]  developed a CNN-based pipeline for MR-based treatment planning in radiation therapy on brain tumor patients, which can produce comparable plans relative to CT-based methods. Suresh et al. [125]  proposed a CNN model for prediction of clinical intervention within intensive care units. Coupled with patient's clinical risk factors, an image-based DL framework named Deep Profiler which is capable of individualizing radiation dose, has been developed to deliver personalized radiation therapy to patients  [126].
It is anticipated that the incorporation of EHR into predictive modelling could drive personalized medicine. Indeed, several EHR-based DL systems have been developed  [137]. Using raw EHR data including free-text notes which formed the patient's personalized input in temporal order, Rajkomar et al. [138]  developed DL approaches for the extraction of curated predictor variables from normalized EHR data, and they were capable of accurately producing predictions for a variety of clinical problems (inhospital mortality, 30-day unplanned readmission, prolonged length of stay and patient's final discharge diagnoses). In an attempt to improve the characterization of a patient's clinical phenotype, Rashidian et al. [139]  applied DL methods to analyze a range of data extracted from EHR (e. g. demographic, laboratory and medication data plus past diagnoses), and to predict International Classification of Disease codes with high accuracy for three test cases (diabetes, acute renal failure and chronic kidney disease).
Beyond the applications for early diagnosis of a disease, DL has shown the potential to improve palliative care. Avati et al. [147]  applied a fully connected DNN to evaluate all EHR data of all admitted patients and identify those at risk for death within the next 3-to 12-month period. Thus, a proactive approach could be taken to reach out to those who may benefit from palliative care consultation and engage patients and their families in informed decision making.
DL in action: a case study on PD As previously introduced in the DL and SM section, PD is a good use case for DL, due to the complexity of the disease and its manifestation, with symptoms usually appearing late and hence preventing an early intervention; and due to the vast array of data that could be used for its study. To conclude this review, we here show some examples of how the previously described DL techniques have been put into action in this disease. The first natural step towards a better treatment of PD is improving its diagnosis, especially in the case of atypical manifestations, and with the objective of reducing the subjectivity of the process. One of the most characteristic features of PD is that it modifies movement control, and hence initially affects gestures such as writing or drawing spirals.
To illustrate, data were recorded with inertial measurement units  [154] , and it has been shown that the precision in detecting events of bradykinesia, i. e. of the slowness of movement, with DL algorithms was at least 4. 6% higher with respect to other state-of-the-art ML techniques. In a similar fashion, DL models have been shown to achieve a 90% precision, as opposed to the 83% of classical classification methods, in the problem of detecting events of freezing of gait. Other examples of the use of wearable sensors, and most notably of motion sensors included in standard smartphones, are presented by several research teams  [155] [156] [157]. 'DL will thus allow patients to be followed in their daily life, to analyze data provided by commonly available sensors and to promptly detect adverse episodes and inform the physician about the real course of the disease.
Finally, the ultimate goal of any analysis is to detect ways for slowing down, or ideally stop the progression of the condition. In this sense, a promising line is yielded by drug repurposing. For example, Zeng et al. [158]  reported a methodology for in silico drug repurposing, based on a network deep-learning approach, which integrates known relationships between drugs, diseases, side effects and targets. When results were validated against the ClinicalTrials. gov database, these included previously approved drugs for PD (i. e. methylphenidate and pergolide).
One may argue that the large number of hyperparameters shared by DNNs makes them an appropriate model of the brain  [162]. It, however, poses two great challenges  [21]. Most DL algorithms have assumed sufficient and balanced training data, which may not be the case in some SM applications. Chen et al. compared 5 ML methods with 2 DL models using 5 clinical datasets and found that conventional ML methods generated better performance when compared with the DL alternatives in most of cases when training data are relatively small. For instance, when applied to the prediction of time to first treatment or patients diagnosed with chronic lymphocytic leukemia, the highest AUC value (0. 924) was obtained by Random Forest and the DL model only achieved an AUC of 0. 802. In addition, having many parameters tends to make a model adapt to the data too much, though the risk of overfitting could be reduced through various regularization approaches such as dropout  [17, 92].
Based on the analysis of 12 314 sMRI images taken from the UK Biobank repository, they demonstrated that DL approaches significantly surpassed ML models and consistently achieved better performance with an increase of sample size. Song et al. [166]  reported a CNN-based AI assistance system deployed at the Chinese PLA General Hospital for gastric cancer detection. It underwent a 3month trial run with the daily gastric dataset and the stable performance with AUC above 0. 98 was achieved across timeline. To prove its clinical utility, the system was tested using the data collected from other hospitals, i. e. Peking Union Medical College Hospital and Cancer Hospital, Chinese Academy of Medical Sciences. Consistent performance was achieved, demonstrating the feasibility and benefits of using DL-based histopathological assistance systems in routine clinical practice scenarios.
Acknowledgement This publication is based upon work from  COST Action Open Multiscale Systems Medicine (OpenMultiMed ,  CA15120 ), supported by  COST (European Cooperation in Science and Technology). COST is funded by the  Horizon   2020 Framework Programme  of the  European Union. HZ and HYW are also supported by the MetaPlat(690998), SenseCare(690862) and STOP(823978) projects funded by  H2020 RISE  programme. FC and PT acknowledge the support of  H2020 project iPC "individualized Paediatric Cure " (826121). Participation of V. S. in OpenMultiMed is supported by the  Czech Ministry of Education, Youth and Sports  (project  LTC18074 ). JLM. thanks  Escola Superior de Tecnologia e Gestão ,  Instituto Politécnico de Portalegre (ESTG/IPP) ; and  Centro de Recursos Naturais e Ambiente, Instituto Superior Técnico (CERENA/IST)  within the support of  FCT-Fundação para a Ciência e a Tecnologia  through the strategic project  FCT-UIDB/04028/2020.
2 million images from the ImageNet dataset. The details of the trained model can be found at https://github. com/riblidezso/frcnn_cad c The model used includes the two layer sparse autoencoders each having two hidden layers. There are 400 and 225 hidden units in the first and second hidden layers, respectively. A set of 537 Hematoxylin and Eosin-stained histopathological images were obtained corresponding to 49 lymph node-negative and estrogen receptor-positive breast cancer (LN-, ER+ BC) patients. d Two DBNs having depths of 2 and 3, respectively, were constructed and trained. All the layers have 100 hidden units apart from the top layer in the second DBN, which has 150 hidden units. Pretraining and fine-tuning learning rates of 0. 01 were used as hyper parameters for both DBN models. Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets were used.
Introduction The East-European Conference on Advances in Databases and Information Systems (ADBIS) aims at providing a forum where researchers and practitioners in the fields of databases and information systems can interact, exchange ideas and disseminate their accomplishments and visions. Inaugurated 24 years ago, ADBIS originally included communities from Central and Eastern Europe, however, throughout its lifetime it has spread and grown to include participants from many other countries throughout the world. The ADBIS conferences provide an international platform for the presentation of research on database theory, development of advanced DBMS technologies, and their advanced applications. The ADBIS series of conferences aims at providing a forum for the presentation and dissemination of research on database theory, development of advanced DBMS technologies, and their advanced applications.
Knowledge bases can be built as these new generation infrastructures which support data science queries on a user-friendly framework and are capable of providing the required machinery for advised decision-making. , DOING Workshop focuses on transforming data into information and then into knowledge. It gathers researchers from Natural Language Processing (NLP), Databases (DB), and Artificial Intelligence (AI). This edition features works in two main areas: (1) information extraction from textual data and its representation on knowledge bases; (2) intelligent methods for handling and maintaining these databases: new forms of requests, including efficient, flexible, and secure analysis mechanisms, adapted to the user, and with quality and privacy preservation guarantees. Overall, the purpose of the workshop is to focus on all aspects concerning modern infrastructures to support these areas, giving particular attention, but not limited, to data on health and environmental domains.
Selected Papers The NLP for information extraction Session includes 4 papers. The first paper, entitled "Extraction of a Knowledge Graph from French Cultural Heritage Documents"  [17] , describes a method for extracting entities and relations in French heritage descriptive texts, using tools that are available only for English. It presents results using as input texts on Quebec's cultural heritage. Besides heritage-specific type identification, the paper contributes by showing how tools developed for English can be used for other languages with fewer resources available, such as French. The second paper, "Natural Language Querying System through Entity Enrichment"  [2]  focuses on translating natural language queries into database queries. The proposed approach is divided into a domain-dependent preprocessing and domain-independent query generation phases.
The Intelligent Data Management Session includes 5 papers. The session starts with the paper "Recursive Expressions for SPARQL Property Paths"  [18]. It proposes rcfSPARQL (restricted-context-free SPARQL), an extension to SPARQL with a subset of context-free languages. It is based on recursive expressions, which the authors claim that are more user-friendly than context-free languages for writing queries. The second paper of the session, titled "Healthcare decision-making over a geographic, socioeconomic, and image data warehouse"  [25]  tackles the problem of processing healthcare analytical queries that involve geographic, socioeconomic and image data. To this end, it proposes three storage models (jointed, split and normalized) and presents an experimental study that determines their performance on a cluster running Spark extended with similarity predicates.
Despite that, one of the hot issues in practice is still how to effectively transform large amounts of daily collected operational data into the useful knowledge from the perspective of declared company goals, and how to set up the information design process aimed at production of effective software services. The main goal of the Modern Approaches in Data Engineering and Information System Design (MADEISD) workshop is to address open questions and real potentials for various applications of modern approaches and technologies in data engineering and information system design so as to develop and implement effective software services in a support of information management in various organization systems. Intention was to address interdisciplinary character of a set of theories, methodologies, processes, architectures, and technologies in disciplines such as Data Engineering, Information System Design, Big Data, NoSQL Systems, and Model Driven Approaches in a development of effective software services.
While, standard approaches of fraudulence monitoring mainly have the focus on the individual client data, the authors' approach concentrate on the hidden data produced by the network of a transaction database. The methodology is based on a cycle detection method with the help of which important patterns can be identified as shown by the test on real data. Proposed solution is integrated into a financial fraud system of a bank. One of the most common imaging methods for diagnosing an abdominal aortic aneurysm, and an endoleak detection is computed tomography angiography. In the paper "Abdominal Aortic Aneurysm segmentation from contrastenhanced computed tomography angiography using deep convolutional networks"  [10] , the authors address the problem of aorta and thrombus semantic segmentation, what is a mandatory step to estimate aortic aneurysm diameter.
To this end, the authors introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which characterises 14M papers and 8M patents according to the research topics drawn from the Computer Science Ontology. 4M papers and 5M patents are also classified according to the type of the author's affiliations (academy, industry, or collaborative) and 66 industrial sectors (e. g. , automotive, financial, energy, electronics) obtained from DBpedia. AIDA was automatically generated by integrating different bibliographic corpora, such as Microsoft Academic Graph, Dimensions, English DBpedia, the Computer Science Ontology, and the Global Research Identifier Database. The last paper, "A Philological Perspective on Meta-Scientific Knowledge Graphs"  [30]  discusses knowledge graphs and networks on the scientific process from a philological point of view.
BBIGAP 2020: 2nd Workshop of BI and Big Data Applications Description. The 2nd Workshop of BI & Big Data Applications (BBIGAP 2020), organized by Fadila Bentayeb and Omar Boussaid (University of Lyon 2, France). BBIGAP focuses on BI and big data applications. Big Data becomes a huge opportunity for computer science research but it also revolutionizes many fields, including business, social science, social media, medicine, public administration, and so on. In this case, big data requires a revisit of data management and data analysis techniques in fundamental ways at all stages from data acquisition and storage to data transformation, analysis, and interpretation. The types of available data fall into various categories: social data (e. g. , Twitter feeds, Facebook likes), data about mobility and geospatial locations (e.
g. , sensor data collected through mobile phones or satellite images), data collected from government administrative sources and multilingual text datasets, only to name a few. Big data bring us into a new scientific and technological era offering architectures and infrastructure (clouds, Hadoop-like computing, NoSQL databases) that allow better data management and analytics for decision-making. BBIGAP workshop received 7 submissions, out of which 3 were accepted as full papers and 1 as a short paper, resulting in an acceptance rate of 50%. Each paper received three reviews from members of the program committee.
Selected Papers This edition of BBIGAP 2020 workshop includes four papers. The first paper, "A Scored Semantic Cache Replacement Strategy for Mobile Cloud Database Systems"  [4]  considers the problem of determining the best cache entry to replace in the case of a mobile device accessing a cloud database system. The key idea is that, instead of traditional approaches (e. g. , LRU, LFU), replacement techniques must take into account metrics such as current battery life, location, and connectivity quality. For this, they proposed a cache replacement method for mobile cloud database systems that utilizes decisional semantic caching. Specifically, they proposed the Lowest Scored Replacement policy (LSR), a method that uses scored metrics that determine cache relevancy and the mobile devices constraints.
The objective is to derive query execution plans (QEPs) expressed as tuples (money, time, energy) and then compute the QEP that best suits the user's requirements. The second paper, "Grid Based Clustering of Waze Data on a Relational Database"  [9]  investigates the effect of a grid clustering on the performance of spatial queries, using a relational database. The authors proposed to organize spatiotemporal data as a set of relational tables, using a clustering strategy in order to group together spatiotemporal events. The objective is to show the benefits of organizing the spatial data in these clustering structures for answering spatial queries. Given data collected from traffic events, the authors proposed an approach for partitioning a geographic area of interest. They implemented their approach by using data from Waze over a period of one year, in a specific geographic area.
Selected Papers We selected two papers. In the first paper, "Towards the detection of promising processes by analysing the relational data"  [24] , the authors cope with the problem of converting relational data into processable event logs observing that multiple views can be obtained from the same relational model fostering different business process analytics. In the last paper, "Analysis of language inspired trace representation for anomaly detection"  [28] , the authors develop a comparative study about approaches using vector space modeling for trace profiling. Their comparison can guide the appropriate trace profiling choice for all methods working at the intersection of Process Mining and Machine Learning. The first edition of the International Workshop on Assessing Impact and Merit in Science (AIMinScience 2020) was held in conjunction with the 24th International Conference on Theory and Practice of Digital Libraries (TPDL 2020).
Research assessment attracts the interest of researchers and professionals from diverse disciplines. For example, librarians have been working on scientometrics for many decades, while the problems regarding the management and processing of large amounts of scholarly data for research assessment applications has attracted the attention of data scientists recently. The workshop aimed to bring together professionals in academia and industry with such diverse backgrounds being interested in the aforementioned topics and to encourage their interaction. This was facilitated by the fact that this year, TPDL was co-located and co-organized with ADBIS and EDA. AIMinScience 2020 accepted for presentation 2 full papers and 3 short papers. The program of the workshop also included 3 invited talks and one special session that presented the results of a hackathon.
In the present work, we provide an overview of the challenges rising in the field and a taxonomy of the existing approaches to identify the open issues that are yet to be addressed. Scientific careers: evolution, interdisciplinarity, gender, and the chaperone effect, by Prof. Roberta Sinatra. The unprecedented availability of large scale datasets about scholarly output has advanced quantitatively our understanding of how science progresses. In this talk we present a series of findings from the analysis and modelling of largescale datasets of publications and of scientific careers. We focus on individual scientific careers and tackle the following questions: How does impact evolve in a career? What is the role of gender and of scientific chaperones in dropout and achieving high impact? How interdisciplinary is our recognition system? We show that impact, as measured by influential publications, is distributed randomly within a scientist's sequence of publications, and formulate a stochastic model that uncouples the effects of productivity, individual ability, and luck in scientific careers.
This paper presents the Open Science Observatory, a prototype online platform that combines data gathered from OpenAIRE e-Infrastructure and other public data sources and informs users via rich visualizations on different OS indicators in Europe. The last paper, "Skyline-based university rankings"  [27]  proposes a novel university ranking method based on the skyline operator, which is used on multidimensional objects to extract the non-dominated (i. e. "prevailing") ones. This method is characterized by several advantages, such as it is transparent, reproducible, without any arbitrarily selected parameters, based on the research output of universities only and not on publicly not traceable or random questionnaires. The proposed method does not provide meaningless absolute rankings but rather it ranks universities categorized in equivalence classes.
This method is evaluated using data extracted from Microsoft Academic. The ADBIS, TPDL & EDA 2020 DC was a forum where Ph. D. students from the database and digital library communities had a chance to present their research ideas. They could gain inspiration and receive feedback from their peers and senior researchers, and to tie cooperation bounds. The DC papers aimed at describing the current status of the thesis research. The DC Committee accepted five presentations, two of which were included in the satellite events proceedings. The topics discussed at the DC included data management, data analysis, social aspects of information technologies, and digitisation of cultural heritage.
Conclusion We hope readers will find the content of this volume interesting and will be inspired to look further into the challenges that are still ahead for the design of advanced databases and information systems, with a special reference to Big Data, AI and Connected World. We are really sure that this volume content will stimulate new ideas for further research and developments by both the scientific and industrial communities. ADBIS, TPDL & EDA 2020 workshops and Doctoral Consortium organizers would like to express their thanks to everyone who contributed to the volume content. We thank the authors, who submitted papers. Special thanks go to the Program Committee members as well as to the external reviewers of the main conferences and of each satellite event, for their support in evaluating the submitted papers, providing comprehensive, critical, and constructive comments, and ensuring the quality of the scientific program and of this volume.