title: Naturally Derived Membrane Lipids Impact Nanoparticle-Based Messenger RNA Delivery

text: INTRODUCTION: Lipid based nanoparticles (LNPs) are clinically successful vectors for hepatic delivery of nucleic acids. These systems are being developed for non-hepatic delivery of mRNA for the treatment of diseases like cystic fibrosis or retinal degeneration as well as infectious diseases. Localized delivery to the lungs requires aerosolization. We hypothesized that structural lipids within LNPs would provide features of integrity which can be tuned for attributes required for efficient hepatic and non-hepatic gene delivery. Herein, we explored whether naturally occurring lipids that originate from the cell membrane of plants and microorganisms enhance mRNA-based gene transfection in vitro and in vivo and whether they assist in maintaining mRNA activity after nebulization. METHODS: We substituted DSPC, a structural lipid used in a conventional LNP formulation, to a series of naturally occurring membrane lipids. We measured the effect of these membrane lipids on size, encapsulation efficiency and their impact on transfection efficiency. We further characterized LNPs after nebulization and measured whether they retained their transfection efficiency. RESULTS: One plant-derived structural lipid, DGTS, led to a significant improvement in liver transfection of mRNA. DGTS LNPs had similar transfection ability when administered in the nasal cavity to conventional LNPs. In contrast, we found that DGTS LNPs had reduced transfection efficiency in cells pre-and post-nebulization while maintaining size and encapsulation similar to DSPC LNPs. CONCLUSIONS: We found that structural lipids provide differential mRNA-based activities in vitro and in vivo which also depend on the mode of administration. Understanding influence of structural lipids on nanoparticle morphology and structure can lead to engineering potent materials for mRNA-based gene therapy applications.


title: Development of Smart Healthcare Monitoring System in IoT Environment

text: Healthcare monitoring system in hospitals and many other health centers has experienced significant growth, and portable healthcare monitoring systems with emerging technologies are becoming of great concern to many countries worldwide nowadays. The advent of Internet of Things (IoT) technologies facilitates the progress of healthcare from face-to-face consulting to telemedicine. This paper proposes a smart healthcare system in IoT environment that can monitor a patient’s basic health signs as well as the room condition where the patients are now in real-time. In this system, five sensors are used to capture the data from hospital environment named heart beat sensor, body temperature sensor, room temperature sensor, CO sensor, and CO(2) sensor. The error percentage of the developed scheme is within a certain limit (< 5%) for each case. The condition of the patients is conveyed via a portal to medical staff, where they can process and analyze the current situation of the patients. The developed prototype is well suited for healthcare monitoring that is proved by the effectiveness of the system.


Provando e Riprovando: Galilean science in times of virulence


title: Food Insecurity in Europe: A Gender Perspective

text: Food insecurity is the limited or uncertain availability of nutritionally adequate and safe foods or limited or uncertain ability to acquire foods in socially acceptable ways. The study presents a comparison of the principal determinants of individual food insecurity in Europe and other Continents, with particular regard to gender, since the literature clearly states the importance of women in the administration of food in the household. The study of gender related differences in food insecurity is particularly important in Europe, since women experience food insecurity at a larger extent than men, but with a variability related to the geographical distribution and with complex relationships with economic and social drivers. Using a large international sample of individual level data, that allows the analysis for developed Countries for the first time, and the first experiential measure of food insecurity comparable at the global level, the paper analyses the principal determinants of gender differences in food insecurity. In order to verify if women’s vulnerability in food insecurity is moderated by specific factors, the modelling approach allows gender to vary by education, poverty, place of residence. The results suggest that the driver that could most mitigate women disadvantage is education: people with a university degree present a lower probability of experiencing food insecurity, both for men and for women. On the contrary, familial characteristics, such as the number of children in the household, present a higher impact on women’s food insecurity than on men’s.


title: Planning and Animal Welfare Law: Considering Why Planning Authorities in the Republic of Ireland Should have Regard to Animal Welfare Laws as Part of the Planning Process

text: Animal welfare legislation routinely places obligations on owners of animals to take action to ensure that the welfare of the animal(s) is protected. Such legislation often requires animals to be sheltered by appropriate housing. Where facilities are built to provide animal housing it may be necessary to seek planning permission for the development, this is particularly an issue in relation to commercial facilities. The general approach of planning authorities in the Republic of Ireland is not to have regard to animal welfare matters at the planning stage. With reference to commercial dog breeding this paper considers the implications of the failure to have regard to animal welfare legislation during the planning process and the impact this can have on both animal welfare, but also the risks such an approach poses to the environment and public health.


title: Executive function training in very preterm children: a randomized controlled trial

text: Objective of the current study was to assess whether game-formatted executive function (EF) training, is effective in improving attention, EF and academic performance in very preterm and/or extremely low birthweight children aged 8–12 years. A multi-center, double-blind, placebo- and waitlist controlled randomized trial (NTR5365) in two academic hospitals in The Netherlands was performed. Eighty-five very preterm children with parent-rated attention problems on the Child Behavior Checklist were randomized to one of three treatment conditions: EF training, placebo training or waitlist condition. EF or placebo training was completed at home (6 weeks, 25 sessions of 30–45 min each). At baseline, 2 weeks after training or being on the waitlist, and five months after first follow-up visit, children underwent assessments of primary outcomes (parent and teacher ratings of attention) and secondary outcomes (parent and teacher ratings of daily-life EF, computerized EF tasks and academic performance). Linear mixed model analyses were performed for all outcome measures. There were no significant differences in improvement over time on parent- and teacher ratings of attention, parent- and teacher ratings of daily-life EF, computerized EF tasks, and academic performance (arithmetic and reading) between the EF training, placebo training and waitlist condition. In conclusion, game-formatted EF training does not improve attention, EF or academic performance in very preterm children with parent-rated attention problems. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00787-020-01561-0) contains supplementary material, which is available to authorized users.


title: Physical Contact and Loneliness: Being Touched Reduces Perceptions of Loneliness

text: OBJECTIVE: This article addresses the connection between loneliness and physical contact. Evolutionary and psychological research has shown that touch is an important part of bond-building and emotion communication; loneliness is intimately related to these elements as well. In this paper, we ask whether physical contact reduces feelings of loneliness —which might derive from evolutionary ancient bonding mechanisms—despite a cultural context that is relatively non-tactile. METHOD: An experimental study (40 participants, 13 males) tested for observable effects of touch on loneliness scores in a low-contact culture to analyse whether they respond positively to that stimulus despite cultural training against it. RESULTS: Participants exposed to physical contact reported significantly lower neglect scores from their close relationships in a short loneliness scale, thus suggesting that there is an underlying mechanism that persists despite enculturation. The effects were particularly strong among single people, which could mean that lower loneliness among married people might be partly explained by the regular availability of physical contact. Participants in the experimental condition also showed a faster reduction in heart rate, interpreted as a sign of physiological wellbeing. CONCLUSIONS: These findings help to specify mechanisms within the evolutionary theoretical framework of loneliness that link internal feelings to environmental cues. This article aims at contributing to a more complex discussion on the interactions between emotions, cultural practices and psychological well-being.


In This Issue/Research Watch/News-in-Brief


Pandemic versus pandemonium: fighting on two fronts


title: Partial Domain Adaptation for Relation Extraction Based on Adversarial Learning

text: Relation extraction methods based on domain adaptation have begun to be extensively applied in specific domains to alleviate the pressure of insufficient annotated corpus, which enables learning by utilizing the training data set of a related domain. However, the negative transfer may occur during the adaptive process due to differences in data distribution between domains. Besides, it is difficult to achieve a fine-grained alignment of relation category without fully mining the multi-mode data structure. Furthermore, as a common application scenario, partial domain adaptation (PDA) refers to domain adaptive behavior when the relation class set of a specific domain is a subset of the related domain. In this case, some outliers belonging to the related domain will reduce the performance of the model. To solve these problems, a novel model based on a multi-adversarial module for partial domain adaptation (MAPDA) is proposed in this study. We design a weight mechanism to mitigate the impact of noise samples and outlier categories, and embed several adversarial networks to realize various category alignments between domains. Experimental results demonstrate that our proposed model significantly improves the state-of-the-art performance of relation extraction implemented in domain adaptation.


title: Modular Graphical Ontology Engineering Evaluated

text: Ontology engineering is traditionally a complex and time-consuming process, requiring an intimate knowledge of description logic and predicting non-local effects of different ontological commitments. Pattern-based modular ontology engineering, coupled with a graphical modeling paradigm, can help make ontology engineering accessible to modellers with limited ontology expertise. We have developed CoModIDE, the Comprehensive Modular Ontology IDE, to develop and explore such a modeling approach. In this paper we present an evaluation of the CoModIDE tool, with a set of 21 subjects carrying out some typical modeling tasks. Our findings indicate that using CoModIDE improves task completion rate and reduces task completion time, compared to using standard Protégé. Further, our subjects report higher System Usability Scale (SUS) evaluation scores for CoModIDE, than for Protégé. The subjects also report certain room for improvements in the CoModIDE tool – notably, these comments all concern comparatively shallow UI bugs or issues, rather than limitations inherent in the proposed modeling method itself. We deduce that our modeling approach is viable, and propose some consequences for ontology engineering tool development.


title: SASOBUS: Semi-automatic Sentiment Domain Ontology Building Using Synsets

text: In this paper, a semi-automatic approach for building a sentiment domain ontology is proposed. Differently than other methods, this research makes use of synsets in term extraction, concept formation, and concept subsumption. Using several state-of-the-art hybrid aspect-based sentiment analysis methods like Ont + CABASC and Ont + LCR-Rot-hop on a standard dataset, the accuracies obtained using the semi-automatically built ontology as compared to the manually built one, are slightly lower (from approximately 87% to 84%). However, the user time needed for building the ontology is reduced by more than half (from 7 h to 3 h), thus showing the usefulness of this work. This is particularly useful for domains for which sentiment ontologies are not yet available.


title: A Knowledge Graph for Industry 4.0

text: One of the most crucial tasks for today’s knowledge workers is to get and retain a thorough overview on the latest state of the art. Especially in dynamic and evolving domains, the amount of relevant sources is constantly increasing, updating and overruling previous methods and approaches. For instance, the digital transformation of manufacturing systems, called Industry 4.0, currently faces an overwhelming amount of standardization efforts and reference initiatives, resulting in a sophisticated information environment. We propose a structured dataset in the form of a semantically annotated knowledge graph for Industry 4.0 related standards, norms and reference frameworks. The graph provides a Linked Data-conform collection of annotated, classified reference guidelines supporting newcomers and experts alike in understanding how to implement Industry 4.0 systems. We illustrate the suitability of the graph for various use cases, its already existing applications, present the maintenance process and evaluate its quality.


title: Embedding-Based Recommendations on Scholarly Knowledge Graphs

text: The increasing availability of scholarly metadata in the form of Knowledge Graphs (KG) offers opportunities for studying the structure of scholarly communication and evolution of science. Such KGs build the foundation for knowledge-driven tasks e.g., link discovery, prediction and entity classification which allow to provide recommendation services. Knowledge graph embedding (KGE) models have been investigated for such knowledge-driven tasks in different application domains. One of the applications of KGE models is to provide link predictions, which can also be viewed as a foundation for recommendation service, e.g. high confidence “co-author” links in a scholarly knowledge graph can be seen as suggested collaborations. In this paper, KGEs are reconciled with a specific loss function (Soft Margin) and examined with respect to their performance for co-authorship link prediction task on scholarly KGs. The results show a significant improvement in the accuracy of the experimented KGE models on the considered scholarly KGs using this specific loss. TransE with Soft Margin (TransE-SM) obtains a score of 79.5% Hits@10 for co-authorship link prediction task while the original TransE obtains 77.2%, on the same task. In terms of accuracy and Hits@10, TransE-SM also outperforms other state-of-the-art embedding models such as ComplEx, ConvE and RotatE in this setting. The predicted co-authorship links have been validated by evaluating profile of scholars.


title: Keyword Search over RDF Using Document-Centric Information Retrieval Systems

text: For ordinary users, the task of accessing knowledge graphs through structured query languages like SPARQL is rather demanding. As a result, various approaches exploit the simpler and widely used keyword-based search paradigm, either by translating keyword queries to structured queries, or by adopting classical information retrieval (IR) techniques. In this paper, we study and adapt Elasticsearch, an out-of-the-box document-centric IR system, for supporting keyword search over RDF datasets. Contrary to other works that mainly retrieve entities, we opt for retrieving triples, due to their expressiveness and informativeness. We specify the set of functional requirements and study the emerging questions related to the selection and weighting of the triple data to index, and the structuring and ranking of the retrieved results. Finally, we perform an extensive evaluation of the different factors that affect the IR performance for four different query types. The reported results are promising and offer useful insights on how different Elasticsearch configurations affect the retrieval effectiveness and efficiency.


title: ESBM: An Entity Summarization BenchMark

text: Entity summarization is the problem of computing an optimal compact summary for an entity by selecting a size-constrained subset of triples from RDF data. Entity summarization supports a multiplicity of applications and has led to fruitful research. However, there is a lack of evaluation efforts that cover the broad spectrum of existing systems. One reason is a lack of benchmarks for evaluation. Some benchmarks are no longer available, while others are small and have limitations. In this paper, we create an Entity Summarization BenchMark (ESBM) which overcomes the limitations of existing benchmarks and meets standard desiderata for a benchmark. Using this largest available benchmark for evaluating general-purpose entity summarizers, we perform the most extensive experiment to date where 9 existing systems are compared. Considering that all of these systems are unsupervised, we also implement and evaluate a supervised learning based system for reference.


title: SAShA: Semantic-Aware Shilling Attacks on Recommender Systems Exploiting Knowledge Graphs

text: Recommender systems (RS) play a focal position in modern user-centric online services. Among them, collaborative filtering (CF) approaches have shown leading accuracy performance compared to content-based filtering (CBF) methods. Their success is due to an effective exploitation of similarities/correlations encoded in user interaction patterns, which is computed by considering common items users rated in the past. However, their strength is also their weakness. Indeed, a malicious agent can alter recommendations by adding fake user profiles into the platform thereby altering the actual similarity values in an engineered way. The spread of well-curated information available in knowledge graphs ([Formula: see text]) has opened the door to several new possibilities in compromising the security of a recommender system. In fact, [Formula: see text] are a wealthy source of information that can dramatically increase the attacker’s (and the defender’s) knowledge of the underlying system. In this paper, we introduce SAShA, a new attack strategy that leverages semantic features extracted from a knowledge graph in order to strengthen the efficacy of the attack to standard CF models. We performed an extensive experimental evaluation in order to investigate whether SAShA is more effective than baseline attacks against CF models by taking into account the impact of various semantic features. Experimental results on two real-world datasets show the usefulness of our strategy in favor of attacker’s capacity in attacking CF models.


title: Entity Summarization with User Feedback

text: Semantic Web applications have benefited from entity summarization techniques which compute a compact summary for an entity by selecting a set of key triples from underlying data. A wide variety of entity summarizers have been developed. However, the quality of summaries they generate are still not satisfying, and we lack mechanisms for improving computed summaries. To address this challenge, in this paper we present the first study of entity summarization with user feedback. We consider a cooperative environment where a user reads the current entity summary and provides feedback to help an entity summarizer compute an improved summary. Our approach represents this iterative process as a Markov decision process where the entity summarizer is modeled as a reinforcement learning agent. To exploit user feedback, we represent the interdependence of triples in the current summary and the user feedback by a novel deep neural network which is incorporated into the policy of the agent. Our approach outperforms five baseline methods in extensive experiments with both real users and simulated users.


title: Processing SPARQL Aggregate Queries with Web Preemption

text: Executing aggregate queries on the web of data allows to compute useful statistics ranging from the number of properties per class in a dataset to the average life of famous scientists per country. However, processing aggregate queries on public SPARQL endpoints is challenging, mainly due to quotas enforcement that prevents queries to deliver complete results. Existing distributed query engines allow to go beyond quota limitations, but their data transfer and execution times are clearly prohibitive when processing aggregate queries. Following the web preemption model, we define a new preemptable aggregation operator that allows to suspend and resume aggregate queries. Web preemption allows to continue query execution beyond quota limits and server-side aggregation drastically reduces data transfer and execution time of aggregate queries. Experimental results demonstrate that our approach outperforms existing approaches by orders of magnitude in terms of execution time and the amount of transferred data.


title: StreamPipes Connect: Semantics-Based Edge Adapters for the IIoT

text: Accessing continuous time series data from various machines and sensors is a crucial task to enable data-driven decision making in the Industrial Internet of Things (IIoT). However, connecting data from industrial machines to real-time analytics software is still technically complex and time-consuming due to the heterogeneity of protocols, formats and sensor types. To mitigate these challenges, we present StreamPipes Connect, targeted at domain experts to ingest, harmonize, and share time series data as part of our industry-proven open source IIoT analytics toolbox StreamPipes. Our main contributions are (i) a semantic adapter model including automated transformation rules for pre-processing, and (ii) a distributed architecture design to instantiate adapters at edge nodes where the data originates. The evaluation of a conducted user study shows that domain experts are capable of connecting new sources in less than a minute by using our system. The presented solution is publicly available as part of the open source software Apache StreamPipes.


title: Equivalent Rewritings on Path Views with Binding Patterns

text: A view with a binding pattern is a parameterized query on a database. Such views are used, e.g., to model Web services. To answer a query on such views, the views have to be orchestrated together in execution plans. We show how queries can be rewritten into equivalent execution plans, which are guaranteed to deliver the same results as the query on all databases. We provide a correct and complete algorithm to find these plans for path views and atomic queries. Finally, we show that our method can be used to answer queries on real-world Web services.


title: YAGO 4: A Reason-able Knowledge Base

text: YAGO is one of the large knowledge bases in the Linked Open Data cloud. In this resource paper, we present its latest version, YAGO 4, which reconciles the rigorous typing and constraints of schema.org with the rich instance data of Wikidata. The resulting resource contains 2 billion type-consistent triples for 64 Million entities, and has a consistent ontology that allows semantic reasoning with OWL 2 description logics.


title: VQuAnDa: Verbalization QUestion ANswering DAtaset

text: Question Answering (QA) systems over Knowledge Graphs (KGs) aim to provide a concise answer to a given natural language question. Despite the significant evolution of QA methods over the past years, there are still some core lines of work, which are lagging behind. This is especially true for methods and datasets that support the verbalization of answers in natural language. Specifically, to the best of our knowledge, none of the existing Question Answering datasets provide any verbalization data for the question-query pairs. Hence, we aim to fill this gap by providing the first QA dataset VQuAnDa that includes the verbalization of each answer. We base VQuAnDa on a commonly used large-scale QA dataset – LC-QuAD, in order to support compatibility and continuity of previous work. We complement the dataset with baseline scores for measuring future training and evaluation work, by using a set of standard sequence to sequence models and sharing the results of the experiments. This resource empowers researchers to train and evaluate a variety of models to generate answer verbalizations.


title: Applying Knowledge Graphs as Integrated Semantic Information Model for the Computerized Engineering of Building Automation Systems

text: During the life cycle of a smart building, an extensive amount of heterogeneous information is required to plan, construct, operate and maintain the building and its technical systems. Traditionally, there is an information gap between the different phases and stakeholders, leading to information being exchanged, processed and stored in a variety of mostly human-readable documents. This paper shows how a knowledge graph can be established as integrated information model that can provide the required information for all phases in a machine-interpretable way. The knowledge graph describes and connects all relevant information, which allows combining and applying it in a holistic way. This makes the knowledge graph a key enabler for a variety of advanced, computerized engineering tasks, ranging from the planning and design phases over the commissioning and the operation of a building. The computerized engineering of building automation systems (BAS) with an advanced software tool chain is presented as such a use case in more detail. The knowledge graph is based on standard semantic web technologies and builds on existing ontologies, such as the Brick and QUDT ontologies, with various novel extensions presented in this paper. Special attention is given to the rich semantic definition of the entities, such as the equipment and the typically thousands of datapoints in a BAS, which can be achieved as a combination of contextual modeling and semantic tagging.


title: Unsupervised Bootstrapping of Active Learning for Entity Resolution

text: Entity resolution is one of the central challenges when integrating data from large numbers of data sources. Active learning for entity resolution aims to learn high-quality matching models while minimizing the human labeling effort by selecting only the most informative record pairs for labeling. Most active learning methods proposed so far, start with an empty set of labeled record pairs and iteratively improve the prediction quality of a classification model by asking for new labels. The absence of adequate labeled data in the early active learning iterations leads to unstable models of low quality which is known as the cold start problem. In our work we solve the cold start problem using an unsupervised matching method to bootstrap active learning. We implement a thresholding heuristic that considers pre-calculated similarity scores and assigns matching labels with some degree of noise at no manual labeling cost. The noisy labels are used for initializing the active learning process and throughout the whole active learning cycle for model learning and query selection. We evaluate our pipeline with six datasets from three different entity resolution settings using active learning with a committee-based query strategy and show it successfully deals with the cold start problem. Comparing our method against two active learning baselines without bootstrapping, we show that it can additionally lead to overall improved learned models in terms of [Formula: see text] score and stability.


title: Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion

text: Learning embeddings of entities and relations existing in knowledge bases allows the discovery of hidden patterns in them. In this work, we examine the contribution of geometrical space to the task of knowledge base completion. We focus on the family of translational models, whose performance has been lagging. We extend these models to the hyperbolic space so as to better reflect the topological properties of knowledge bases. We investigate the type of regularities that our model, dubbed HyperKG, can capture and show that it is a prominent candidate for effectively representing a subset of Datalog rules. We empirically show, using a variety of link prediction datasets, that hyperbolic space allows to narrow down significantly the performance gap between translational and bilinear models and effectively represent certain types of rules. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this chapter (10.1007/978-3-030-49461-2_12) contains supplementary material, which is available to authorized users.


title: Hybrid Reasoning Over Large Knowledge Bases Using On-The-Fly Knowledge Extraction

text: The success of logic-based methods for comparing entities heavily depends on the axioms that have been described for them in the Knowledge Base (KB). Due to the incompleteness of even large and well engineered KBs, such methods suffer from low recall when applied in real-world use cases. To address this, we designed a reasoning framework that combines logic-based subsumption with statistical methods for on-the-fly knowledge extraction. Statistical methods extract additional (missing) axioms for the compared entities with the goal of tackling the incompleteness of KBs and thus improving recall. Although this can be beneficial, it can also introduce noise (false positives or false negatives). Hence, our framework uses heuristics to assess whether knowledge extraction is likely to be advantageous and only activates the statistical components if this is the case. We instantiate our framework by combining lightweight logic-based reasoning implemented on top of existing triple-stores with an axiom extraction method that is based on the labels of concepts. Our work was motivated by industrial use cases over which we evaluate our instantiated framework, showing that it outperforms approaches that are only based on textual information. Besides the best combination of precision and recall, our implementation is also scalable and is currently used in an industrial production environment.


title: The Knowledge Graph Track at OAEI: Gold Standards, Baselines, and the Golden Hammer Bias

text: The Ontology Alignment Evaluation Initiative (OAEI) is an annual evaluation of ontology matching tools. In 2018, we have started the Knowledge Graph track, whose goal is to evaluate the simultaneous matching of entities and schemas of large-scale knowledge graphs. In this paper, we discuss the design of the track and two different strategies of gold standard creation. We analyze results and experiences obtained in first editions of the track, and, by revealing a hidden task, we show that all tools submitted to the track (and probably also to other tracks) suffer from a bias which we name the golden hammer bias.


title: MetaLink: A Travel Guide to the LOD Cloud

text: Graph-based traversal is an important navigation paradigm for the Semantic Web, where datasets are interlinked to provide context. While following links may result in the discovery of complementary data sources and enriched query results, it is widely recognized that traversing the LOD Cloud indiscriminately results in low quality answers. Over the years, approaches have been published that help to determine whether links are trustworthy or not, based on certain criteria. While such approaches are often useful for specific datasets and/or in specific applications, they are not yet widely used in practice or at the scale of the entire LOD Cloud. This paper introduces a new resource called MetaLink. MetaLink is a dataset that contains metadata for a very large set of owl:sameAs links that are crawled from the LOD Cloud. MetaLink encodes a previously published error metric for each of these links. MetaLink is published in combination with LOD-a-lot, a dataset that is based on a large crawl of a subset of the LOD Cloud. By combining MetaLink and LOD-a-lot, applications are able to make informed decisions about whether or not to follow specific links on the LOD Cloud. This paper describes our approach for creating the MetaLink dataset. It describes the vocabulary that it uses and provides an overview of multiple real-world use cases in which the MetaLink dataset can solve non-trivial research and application challenges that were not addressed before.


title: Investigating Software Usage in the Social Sciences: A Knowledge Graph Approach

text: Knowledge about the software used in scientific investigations is necessary for different reasons, including provenance of the results, measuring software impact to attribute developers, and bibliometric software citation analysis in general. Additionally, providing information about whether and how the software and the source code are available allows an assessment about the state and role of open source software in science in general. While such analyses can be done manually, large scale analyses require the application of automated methods of information extraction and linking. In this paper, we present SoftwareKG—a knowledge graph that contains information about software mentions from more than 51,000 scientific articles from the social sciences. A silver standard corpus, created by a distant and weak supervision approach, and a gold standard corpus, created by manual annotation, were used to train an LSTM based neural network to identify software mentions in scientific articles. The model achieves a recognition rate of .82 F-score in exact matches. As a result, we identified more than 133,000 software mentions. For entity disambiguation, we used the public domain knowledge base DBpedia. Furthermore, we linked the entities of the knowledge graph to other knowledge bases such as the Microsoft Academic Knowledge Graph, the Software Ontology, and Wikidata. Finally, we illustrate, how SoftwareKG can be used to assess the role of software in the social sciences.


title: SemTab 2019: Resources to Benchmark Tabular Data to Knowledge Graph Matching Systems

text: Tabular data to Knowledge Graph matching is the process of assigning semantic tags from knowledge graphs (e.g., Wikidata or DBpedia) to the elements of a table. This task is a challenging problem for various reasons, including the lack of metadata (e.g., table and column names), the noisiness, heterogeneity, incompleteness and ambiguity in the data. The results of this task provide significant insights about potentially highly valuable tabular data, as recent works have shown, enabling a new family of data analytics and data science applications. Despite significant amount of work on various flavors of this problem, there is a lack of a common framework to conduct a systematic evaluation of state-of-the-art systems. The creation of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) aims at filling this gap. In this paper, we report about the datasets, infrastructure and lessons learned from the first edition of the SemTab challenge.


title: GEval: A Modular and Extensible Evaluation Framework for Graph Embedding Techniques

text: While RDF data are graph shaped by nature, most traditional Machine Learning (ML) algorithms expect data in a vector form. To transform graph elements to vectors, several graph embedding approaches have been proposed. Comparing these approaches is interesting for 1) developers of new embedding techniques to verify in which cases their proposal outperforms the state-of-art and 2) consumers of these techniques in choosing the best approach according to the task(s) the vectors will be used for. The comparison could be delayed (and made difficult) by the choice of tasks, the design of the evaluation, the selection of models, parameters, and needed datasets. We propose GEval, an evaluation framework to simplify the evaluation and the comparison of graph embedding techniques. The covered tasks range from ML tasks (Classification, Regression, Clustering), semantic tasks (entity relatedness, document similarity) to semantic analogies. However, GEval is designed to be (easily) extensible. In this article, we will describe the design and development of the proposed framework by detailing its overall structure, the already implemented tasks, and how to extend it. In conclusion, to demonstrate its operating approach, we consider the parameter tuning of the KGloVe algorithm as a use case.


title: Fast and Exact Rule Mining with AMIE 3

text: Given a knowledge base (KB), rule mining finds rules such as “If two people are married, then they live (most likely) in the same place”. Due to the exponential search space, rule mining approaches still have difficulties to scale to today’s large KBs. In this paper, we present AMIE 3, a system that employs a number of sophisticated pruning strategies and optimizations. This allows the system to mine rules on large KBs in a matter of minutes. Most importantly, we do not have to resort to approximations or sampling, but are able to compute the exact confidence and support of each rule. Our experiments on DBpedia, YAGO, and Wikidata show that AMIE 3 beats the state of the art by a factor of more than 15 in terms of runtime.


title: On Modeling the Physical World as a Collection of Things: The W3C Thing Description Ontology

text: This document presents the Thing Description ontology, an axiomatization of the W3C Thing Description model. It also introduces an alignment with the Semantic Sensor Network ontology and evaluates how this alignment contributes to semantic interoperability in the Web of Things.


title: Fostering Scientific Meta-analyses with Knowledge Graphs: A Case-Study

text: A meta-analysis is a Science of Science method widely used in the medical and social sciences to review, aggregate and quantitatively synthesise a body of studies that address the same research question. With the volume of research growing exponentially every year, conducting meta-analyses can be costly and inefficient, as a significant amount of time and human efforts needs to be spent in finding studies meeting research criteria, annotating them, and properly performing the statistical analyses to summarise the findings. In this work, we show these issues can be tackled with semantic representations and technologies, using a social science scenario as case-study. We show how the domain-specific content of research outputs can be represented and used to facilitate their search, analysis and synthesis. We present the very first representation of the domain of human cooperation, and the application we built on top of this to help experts in performing meta-analyses semi-automatically. Using few application scenarios, we show how our approach supports the various phases meta-analyses, and more in general contributes towards research replication and automated hypotheses generation.


title: Incremental Multi-source Entity Resolution for Knowledge Graph Completion

text: We present and evaluate new methods for incremental entity resolution as needed for the completion of knowledge graphs integrating data from multiple sources. Compared to previous approaches we aim at reducing the dependency on the order in which new sources and entities are added. For this purpose, we consider sets of new entities for an optimized assignment of them to entity clusters. We also propose the use of a light-weight approach to repair entity clusters in order to correct wrong clusters. The new approaches are integrated within the FAMER framework for parallel and scalable entity clustering. A detailed evaluation of the new approaches for real-world workloads shows their high effectiveness. In particular, the repair approach outperforms other incremental approaches and achieves the same quality than with batch-like entity resolution showing that its results are independent from the order in which new entities are added.


title: Piveau: A Large-Scale Open Data Management Platform Based on Semantic Web Technologies

text: The publication and (re)utilization of Open Data is still facing multiple barriers on technical, organizational and legal levels. This includes limitations in interfaces, search capabilities, provision of quality information and the lack of definite standards and implementation guidelines. Many Semantic Web specifications and technologies are specifically designed to address the publication of data on the web. In addition, many official publication bodies encourage and foster the development of Open Data standards based on Semantic Web principles. However, no existing solution for managing Open Data takes full advantage of these possibilities and benefits. In this paper, we present our solution “Piveau”, a fully-fledged Open Data management solution, based on Semantic Web technologies. It harnesses a variety of standards, like RDF, DCAT, DQV, and SKOS, to overcome the barriers in Open Data publication. The solution puts a strong focus on assuring data quality and scalability. We give a detailed description of the underlying, highly scalable, service-oriented architecture, how we integrated the aforementioned standards, and used a triplestore as our primary database. We have evaluated our work in a comprehensive feature comparison to established solutions and through a practical application in a production environment, the European Data Portal. Our solution is available as Open Source. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this chapter (10.1007/978-3-030-49461-2_38) contains supplementary material, which is available to authorized users.


title: Astrea: Automatic Generation of SHACL Shapes from Ontologies

text: Knowledge Graphs (KGs) that publish RDF data modelled using ontologies in a wide range of domains have populated the Web. The SHACL language is a W3C recommendation that has been endowed to encode a set of either value or model data restrictions that aim at validating KG data, ensuring data quality. Developing shapes is a complex and time consuming task that is not feasible to achieve manually. This article presents two resources that aim at generating automatically SHACL shapes for a set of ontologies: (1) Astrea-KG, a KG that publishes a set of mappings that encode the equivalent conceptual restrictions among ontology constraint patterns and SHACL constraint patterns, and (2) Astrea, a tool that automatically generates SHACL shapes from a set of ontologies by executing the mappings from the Astrea-KG. These two resources are openly available at Zenodo, GitHub, and a web application. In contrast to other proposals, these resources cover a large number of SHACL restrictions producing both value and model data restrictions, whereas other proposals consider only a limited number of restrictions or focus only on value or model restrictions.


title: Entity Extraction from Wikipedia List Pages

text: When it comes to factual knowledge about a wide range of domains, Wikipedia is often the prime source of information on the web. DBpedia and YAGO, as large cross-domain knowledge graphs, encode a subset of that knowledge by creating an entity for each page in Wikipedia, and connecting them through edges. It is well known, however, that Wikipedia-based knowledge graphs are far from complete. Especially, as Wikipedia’s policies permit pages about subjects only if they have a certain popularity, such graphs tend to lack information about less well-known entities. Information about these entities is oftentimes available in the encyclopedia, but not represented as an individual page. In this paper, we present a two-phased approach for the extraction of entities from Wikipedia’s list pages, which have proven to serve as a valuable source of information. In the first phase, we build a large taxonomy from categories and list pages with DBpedia as a backbone. With distant supervision, we extract training data for the identification of new entities in list pages that we use in the second phase to train a classification model. With this approach we extract over 700k new entities and extend DBpedia with 7.5M new type statements and 3.8M new facts of high precision.


title: Estimating Characteristic Sets for RDF Dataset Profiles Based on Sampling

text: RDF dataset profiles provide a formal representation of a dataset’s characteristics (features). These profiles may cover various aspects of the data represented in the dataset as well as statistical descriptors of the data distribution. In this work, we focus on the characteristic sets profile feature summarizing the characteristic sets contained in an RDF graph. As this type of feature provides detailed information on both the structure and semantics of RDF graphs, they can be very beneficial in query optimization. However, in decentralized query processing, computing them is challenging as it is difficult and/or costly to access and process all datasets. To overcome this shortcoming, we propose the concept of a profile feature estimation. We present sampling methods and projection functions to generate estimations which aim to be as similar as possible to the original characteristic sets profile feature. In our evaluation, we investigate the feasibility of the proposed methods on four RDF graphs. Our results show that samples containing [Formula: see text] of the entities in the graph allow for good estimations and may be used by downstream tasks such as query plan optimization in decentralized querying.


title: Entity Linking and Lexico-Semantic Patterns for Ontology Learning

text: Ontology learning from a text written in natural language is a well-studied domain. However, the applicability of techniques for ontology learning from natural language texts is strongly dependent on the characteristics of the text corpus and the language used. In this paper, we present our work so far in entity linking and enhancing the ontology with extracted relations between concepts. We discuss the benefits of adequately designed lexico-semantic patterns in ontology learning. We propose a preliminary set of lexico-semantic patterns designed for the Czech language to learn new relations between concepts in the related domain ontology in a semi-supervised approach. We utilize data from the urban planning and development domain to evaluate the introduced technique. As a partial prototypical implementation of the stack, we present Annotace, a text annotation service that provides links between the ontology model and the textual documents in Czech.


title: Detecting Synonymous Properties by Shared Data-Driven Definitions

text: Knowledge graphs have become an essential source of entity-centric information for modern applications. Today’s KGs have reached a size of billions of RDF triples extracted from a variety of sources, including structured sources and text. While this definitely improves completeness, the inherent variety of sources leads to severe heterogeneity, negatively affecting data quality by introducing duplicate information. We present a novel technique for detecting synonymous properties in large knowledge graphs by mining interpretable definitions of properties using association rule mining. Relying on such shared definitions, our technique is able to mine even synonym rules that have only little support in the data. In particular, our extensive experiments on DBpedia and Wikidata show that our rule-based approach can outperform state-of-the-art knowledge graph embedding techniques, while offering good interpretability through shared logical rules.


title: Supporting Complex Decision Making by Semantic Technologies

text: Complex decisions require stakeholders to identify potential decision options and collaboratively select the optimal option. Identifying potential decision options and communicating them to stakeholders is a challenging task as it requires the translation of the decision option’s technical dimension to a stakeholder-compliant language which describes the impact of the decision (e.g., financial, political). Existing knowledge-driven decision support methods generate decision options by automatically processing available data and knowledge. Ontology-based methods emerged as a sub-field in the medical domain and provide concrete instructions for given medical problems. However, the research field lacks an evaluated practical approach to support the full cycle from data and knowledge assessment to the actual decision making. This work advances the field by: (i) a problem-driven ontology engineering method which (a) supports creating the necessary ontology model for the given problem domain and (b) harmonizes relevant data and knowledge sources for automatically identifying decision options by reasoners, and (ii) an approach which translates technical decision options into a language that is understood by relevant stakeholders. Expert evaluations and real-world deployments in three different domains demonstrate the added value of this method.


title: Handling Impossible Derivations During Stream Reasoning

text: With the rapid expansion of the Web and the advent of the Internet of Things, there is a growing need to design tools for intelligent analytics and decision making on streams of data. Logic-based frameworks like LARS allow the execution of complex reasoning on such streams, but it is paramount that the computation is completed in a timely manner before the stream expires. To reduce the runtime, we can extend the validity of inferred conclusions to the future to avoid repeated derivations, but this is not enough to avoid all sources of redundant computation. To further alleviate this problem, this paper introduces a new technique that infers the impossibility of certain derivations in the future and blocks the reasoner from performing computation that is doomed to fail anyway. An experimental analysis on microbenchmarks shows that our technique leads to a significant reduction of the reasoning runtime.


title: Building Linked Spatio-Temporal Data from Vectorized Historical Maps

text: Historical maps provide a rich source of information for researchers in the social and natural sciences. These maps contain detailed documentation of a wide variety of natural and human-made features and their changes over time, such as the changes in the transportation networks and the decline of wetlands. It can be labor-intensive for a scientist to analyze changes across space and time in such maps, even after they have been digitized and converted to a vector format. In this paper, we present an unsupervised approach that converts vector data of geographic features extracted from multiple historical maps into linked spatio-temporal data. The resulting graphs can be easily queried and visualized to understand the changes in specific regions over time. We evaluate our technique on railroad network data extracted from USGS historical topographic maps for several regions over multiple map sheets and demonstrate how the automatically constructed linked geospatial data enables effective querying of the changes over different time periods.


title: QAnswer KG: Designing a Portable Question Answering System over RDF Data

text: While RDF was designed to make data easily readable by machines, it does not make data easily usable by end-users. Question Answering (QA) over Knowledge Graphs (KGs) is seen as the technology which is able to bridge this gap. It aims to build systems which are capable of extracting the answer to a user’s natural language question from an RDF dataset. In recent years, many approaches were proposed which tackle the problem of QA over KGs. Despite such efforts, it is hard and cumbersome to create a Question Answering system on top of a new RDF dataset. The main open challenge remains portability, i.e., the possibility to apply a QA algorithm easily on new and previously untested RDF datasets. In this publication, we address the problem of portability by presenting an architecture for a portable QA system. We present a novel approach called QAnswer KG, which allows the construction of on-demand QA systems over new RDF datasets. Hence, our approach addresses non-expert users in QA domain. In this paper, we provide the details of QA system generation process. We show that it is possible to build a QA system over any RDF dataset while requiring minimal investments in terms of training. We run experiments using 3 different datasets. To the best of our knowledge, we are the first to design a process for non-expert users. We enable such users to efficiently create an on-demand, scalable, multilingual, QA system on top of any RDF dataset.


title: SchemaTree: Maximum-Likelihood Property Recommendation for Wikidata

text: Wikidata is a free and open knowledge base which can be read and edited by both humans and machines. It acts as a central storage for the structured data of several Wikimedia projects. To improve the process of manually inserting new facts, the Wikidata platform features an association rule-based tool to recommend additional suitable properties. In this work, we introduce a novel approach to provide such recommendations based on frequentist inference. We introduce a trie-based method that can efficiently learn and represent property set probabilities in RDF graphs. We extend the method by adding type information to improve recommendation precision and introduce backoff strategies which further increase the performance of the initial approach for entities with rare property combinations. We investigate how the captured structure can be employed for property recommendation, analogously to the Wikidata PropertySuggester. We evaluate our approach on the full Wikidata dataset and compare its performance to the state-of-the-art Wikidata PropertySuggester, outperforming it in all evaluated metrics. Notably we could reduce the average rank of the first relevant recommendation by 71%.


title: A Simple Method for Inducing Class Taxonomies in Knowledge Graphs

text: The rise of knowledge graphs as a medium for storing and organizing large amounts of data has spurred research interest in automated methods for reasoning with and extracting information from this representation of data. One area which seems to receive less attention is that of inducing a class taxonomy from such graphs. Ontologies, which provide the axiomatic foundation on which knowledge graphs are built, are often governed by a set of class subsumption axioms. These class subsumptions form a class taxonomy which hierarchically organizes the type classes present in the knowledge graph. Manually creating and curating these class taxonomies oftentimes requires expert knowledge and is time costly, especially in large-scale knowledge graphs. Thus, methods capable of inducing the class taxonomy from the knowledge graph data automatically are an appealing solution to the problem. In this paper, we propose a simple method for inducing class taxonomies from knowledge graphs that is scalable to large datasets. Our method borrows ideas from tag hierarchy induction methods, relying on class frequencies and co-occurrences, such that it requires no information outside the knowledge graph’s triple representation. We demonstrate the use of our method on three real-world datasets and compare our results with existing tag hierarchy induction methods. We show that our proposed method outperforms existing tag hierarchy induction methods, although both perform well when applied to knowledge graphs.


Playing with Creativity Across the Lifespan: a Conversation with Dr. Sandra Russ


title: Minimally invasive surfactant therapy versus InSurE in preterm neonates of 28 to 34 weeks with respiratory distress syndrome on non-invasive positive pressure ventilation—a randomized controlled trial

text: Preterm neonates with respiratory distress syndrome (RDS) are commonly treated with surfactant by intubate surfactant extubate (InSurE) technique. Mode of surfactant administration has evolved towards less invasive technique in the last few years. We randomised 58 preterm infants of 28–34 weeks of gestation with RDS within 6 h of birth to receive surfactant by InSurE or minimally invasive surfactant therapy (MIST). Non-invasive positive pressure ventilation (NIPPV) was used as primary respiratory support. The main objective was to compare the need of invasive mechanical ventilation (IMV) in first 72 h of life and secondarily hemodynamically significant patent ductus arteriosus (hsPDA), intraventricular haemorrhage (IVH) (> grade 2), bronchopulmonary dysplasia (BPD) and composite outcome of BPD/mortality. We did not find any difference in need of IMV in first 72 h between MIST and InSurE (relative risk with MIST, 0.62; 95% confidence interval, 0.22 to 1.32). No difference was observed in terms of hs PDA, IVH (> grade 2), BPD and composite outcome of BPD/mortality. Conclusion: There is no difference between MIST and InSurE in preterm neonates with RDS with NIPPV as a primary mode of respiratory support. Larger multicentre studies are needed to further explore differences in treatment failure and other secondary outcomes. Trial registration: www.ctri.nic.in id CTRI/2019/03/017992, registration date March 8, 2019.


Focus on better care and ethics: Are medical ethics lagging behind the development of new medical technologies?


title: Modernity, Catastrophe, and Realism in the Postcolonial Indian Novel

text: British colonialism in South Asia was marked by modernisation programmes of agriculture and industry. These programmes resulted in a long-term agrarian and food crisis which then led to a number of catastrophic events in the long twentieth century. The present book is about three such events from India’s late-colonial and postcolonial periods—the 1943–1944 Bengal famine, the 1967–1972 Naxalbari movement, and the 1975–1977 national emergency. It argues that these events are all linked with the historical crisis and yet distinct in their nature and orientation. The chapter contends that such a condition of relation through difference can be better understood via a reading of form and mode of the novels of these events. It shows that writers have predominantly used the realist form to represent the long-term historical crisis, while they have employed a range of modes—from social realist, critical realist to metafictional, urban fantastic, gothic, and others (many of which are conventionally understood as anti-realist or non-realist). These modes have shaped their realisms into a deeply heterogeneous and dynamic form, and, in so doing, composed the aesthetic framework of catastrophe-prone, crisis-ridden condition of life and living in postcolonial India, which is called here as “catastrophic realism.” The chapter situates the link between crisis and event by drawing upon the works of Fredric Jameson, Veena Das, Shahid Amin, and Louis Althusser. Its take on catastrophic realism is mediated through a thorough reading of realism, form, and mode in the European and postcolonial/Indian contexts, where Sa’adat Hasan Manto’s pre- and post-partition stories are offered as examples.


title: Using Historical Scientific Controversies to Promote Undergraduates’ Argumentation

text: The Covid-19 pandemic is the reason why humanity is paying more attention to the importance of regular and rigorous handwashing. Interestingly, in the nineteenth century, regular and rigorous handwashing was a key (and controversial) solution proposed by the Hungarian obstetrician Ignaz Philipp Semmelweis to cut drastically cases of puerperal fever. The purpose of this study was to provide evidence that the case of Semmelweis and puerperal fever—a crucial historical scientific controversy—can be used as a springboard to promote university student argumentation. Our study was inspired by the fact that the Organization for Economic and Cooperative Development (OECD) stressed that more efforts and resources should be invested in promoting argumentation as an essential component for scientifically literate citizens in twenty-first century societies. However, nowadays, argument and debate are virtually absent from university science education. The data was derived from 124 undergraduates’ (64 females and 60 males, 15–30 years old) written responses and audio and video recordings in a university biology course in Colombia. The findings show that the articulation of this historical controversy with decision-making, small-group debate, and whole-class debate activities can be useful for promoting undergraduates’ argumentation. This study contributes to the development of a research-based university science education that can inform the design of an argumentation curriculum for higher education.


title: Circulating fibrocytes traffic to the lung in murine acute lung injury and predict outcomes in human acute respiratory distress syndrome: a pilot study

text: BACKGROUND: Fibrosis is an integral component of the pathogenesis of acute lung injury and is associated with poor outcomes in patients with acute respiratory distress syndrome (ARDS). Fibrocytes are bone marrow-derived cells that traffic to injured tissues and contribute to fibrosis; hence their concentration in the peripheral blood has the potential to serve as a biomarker of lung fibrogenesis. We therefore sought to test the hypothesis that the concentration and phenotype of circulating fibrocytes in patients with ARDS predicts clinical outcomes. METHODS: For the animal studies, C57Bl/6 mice were infected with experimental Klebsiella pneumoniae in a model of acute lung injury; one-way ANOVA was used to compare multiple groups and two-way ANOVA was used to compare two groups over time. For the human study, 42 subjects with ARDS and 12 subjects with pneumonia (without ARDS) were compared to healthy controls. Chi-squared or Fisher’s exact test were used to compare binary outcomes. Survival data was expressed using a Kaplan-Meier curve and compared by log-rank test. Univariable and multivariable logistic regression were used to predict death. RESULTS: In mice with acute lung injury caused by Klebsiella pneumonia, there was a time-dependent increase in lung soluble collagen that correlated with sequential expansion of fibrocytes in the bone marrow, blood, and then lung compartments. Correspondingly, when compared via cross-sectional analysis, the initial concentration of blood fibrocytes was elevated in human subjects with ARDS or pneumonia as compared to healthy controls. In addition, fibrocytes from subjects with ARDS displayed an activated phenotype and on serial measurements, exhibited intermittent episodes of markedly elevated concentration over a median of 1 week. A peak concentration of circulating fibrocytes above a threshold of > 4.8 × 10(6) cells/mL cells correlated with mortality that was independent of age, ratio of arterial oxygen concentration to the fraction of inspired oxygen, and vasopressor requirement. CONCLUSIONS: Circulating fibrocytes increase in a murine model of acute lung injury and elevation in the number of these cells above a certain threshold is correlated with mortality in human ARDS. Therefore, these cells may provide a useful and easily measured biomarker to predict outcomes in these patients.


Firearms, pesticides, and suicide: A look back for a way forward


title: Onboarding: How Newcomers Integrate into an Agile Project Team

text: Although a stable team is deemed optimal for agile project success, new team members need to join ongoing agile projects. Newcomers must rapidly assimilate into the organisational and project environment while learning how to contribute effectively to the project and integrate into the team without seriously interrupting project progress. This paper addresses how newcomers integrate into an established agile project team and the challenges newcomers and the team face during this process. This paper is a single case study of a co-located agile project team in a large IT department who regularly onboard inexperienced newcomers. We found a mixture of traditional onboarding practices and specific agile practices contribute to the onboarding process. Onboarding challenges include empowerment and mindset change, accommodating part-timers, conveying agile principles, and adjusting to changes in team composition.


title: A Quantitative Exploration of the 9-Factor Theory: Distribution of Leadership Roles Between Scrum Master and Agile Team

text: A number of qualitative studies find that team leadership is one essential success factor for evolving into a mature agile team. One such qualitative study suggests the 9-Factor Theory of Scrum Master roles, which claims that the Scrum Master performs a set of 9 leadership roles which are transferred to the team over time [14]. We aimed at conducting a quantitative exploration that examines the presence and change of the 9-Factor Theory in relation to team maturity. We conducted an online survey with 67 individuals at the conglomerate Robert Bosch GmbH. Descriptive statistics reveal that the Scrum Master and the agile team score differently on the 9 factors and that the Scrum Master role is most often distributed in teams that had been working between 3 and 5 months in an agile manner. Yet, we also find that the leadership roles predominantly remain with one dedicated Scrum Master. Based on our results we suggest to group the 9-Factor Theory into three clusters: the Scrum Master is rather linked to psychological team factors (1), while the team tends to be linked to rather product-related factors (2). Organizational factors (3) are less often present. Our practical implications suggest an extension of the Scrum Master description. Furthermore, our study lays groundwork for future quantitative testing of leadership in agile teams.


title: On the Use of Design Thinking: A Survey of the Brazilian Agile Software Development Community

text: Design Thinking (DT) has been chosen as an approach to support problem-solving by many software development companies. However, there are divergences between the professionals of these companies concerning which techniques are performed, which steps are followed, and the way to implement this approach, as it proposes itself, to be divergent to generate numerous alternatives and, also, convergent, to find a solution. For this reason, aiming to characterize how the software companies have been implemented DT, this paper presents the results of a survey answered by 127 professionals from the Brazilian software industry. The results report a variety of scenarios in which DT has been applied: more than ten different models (sets of steps) are followed by the professionals; more than 50 techniques have been used, mainly, for meeting the needs in the process, according to the context of use and based on previous experiences. We also present 29 computational tools that, according to the respondents, assist the execution of DT, in addition to the integration with agile methods, allowing them to generate ideas and solutions, to explore and understand the problem.


title: “I Don’t Understand!”: Toward a Model to Evaluate the Role of User Story Quality

text: User stories are popular for conveying requirements in agile software projects. Despite existing quality criteria, authors make formal mistakes that result in “bad” user story quality. If developers have insufficient experience in balancing quality problems, the creation of a shared mental model is impossible, thus increasing the risk of impacts on the project’s success. This article provides a work-in-progress research model to set these variables in relation and establish a systematic method to uncover answers regarding their correlation. Details on the effects support research in agile requirements engineering to gain a better understanding of cognitive processes in the comprehension of user stories. In addition, insights can help to develop design recommendations and AI tools to improve user stories. A first evaluation of the model provides promising insights into the behavior and forms a basis for future research.


title: Large-Scale Agile Transformation: A Case Study of Transforming Business, Development and Operations

text: Today, product development organizations are adopting agile methods in units outside the software development unit, such as in sales, market, legal, operations working with the customer. This broader adoption of agile methods has been labeled large-scale agile transformation and is considered a particular type of organizational change, originating in the software development units. So far, there is little research-based advice on conducting such transformations. Aiming to contribute towards providing relevant research advice on large-scale agile transformation, we apply a research-based framework for evaluating organizational agility on a product development program in a maritime service provider organization. We found that doing a large-scale agile transformation involves many significant challenges, such as having a shared understanding of the problem, getting access to users, and getting commitment to change that needs to be done. In order to overcome such challenges, we discuss the need for a holistic and integrated approach to agile transformation involving all the units linked to software development.


title: Jeffersonian Directions in Curricular Renovation

text: In a contemporary update to Jefferson’s spirit of curricular innovation and experimentation, the final chapter contends that the best way for the public schools to meet the evolving needs of American youth would be for them to implement a Jeffersonian-inspired reconstruction of educational purpose. The author maintains that the schools should “re-found” their fundamental civic purposes to better align themselves with that which is most revolutionary about the American Revolution. To achieve these ends, the final section of the book offers a K-12 Curricular Redesign. It highlights the need to restore the humanities to their place of privilege within the curriculum. It does so by reorganizing the curriculum to meet the evolving needs of American youth: the need to recover their civic selfhood, the need to become ecologically literate, and the need to be able to distinguish between truth and falsity in the age dominated by social media.


title: Agile Implementation and Expansive Learning: Identifying Contradictions and Their Resolution Using an Activity Theory Perspective

text: A key challenge organisations face when transitioning to agile delivery methods is that of quickly and effectively learning new ways of working. This study posits that fundamental historical, cultural and behavioural aspects affect the transition and contribute to the poor performance of many agile implementations. In order to address such factors, this study applies a modified Activity Theory (AT) based framework to a case study agile implementation within a large public sector organisation. An activity is closely defined, and six generic activities associated with all agile implementations are identified. These are validated against the agile maturity model literature and a set of evaluation criteria of contradictions, congruences and collaboration is established. Evidence is gathered from participant interviews and the framework is used to surface learning and development obstacles and issues within an expansive learning cycle. The study argues that analysis via this modified AT framework brings original insight. Initial findings indicate that there are relatively few learning and development issues associated with the use of agile tools and techniques themselves and that most problems arise at the interface where the “changed” (more agile) delivery teams meet the organisation’s behavioural norms and practices.


title: Agile Software Development Practices and Success in Outsourced Projects: The Moderating Role of Requirements Risk

text: Although agile practices are gaining in popularity, there is little evidence showing how particular agile practices, in particular those involving the client, affect the success of outsourced software projects. Data from a matched survey of sponsors and developers in 60 outsourced information systems projects indicate negative effects of continuous analysis and positive effects of joint decision making and continuous integration on project success. Moreover, interaction analyses show that some positive effects are enhanced and negative effects dampened when requirements risk is high. These findings caution against continuous analysis in outsourced projects while they support joint decision making and continuous integration. The findings also empirically substantiate the largely untested assertion that agile practices help cope with changing requirements.


title: What an Agile Leader Does: The Group Dynamics Perspective

text: When large industrial organizations change to (or start with) an agile approach to operations, managers and some employees are supposed to be “agile leaders” often without being given a clear definition of what that comprises when building agile teams. An inductive thematic analysis was used to investigate what 15 appointed leaders actually do and perceive as challenges regarding group dynamics working with an agile approach. Team maturity, Team design, and Culture and mindset were all categories of challenges related to group dynamics that the practitioners face and manage in their work-life that are not explicitly mentioned in the more process-focused agile transformation frameworks. The results suggest that leader mitigation of these three aspects of group dynamics is essential to the success of an agile transformation.


title: “This is Just a Prototype”: How Ethics Are Ignored in Software Startup-Like Environments

text: Artificial Intelligence (AI) solutions are becoming increasingly common in software development endeavors, and consequently exert a growing societal influence as well. Due to their unique nature, AI based systems influence a wide range of stakeholders with or without their consent, and thus the development of these systems necessitates a higher degree of ethical consideration than is currently carried out in most cases. Various practical examples of AI failures have also highlighted this need. However, there is only limited research on methods and tools for implementing AI ethics in software development, and we currently have little knowledge of the state of practice. In this study, we explore the state of the art in startup-like environments where majority of the AI software today gets developed. Based on a multiple case study, we discuss the current state of practice and highlight issues. The cases underline the complete ignorance of ethical consideration in AI endeavors. We also outline existing good practices that can already support the implementation of AI ethics, such as documentation and error handling.


title: Improving Risk Management in a Scaled Agile Environment

text: Agile methods are designed for handling uncertainty as well as reducing risks in product development through transparency, inspection, and adaptation. Applying an effective risk management is in the nature of agile methods. However, when multiple agile teams work on the same product, a higher coordination effort is required and more formal practices are applied. The objective of this paper is to study how risk management can be improved in a scaled agile environment. Therefore, we conducted a case study in a large-sized ecommerce company and interviewed several project managers. The results show that there are differences for risk management in terms of two contexts. On the one hand, informal risk management is rated as good enough for one autonomous team. On the other hand, more formal approaches are needed, when several teams work on the same requirement. Furthermore, a tool for the support of risk management in a scaled agile environment is presented. We can conclude that hybrid development approaches consisting of agile practices and traditional practices, are beneficial, when several teams work in parallel.


title: “When in Rome, Do as the Romans Do”: Cultural Barriers to Being Agile in Distributed Teams

text: With the growing interest of adopting agile methods in offshored process, many companies realized that the use of agile methods and practices in companies located outside the location of early adopters of agile methods may be challenging. India, the main destination of offshoring contracts, have received particular attention, due to the big cultural differences. Critical analysis of related studies suggests that impeding behaviors are mostly rooted in the hierarchical culture of Indian organizations and related management behavior of command-and-control. But what happens in distributed projects with a more empowering onshore management? In this paper, we present the findings from a multiple-case study of DevOps teams with members from a mature agile company located in Sweden and a more hierarchical offshore vendor from India. Based on two focus groups we list culturally different behaviors of offshore engineers that were reported to impede agile ways of working. Furthermore, we report the findings from surveying 36 offshore team members from five DevOps teams regarding their likely behavior in situations reported to be problematic. Our findings confirm a number of previously reported behaviors rooted in cultural differences that impede the adoption of agile ways of working when collaborating with offshore engineers. At the same time, our survey results suggest that among the five surveyed teams there were teams that succeeded with the cultural integration of the offshore team members. Finally, our findings demonstrate the importance of cultural training especially when onboarding new team members.


title: Hypotheses Elicitation in Early-Stage Software Startups Based on Cognitive Mapping

text: Software startups develop innovative products for which there are typically no customers to refer to elicit requirements. Often, these companies develop a set of features without a better understanding of customer needs. An experiment-based approach to validate hypotheses about the customer and market could increase their chance of success or, at least, accelerate their realization of the product worthlessness. The first step of an experiment-based approach is to elicit hypotheses to guide experiments. Software startups base their products on business assumptions, but there is a lack of understanding of how these assumptions are formed and how teams could elicit hypotheses systematically. To fill this gap, we performed an empirical study consisted of two steps. First, we explored based on which assumptions startups define their products using a multiple case study. The results indicate that these companies developed their products based on founders’ assumptions derived from their previous experience. Second, we investigated cognitive mapping as a tool to elicit hypotheses systematically with two software startups. The results indicate that this approach can serve as the basis of a method to elicit hypotheses in early-stage software startups.


title: Combining User-Centered Design and Lean Startup with Agile Software Development: A Case Study of Two Agile Teams

text: The combined use of User-Centered Design and Lean Startup with Agile Development has been pointed out by the literature as a manner to boost software development. User-Centered Design principles focus on providing tools for developers to better explore user needs and seek for a fitter solution. Lean Startup, on the other hand, supplements the triad combination by bringing the Build-Measure-Learn cycle and the concept of pivoting, either the problem understanding or the proposed solution. This paper reports on a case study of two software teams that have been undergoing the changes and impacts of such combined adoption. We investigated these teams for six months, from the moment that team members were trained on the job to grasp the essence of using the integrated approach inspired on Pivotal Labs proposal to the time they were considered mature enough to share their experiences with others within the organization. Through our in-depth study, we illustrate how this adoption promotes changes regarding to mindset, activities, practices, and techniques. We also report on the ‘team rhythm’ (or work flow) as experienced by the two teams. The paper contributes to current knowledge on the topic reporting on the changes and impacts that teams observed during the combined approach adoption.


title: Examining the Current State of System Testing Methodologies in Quality Assurance

text: Testing is an important phase of every software system, as it can reveal defects early and contribute to achieving high software quality. In this process of quality assurance, organizations are usually relying on one testing technique. However, a combination of techniques may prove more beneficial to the organization, as it might give the chance to discover a larger number of defects early. In order to examine the above, in the current work we present a survey on the use of system testing methodologies. We have gathered data from 252 individuals that reveal current trends in testing, such as whether requirements are used in the test case definition and whether the testing techniques used are affected by parameters, such as years of experience, whereas we examine the combination of smoke testing and regression testing. We also demonstrate an industrial use case, where this combination was applied, reducing the number of defects identified by the customer.


title: Characterising the Quality of Behaviour Driven Development Specifications

text: Behaviour Driven Development (BDD) is an agile testing technique that enables software requirements to be specified as example interactions with the system, using structured natural language. While (in theory) being readable by non-technical stakeholders, the examples can also be executed against the code base to identify behaviours that are not yet correctly implemented. Writing good BDD suites, however, is challenging. A typical suite can contain hundreds of individual scenarios, that must correctly specify the system as a whole as well as individually. Despite much discussion amongst practitioners and in the blogosphere, as yet no formal definition of what makes for a high quality BDD suite has been given. To shed light on this, we surveyed BDD practitioners, asking for their opinions on the quality criteria that are important for BDD suites. We proposed, and asked for opinions on, four quality principles, and gave practitioners the option to add more principles of their own. This paper reports on the results of the survey, and presents an approach to defining BDD suite quality.


title: Results from a Replicated Experiment on the Affective Reactions of Novice Developers When Applying Test-Driven Development

text: Test-Driven Development (TDD) is an incremental approach to software development. Despite it is claimed to improve both quality of software and developers’ productivity, the research on the claimed effects of TDD has so far shown inconclusive results. Some researchers have ascribed these inconclusive results to the negative affective states that TDD would provoke. A previous (baseline) experiment has, therefore, studied the affective reactions of (novice) developers—i.e., 29 third-year undergraduates in Computer Science (CS)—when practicing TDD to implement software. To validate the results of the baseline experiment, we conducted a replicated experiment that studies the affective reactions of novice developers when applying TDD to develop software. Developers in the treatment group carried out a development task using TDD, while those in the control group used a non-TDD approach. To measure the affective reactions of developers, we used the Self-Assessment Manikin instrument complemented with a liking dimension. The most important differences between the baseline and replicated experiments are: (i) the kind of novice developers involved in the experiments—third-year vs. second-year undergraduates in CS from two different universities; and (ii) their number—29 vs. 59. The results of the replicated experiment do not show any difference in the affective reactions of novice developers. Instead, the results of the baseline experiment suggest that developers seem to like TDD less as compared to a non-TDD approach and that developers following TDD seem to like implementing code less than the other developers, while testing code seems to make them less happy.


Agrariër Vraagt Niet Snel Om Hulp


Wacht Een Sociale Crisis?


title: Deep Learning-Based Computer Vision Application with Multiple Built-In Data Science-Oriented Capabilities

text: This paper presents a Data Science-oriented application for image classification tasks that is able to automatically: a) gather images needed for training Deep Learning (DL) models with a built-in search engine crawler; b) remove duplicate images; c) sort images using built-in pre-trained DL models or user’s own trained DL model; d) apply data augmentation; e) train a DL classification model; f) evaluate the performance of a DL model and system by using an accuracy calculator as well as the Accuracy Per Consumption (APC), Accuracy Per Energy Cost (APEC), Time to closest APC (TTCAPC) and Time to closest APEC (TTCAPEC) metrics calculators. Experimental results show that the proposed Computer Vision application has several unique features and advantages, proving to be efficient regarding execution time and much easier to use when compared to similar applications.


“Migration and Sovereign Default Risk” A Comment


Commentary: Burning Bright without Burning Out – Protecting the Spirit of Cardiothoracic Surgery


Could lung ultrasound be used instead of auscultation?


Current biology special issue: AGRI2019()()


National Academies of Practice Call for Nominations


Oral healthcare during the COVID-19 pandemic


title: Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming

text: In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings in order to improve hiring and placement decisions. The proposed framework follows two main phases: a local prediction scheme for recruitments' success at the level of a single job placement, and a mathematical model that provides a global recruitment optimization scheme for the organization, taking into account multilevel considerations. In the first phase, a key property of the proposed prediction approach is the interpretability of the machine learning (ML) model, which in this case is obtained by applying the Variable-Order Bayesian Network (VOBN) model to the recruitment data. Specifically, we used a uniquely large dataset that contains recruitment records of hundreds of thousands of employees over a decade and represents a wide range of heterogeneous populations. Our analysis shows that the VOBN model can provide both high accuracy and interpretability insights to HR professionals. Moreover, we show that using the interpretable VOBN can lead to unexpected and sometimes counter-intuitive insights that might otherwise be overlooked by recruiters who rely on conventional methods. We demonstrate that it is feasible to predict the successful placement of a candidate in a specific position at a pre-hire stage and utilize predictions to devise a global optimization model. Our results show that in comparison to actual recruitment decisions, the devised framework is capable of providing a balanced recruitment plan while improving both diversity and recruitment success rates, despite the inherent trade-off between the two.


Commentary: Collaboration is Key to Saving as Many Lives as Possible


title: SpaceX's first crewed flight is a go

text: Social distancing measures will allow the test mission to launch as planned


The Virus


Commentary: Lessons learned during the coronavirus disease 2019 pandemic can make us better at something


Review of ‘knowledge resistance. How avoid insight from others’ by Mikael Klintman, Manchester University Press, 2019, 256 pages, Manchester, retail price 12.99£


Gita Ramjee


Opportunities for education during the COVID-19 pandemic


DIARRHEAL (DIARRHOEAL) DISEASES


Dysimmune neuropathies


title: The Structure of Bacteria and Molecular Biology of Viruses

text: The design of prokaryotes has been a tremendously successful feature of their existence. Even today, bacteria and viruses remain one of the most successful life forms and are among the most abundant. Unlike some eukaryotic cells, bacteria are not able to engulf particulate foodstuffs. Instead, they must rely entirely on diffusion. Bacteria come in many shapes and forms; they can be spherical, rod-like, spiral, filamentous, comma-like, square, or ring-like. One of the primary functions of these various designs is to increase the surface area to volume relationship; the greater the surface area, the greater the likelihood of encountering a diffusing substrate. Receptor proteins, transport proteins, and specialized pores must also contribute to ingestion, but it is the overall cellular design that provides a fundamental advantage. The structure of viruses reflects their association with the extracellular and intracellular environment. In its extracellular environment, viruses are in the form of mature virions that are capable of protecting their sensitive genomes and transmitting their genetic information to new susceptible cells. In its intracellular environment, the protective coat is no longer needed and the virus structure changes. The mature virus structure allows survival in the extracellular environment and specific association with cells.


FUNCTIONS AND ALTERATIONS OF CELL MEMBRANES DURING ACTIVE VIRUS INFECTION


The Respiratory System


Vaccine safety


title: Antimicrobial De-Escalation in the ICU: From Recommendations to Level of Evidence

text: Antimicrobial de-escalation (ADE) is a component of antimicrobial stewardship (AMS) aimed to reduce exposure to broad-spectrum antimicrobials. In the intensive care unit, ADE is a strong recommendation that is moderately applied in clinical practice. Following a systematic review of the literature, we assessed the studies identified on the topic which included one randomized controlled trial and 20 observational studies. The literature shows a low level of evidence, although observational studies suggested that this procedure is safe. The effects of ADE on the level of resistance of ecological systems and especially on the microbiota are unclear. The reviewers recommend de-escalating antimicrobial treatment in patients requiring long-term antibiotic therapy and considering de-escalation in short-term treatments. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12325-020-01390-2) contains supplementary material, which is available to authorized users.


title: Evaluation of the recombinant proteins RlpB and VacJ as a vaccine for protection against Glaesserella parasuis in pigs

text: BACKGROUND: Glaesserella parasuis, the causative agent of Glӓsser’s disease, is widespread in swine globally resulting in significant economic losses to the swine industry. Prevention of Glӓsser’s disease in pigs has been plagued with an inability to design broadly protective vaccines, as many bacterin based platforms generate serovar or strain specific immunity. Subunit vaccines are of interest to provide protective immunity to multiple strains of G. parasuis. Selected proteins for subunit vaccination should be widespread, highly conserved, and surface exposed. RESULTS: Two candidate proteins for subunit vaccination (RlpB and VacJ) against G. parasuis were identified using random mutagenesis and an in vitro organ culture system. Pigs were vaccinated with recombinant RlpB and VacJ, outer membrane proteins with important contributions to cellular function and viability. Though high antibody titers to the recombinant proteins and increased interferon-γ producing cells were found in subunit vaccinated animals, the pigs were not protected from developing systemic disease. CONCLUSIONS: It appears there may be insufficient RlpB and VacJ exposed on the bacterial surface for antibody to bind, preventing high RlpB and VacJ specific antibody titers from protecting animals from G. parasuis. Additionally, this work confirms the importance of utilizing the natural host species when assessing the efficacy of vaccine candidates.


title: Contusion Progression Following Traumatic Brain Injury: A Review of Clinical and Radiological Predictors, and Influence on Outcome

text: Secondary injuries remain an important cause of the morbidity and mortality associated with traumatic brain injury (TBI). Progression of cerebral contusions occurs in up to 75% of patients with TBI, and this contributes to subsequent clinical deterioration and requirement for surgical intervention. Despite this, the role of early clinical and radiological factors in predicting contusion progression remains relatively poorly defined due to studies investigating progression of all types of hemorrhagic injuries as a combined cohort. In this review, we summarize data from recent studies on factors which predict contusion progression, and the effect of contusion progression on clinical outcomes.


title: Hospitalist and Intensivist Experiences of the “Open” Intensive Care Unit Environment: a Qualitative Exploration

text: BACKGROUND: Most U.S. academic medical centers employ “closed” intensive care units (ICUs), where critically ill patients are admitted under the supervision of intensivists managing dedicated ICU teams. Some centers utilize a unique “open” ICU structure, where primary services longitudinally follow patients who become critically ill into the ICU with intensivist comanagement. The impact of open ICUs on patient care and education of trainees has not been well-characterized. OBJECTIVE: The objective of this study is to characterize affordances and barriers to education and patient care, from the perspectives of hospitalists and intensivists teaching in the ICU. DESIGN: We conducted semi-structured interviews with hospitalist and intensivist faculty at a large academic medical center with an open ICU structure. We coded deidentified interview transcripts to inductively analyze the data for themes and subthemes. PARTICIPANTS: We recruited hospitalist and intensivist faculty members who attend on teaching services in the open ICU system. APPROACH: Given the complexity of multiple teachers and learners in the ICU environment, we selected shared mental models as our primary theoretical lens through which we analyzed and interpreted our data. KEY RESULTS: We identified three main themes regarding education in the open ICU system: (1) communication challenges, (2) educational barriers and affordances, and (3) structural barriers and affordances. Hospitalists and intensivists agreed on some barriers and facilitators to education, such as continuity of care, yet they disagreed on others. Specifically, hospitalists and intensivists had a shared mental model regarding barriers to patient care and education in the open ICU structure, but had divergent opinions regarding the affordances of the structure, such as continuity and availability of ICU expertise. CONCLUSIONS: The open ICU environment presents facilitators and barriers to trainee education and patient care. Our findings can be leveraged to improve communication, education, and patient care on both hospitalist and ICU teams. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11606-020-05835-w) contains supplementary material, which is available to authorized users.


Flexner’s Words


title: ‘Incantatory’ governance: global climate politics’ performative turn and its wider significance for global politics

text: The 2015 Paris agreement represents a deep-rooted change in global climate governance. While existing scholarly assessments highlight central institutional features of the Paris shift, they tend to overlook its symbolic and discursive dimensions. Our analysis shows that the Paris architecture combines two core elements: an iterative pledge and review process to stimulate global climate action, and a ‘performative’ narrative aimed at aligning actors’ expectations on the prospect of a low-carbon future. We therefore suggest calling it an incantatory system of governance. We then examine the origins of the new approach and find that the rise of ‘soft law’ approaches and communicative techniques in global climate governance are both indicative of a broader process: the entry of management culture in international organisations. Against this backdrop, we examine the prospects, limitations and caveats of the new approach and discuss its wider implications for global politics.