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Core decomposition is a classic technique for discovering densely connected regions in a graph with large range of applications. Formally, a $k$-core is a maximal subgraph where each vertex has at least $k$ neighbors. A natural extension of a $k$-core is a $(k, h)$-core, where each node must have at least $k$ nodes that can be reached with a path of length $h$. The downside in using $(k, h)$-core decomposition is the significant increase in the computational complexity: whereas the standard core decomposition can be done in $O(m)$ time, the generalization can require $O(n^2m)$ time, where $n$ and $m$ are the number of nodes and edges in the given graph. In this paper we propose a randomized algorithm that produces an $\epsilon$-approximation of $(k, h)$ core decomposition with a probability of $1 - \delta$ in $O(\epsilon^{-2} hm (\log^2 n - \log \delta))$ time. The approximation is based on sampling the neighborhoods of nodes, and we use Chernoff bound to prove the approximation guarantee. We also study distance-generalized dense subgraphs, show that the problem is NP-hard, provide an algorithm for discovering such graphs with approximate core decompositions, and provide theoretical guarantees for the quality of the discovered subgraphs. We demonstrate empirically that approximating the decomposition complements the exact computation: computing the approximation is significantly faster than computing the exact solution for the networks where computing the exact solution is slow, Comment: Extended version

The suffix array, perhaps the most important data structure in modern string processing, is often augmented with the longest common prefix (LCP) array which stores the lengths of the LCPs for lexicographically adjacent suffixes of a string. Together the two arrays are roughly equivalent to the suffix tree with the LCP array representing the tree shape. In order to better understand the combinatorics of LCP arrays, we consider the problem of inferring a string from an LCP array, i.e., determining whether a given array of integers is a valid LCP array, and if it is, reconstructing some string or all strings with that LCP array. There are recent studies of inferring a string from a suffix tree shape but using significantly more information (in the form of suffix links) than is available in the LCP array. We provide two main results. (1) We describe two algorithms for inferring strings from an LCP array when we allow a generalized form of LCP array defined for a multiset of cyclic strings: a linear time algorithm for binary alphabet and a general algorithm with polynomial time complexity for a constant alphabet size. (2) We prove that determining whether a given integer array is a valid LCP array is NP-complete when we require more restricted forms of LCP array defined for a single cyclic or non-cyclic string or a multiset of non-cyclic strings. The result holds whether or not the alphabet is restricted to be binary. In combination, the two results show that the generalized form of LCP array for a multiset of cyclic strings is fundamentally different from the other more restricted forms.

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We study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs. In the context of this system, we explore affirmative action policies that seek to narrow the gap between the admission rates of different socio-demographic groups while still accepting students with high scores. Since there is uncertainty about the score distribution of the students who will apply to each program, it is unclear what policy would have the desired effect on the admission rates of different groups. We address this challenge by using a predictive model trained on historical data to help optimize the parameters of such policies. We find that a learned predictive model does significantly better than relying on the ideal parameters for the last year. At the same time, we also find that a large pool of historical data yields similar results as our predictive approach for our data. Due to the more complex nature of the predictive approach, we conclude that a simpler approach should be preferred if enough data is available (e.g., long-standing, traditional university programs), but not for newer programs and other cases in which our predictive strategy can prove helpful.

Gamification has been widely used to design better educational systems aiming to increase students’ concentration, motivation, engagement, flow experience, and others positive experiences. With advances in research on gamification in education, over the past few years, many studies have highlighted the need to tailor the gamification design properties to match individual students’ needs, characteristics and preferences. Thus, different studies have been conducted to personalize the gamification in education. However, the results are still contradictory and need to be better understood to advance this field. To provide a complete understanding of this research domain, we conducted a systematic literature review to summarize the results and discussions on studies that cover the field of tailored gamified education. Following a systematic process, we analysed 2108 studies and identified 19 studies to answer our research questions. The results indicate that most of the studies only consider students’ gamer types to tailor the systems, and most of the experiments do not provide sufficient statistical evidence, especially regarding learning performance using tailored gamified systems. Based on the results, we also provided an agenda with different challenges, opportunities, and research directions to improve the literature on tailored gamification in education. Our study contributes to the field of gamification design in education.

The digital game industry has actively integrated data-driven methods into its core processes. This interview-based study shows how game industry professionals perceive the role of data as part of their everyday work. Analysing the data-related notions and negotiations helps to explicate how mainstream data imaginaries are both reproduced and challenged in the different phases and contexts of game making. The analysis is divided into the following themes: data is everywhere, data is messy, data is constructed and data redefines creativity. The qualitative inquiry shows how the meaning of game data cannot be reduced to individual metrics or analytics services, or new positions like data analysts. Data-driven development is based on particular values and assumptions, and it creates new practices, working cultures and conflicting forms of agency. publishedVersion

Higher education institutions may adopt various approaches to the pedagogic principles and methods used in teaching sustainable development in business and marketing courses. These methods can include the utilisation of digital technologies and online communication to facilitate distance learning and fast access to relevant information. Changes towards the digitalisation of the learning environment especially gained popularity during the Covid-19 pandemic. In the post-pandemic period, digitalisation continues to facilitate the learning and teaching processes. However, the implementation of digital technologies, besides technological expertise, requires appropriate theoretical frameworks for understanding how learning is developed. This study explores connectivism theory applied to the pedagogic practices of knowledge dissemination concerning sustainable development in the fields of business and marketing. Connectivism embraces knowledge as a network where the learner, with the help of digital technologies, develops mental connections between pieces of information during interaction with various information sources. This qualitative research empirically explores the principles of connectivism embedded in the learning and teaching of a university course conducted online. The research findings indicate that connectivism may be a suitable conceptual framework that motivates learners to develop knowledge through digital enablers, discussions and social networking and to make connections to sustainability concepts. The principles of connectivism may help instructors to develop a learning environment where learners add understandings to their previous knowledge on sustainability through online interactions and by accessing digital knowledge sources. This study makes several interdisciplinary contributions by deepening the insights into digital pedagogic methods and approaches for the facilitation of learning, which may be of interest to academic and other pedagogic practitioners.

We present the Eco-ISEA3H database, a compilation of global spatial data characterizing climate, geology, land cover, physical and human geography, and the geographic ranges of nearly 900 large mammalian species. The data are tailored for machine learning (ML)-based ecological modeling, and are intended primarily for continental- to global-scale ecometric and species distribution modeling. Such models are trained on present-day data and applied to the geologic past, or to future scenarios of climatic and environmental change. Model training requires integrated global datasets, describing species’ occurrence and environment via consistent observational units. The Eco-ISEA3H database incorporates data from 17 sources, and includes 3,033 variables. The database is built on the Icosahedral Snyder Equal Area (ISEA) aperture 3 hexagonal (3H) discrete global grid system (DGGS), which partitions the Earth’s surface into equal-area hexagonal cells. Source data were incorporated at six nested ISEA3H resolutions, using scripts developed and made available here. We demonstrate the utility of the database in a case study analyzing the bioclimatic envelopes of ten large, widely distributed mammalian species.

Educators are continuously exploring ways to enhance the academic potential of students while fostering a positive social atmosphere within classrooms. To meet these various curricular and interpersonal objectives, teachers are increasingly utilising educational storification in order to engage students and positively support their social relationships. However, research still lacks in terms of how storification impacts students’ social behaviour and communities. With grounded theory methods, and data from a 10-day ethnographic fieldwork, participatory observations, interviews with 11 educational staff and focus groups with 79 students at a middle school employing a Harry Potter theme, this study indicates that storification can strengthen the school community and hinder students’ antisocial behaviour. The storified learning environment formed a shared interest at the school, which facilitated further friendship formations and sense of belonging, however, careful considerations on social cliques and certain norms the selected story potentially delivers are called for. publishedVersion

Publisher Copyright: © 2013 IEEE. Crowdsourcing is a general term that describes the practice of many individuals working collectively to achieve a common goal or complete a task, often involving the generation of content. In an educational context, crowdsourcing of learning materials-where students create resources that can be used by other learners-offers several benefits. Students benefit from the act of producing resources as well as from using the resources. Despite benefits, instructors may be hesitant to adopt crowdsourcing for several reasons, such as concerns around the quality of content produced by students and the perceptions students may have of creating resources for their peers. While prior work has explored crowdsourcing concerns within the context of individual tools, lessons that are generalisable across multiple platforms and derived from practical use can provide considerably more robust insights. In this perspective article, we present four crowdsourcing tools that we have developed and used in computing classrooms. From our previous studies and experience, we derive lessons which shed new light on some of the concerns that are typical for instructors looking to adopt such tools. We find that across multiple contexts, students are capable of generating high quality learning content which provides good coverage of key concepts. Although students do appear hesitant to engage with new kinds of activities, various types of incentives have proven effective. Finally, although studies on learning effects have shown mixed results, no negative outcomes have been observed. In light of these lessons, we hope to see a greater uptake and use of crowdsourcing in computing education.

2020 marks the beginning of a new era as the pandemic catapulted us into new digital and virtual ways of everyday life. As the world changes, we reimagine empowering, equitable, accessible, diverse, and inclusive digital futures, through a series of projects and workshops with a diverse set of participants - children in schools and Child Computer Interaction researchers. We conducted one long-term project with two schools in Finland and two one-day workshops with an international set of participants. Through an analysis of participants’ experiences and outcomes in the project and workshops, we build a case for diversity and inclusion through design fiction in the context of children’s education. In addition, through an analysis of the process we as researchers took for developing the project and workshops, we showcase the support of diversity and inclusion in design fiction. publishedVersion

Pharmacy is a multidisciplinary research field that combines natural sciences, health sciences and social sciences to study drugs and pharmaceutical preparations from multiple perspectives. The study explores publishing patterns in pharmacy via bibliometric methods, that is statistical methods applied to study scientific literature. Earlier bibliometric studies focusing on pharmacy have used data from the international citation databases Web of Science and Scopus. In most of these studies, pharmacy has been operationalized by focusing on journals categorized as pharmacy journals. This study provides a new approach to the study of publishing patterns, by using data from institutional Current Research Information Systems (CRIS), and by using pharmacy organizations as the basis of operationalization of pharmacy. It seeks to provide a more comprehensive picture of publishing patterns, since the data covers all publication types used in pharmacy and is not limited to pharmacy journals. The objective of this study is thus to explore whether the selection of databases and operationalization of the discipline affects the results concerning publishing patterns in pharmacy. The results obtained in this study are very similar to earlier studies utilizing international databases. However, the results show that pharmacy researchers also publish in national languages, and that there are several national journals amongst the core journals that are not covered by the international databases. The multidisciplinary nature of pharmacy can be seen in the wide range of journals in which pharmacy researchers publish their articles. publishedVersion

COVID-19 pandemic has affected the entire world in many ways. It has sparked a prominent pedagogical shift for university level students, as it has changed the way students learn, attend classes, or communicate with teachers. Globally, every student is forced to adopt Emergency Remote Learning (ERL) as a result of immediate transformation of physical classes into remote education. This two-fold study investigated the differences between traditional distance, online, and virtual learning solutions and the new Emergency Remote Learning (ERL) method for the university level education. Furthermore, a pragmatic mix-method study is conducted in the form of surveys, semi-structured interviews, and diary study spanning across 10 months of pandemic, to examine self-reported insights on ERL challenges, experiences, and learning engagement of the students from Finland and India. Cumulative findings suggest that scheduling, distractions, pessimistic emotions, longer durations, and concentration were the highest challenges faced by the students which impacted their learning experiences and engagement. The study also found that the ERL specific factors like low-interactivity, technical limitations, non-structured, and non-standardized methods had a prominent impact on the effectiveness of remote education. Furthermore, the study has suggested guidelines for improving remote learning experience as a futuristic solution beyond COVID-19 pandemic. Supplementary Information The online version contains supplementary material available at 10.1007/s10639-021-10747-1.

Data sharing is one of the cornerstones of modern science that enables large-scale analyses and reproducibility. We evaluated data availability in research articles across nine disciplines in Nature and Science magazines and recorded corresponding authors’ concerns, requests and reasons for declining data sharing. Although data sharing has improved in the last decade and particularly in recent years, data availability and willingness to share data still differ greatly among disciplines. We observed that statements of data availability upon (reasonable) request are inefficient and should not be allowed by journals. To improve data sharing at the time of manuscript acceptance, researchers should be better motivated to release their data with real benefits such as recognition, or bonus points in grant and job applications. We recommend that data management costs should be covered by funding agencies; publicly available research data ought to be included in the evaluation of applications; and surveillance of data sharing should be enforced by both academic publishers and funders. These cross-discipline survey data are available from the plutoF repository.

The study of computational models for argumentation is a vibrant area of artificial intelligence and, in particular, knowledge representation and reasoning research. Arguments most often have an intrinsic structure made explicit through derivations from more basic structures. Computational models for structured argumentation enable making the internal structure of arguments explicit. Assumption-based argumentation (ABA) is a central structured formalism for argumentation in AI. In this article, we make both algorithmic and complexity-theoretic advances in the study of ABA. In terms of algorithms, we propose a new approach to reasoning in a commonly studied fragment of ABA (namely the logic programming fragment) with and without preferences. While previous approaches to reasoning over ABA frameworks apply either specialized algorithms or translate ABA reasoning to reasoning over abstract argumentation frameworks, we develop a direct declarative approach to ABA reasoning by encoding ABA reasoning tasks in answer set programming. We show via an extensive empirical evaluation that our approach significantly improves on the empirical performance of current ABA reasoning systems. In terms of computational complexity, while the complexity of reasoning over ABA frameworks is well-understood, the complexity of reasoning in the ABA+ formalism integrating preferences into ABA is currently not fully established. Towards bridging this gap, our results suggest that the integration of preferential information into ABA via so-called reverse attacks results in increased problem complexity for several central argumentation semantics.

In a novel experimental setting, we augmented a variation of traditional compassion meditation with our custom-built VR environment for multiple concurrent users. The presence of another user’s avatar in shared virtual space supports social interactions and provides an active target for evoked compassion. The system incorporates respiration and brainwave-based biofeedback to enable closed-loop interaction of users based on their shared physiological state. Specifically, we enhanced interoception and the deep empathetic processes involved in compassion meditation with real-time visualizations of: breathing rate, level of approach motivation assessed from EEG frontal asymmetry, and dyadic synchrony of those signals between two users. We manipulated these interventions across eight separate conditions (dyadic or solo meditation; brainwave, breathing, both or no biofeedback) in an experiment with 39 dyads (N=8), observing the effect of conditions on self-reported experience and physiological synchrony. We found that each different shared biofeedback type increased users’ self-reported empathy and social presence, compared to no-biofeedback or solo conditions. Our study illustrates how dyadic synchrony biofeedback can expand the possibilities of biofeedback in affective computing and VR solutions for health and wellness.

This article discusses the prospects and challenges of combining multimodality theory with distant viewing, a recent framework proposed in the field of digital humanities. This framework advocates the use of computational methods to enable large-scale analysis of visual and multimodal materials, which must be nevertheless supported by theories that explain how these materials are structured. Multimodality theory is well-positioned to support this effort by providing descriptive schemas that impose structure on the materials under analysis. The field of multimodality research can also benefit from adopting computational methods, which help to achieve the long-term goal of building large multimodal corpora for empirical research. However, despite their immense potential for multimodality research, the use of computational methods warrants caution, because they involve a number of potentially cascading risks that arise from biases inherent to the underlying data and different approaches to the phenomenon of multimodality.

Publisher Copyright: © 2021 Estonian Association Applied Linguists. All rights reserved. The study focuses on the sharing of linguistic data containing personal data, which is the processing of personal data. Therefore, the requirements of the General Data Protection Regulation (GDPR) must be complied with. Compliance with these requirements is the responsibility of the controller, who may use the assistance of processors to process the data. In international practice, it is not clear how the responsibility for the processing of personal data is divided between a specific researcher and a research institution. For example, the French and German models differ from the Estonian, Lithuanian and Finnish models. The Greek model offers a unique approach. In general, the employer (organization or legal entity) is responsible for the processing of personal data. At the same time, research has its own specificity, which is characterized by academic freedom and as well as mobility of researchers. The situation is further complicated by the sharing of language data through research networks such as CLARIN. In the present study, the authors analyze the division of duties and responsibilities between the researcher and the research institution (including research networks). The authors also analyze how data sharing should take place from a personal data protection perspective. It is important to clarify whether the data provider and the data recipient are both controllers, joint controllers or the recipient is the processor and how the responsibility for data sharing is shared. The authors of the analysis have an interdisciplinary and international background, covering different areas of law (data protection, labor law, contract law) and jurisdictions (Estonia, Italy, Greece, Lithuania, France, Germany, Finland) and language technology.

Purpose The purpose of this paper is to investigate the long-term development of online research skills among lower secondary school students and how various factors such as teaching interventions and students’ self-efficacy, attitudes, information and communication technology (ICT) activity and gender are associated with development. Design/methodology/approach Two intervention courses were implemented to improve online research skills among 7th-grade students. In the follow-up test in the 8th grade, students’ skills were measured in Web searching, critical evaluation of sources and argumentative use of Web information. Students’ self-efficacy beliefs in online research, their attitudes toward learning, behavioral intentions in online research and ICT activity were surveyed by questionnaires. Findings The main finding was that the effect observed immediately after the intervention in 7th grade did not last until the following year. A cluster analysis revealed six skill profiles characterizing strengths and weaknesses in students’ performance in the subtasks of online research and indicated that many students suffer from poor evaluation skills. Self-efficacy beliefs stood out as a student-related factor associated with the development of online research skills. Originality/value This study contributed to the pedagogy of online research skills. It indicates that small-scale interventions are not enough to enhance 7th-graders’ online research skills. Students need continuous practice in different contexts during their school years. It is important to support students’ self-efficacy to motivate them to develop their skills in all the subtasks of online research. This study also demonstrated the importance of follow-up studies in online research skills, as they have been rare thus far.

This research presents a virtual exchange project between two tertiary institutions in New Zealand and Finland with 26 participants who were intermediate German language students. During the project, the students used a closed Facebook group to post about given topics; the posts combined video, audio, and text that adhered to multimodal meaning-making theory. The theoretical framework was task-based language teaching underpinned by the notion of engagement, social media in language learning, and telecollaboration. Language learning was viewed through a socio-cultural lens. A mixed-methods approach was used to collect data including questionnaires, interviews, and FB-logs. The qualitative data was analysed by content analysis method. The results indicate that the students perceived FB as an applicable tool for community building and they enjoyed the variation it brought to the course. Collaboration, use of communication tools, authenticity, and teachers' support fostered student engagement.

Publisher Copyright: © 2021 GI_Forum. Europe has acknowledged the need to develop a very high precision digital model of the Earth, a Digital Twin Earth, running on cloud infrastructure to bring data and end-users closer together. We present results of an investigation of a proposed submodel of the digital twin, simulating the worlds’ forests. We focus on the architecture of the system and the key user needs on data content and access. The results are based on a user survey showing that the forest-related communities in Europe require information on contrasting forest variables and processes, with common interest in the status and forecast of forest carbon stock. We discuss the required spatial resolution, accuracies, and modelling tools required to match the needs of the different communities in data availability and simulation of the forest ecosystem. This, together with the knowledge on existing and projected future capabilities, allows us to specify a data architecture to implement the proposed system regionally, with the outlook to expand to continental and global scales. Ultimately, a system simulating the behaviour of forests, a digital twin, would connect the bottom-up and top-down approaches of computing the forest carbon balance: from tree-based accounting of forest growth to atmospheric measurements, respectively.

Social and pragmatic difficulties in autism spectrum disorder (ASD) are widely recognized, although their underlying neural level processing is not well understood. The aim of this study was to examine the activity of the brain network components linked to social and pragmatic understanding in order to reveal whether complex socio-pragmatic events evoke differences in brain activity between the ASD and control groups. Nineteen young adults (mean age 23.6 years) with ASD and 19 controls (mean age 22.7 years) were recruited for the study. The stimulus data consisted of video clips showing complex social events that demanded processing of pragmatic communication. In the analysis, the functional magnetic resonance imaging signal responses of the selected brain network components linked to social and pragmatic information processing were compared. Although the processing of the young adults with ASD was similar to that of the control group during the majority of the social scenes, differences between the groups were found in the activity of the social brain network components when the participants were observing situations with concurrent verbal and non-verbal communication events. The results suggest that the ASD group had challenges in processing concurrent multimodal cues in complex pragmatic communication situations.

Species traits are an essential currency in ecology, evolution, biogeography, and conservation biology. However, trait databases are unavailable for most organisms, especially those living in difficult-to-access habitats such as caves and other subterranean ecosystems. We compiled an expert-curated trait database for subterranean spiders in Europe using both literature data (including grey literature published in many different languages) and direct morphological measurements whenever specimens were available to us. We started by updating the checklist of European subterranean spiders, now including 512 species across 20 families, of which at least 192 have been found uniquely in subterranean habitats. For each of these species, we compiled 64 traits. The trait database encompasses morphological measures, including several traits related to subterranean adaptation, and ecological traits referring to habitat preference, dispersal, and feeding strategies. By making these data freely available, we open up opportunities for exploring different research questions, from the quantification of functional dimensions of subterranean adaptation to the study of spatial patterns in functional diversity across European caves.

Publisher Copyright: © 2021 The Author Programming is often misaligned with beginner students' interests and viewed as difficult. However, most students and teachers are not aware that it is possible to utilise domain-specific programming languages that combine programming with other domains like music making. Sonic Pi is one free domain-specific programming platform that enables beginners to code music, which has been designed for and used in schools since its first release in 2012. However, there is a lack of academic research on the Sonic Pi platform about the extent it may affect beginner student attitudes towards programming in a school context. The aim of this study was to investigate the extent Sonic Pi may help to promote positive attitudes towards programming. A mixed-methods case study was developed and trialled in school time with a middle school class, which measured student attitudes with the three subscales of enjoyment, importance, and anxiety. Overall, the results confirmed an alternative hypothesis that all students’ subscales for programming attitude increased significantly. While these findings are not generalisable due to its limited scope, they are very positive to inform the design and use of platforms like Sonic Pi in comparison to similar music coding platforms like EarSketch and TunePad.

Funding Information: This work was partially supported by an IBM Faculty Award, by the US National Science Foundation under grant numbers CSR-1903136, CNS-1908051, and CAREER-2046072, and by the Academy of Finland under grants number 326346, 319710, 332307 and 338854. We would like to thank Niki Loppi of the NVIDIA AI Technology Center Finland for his help with the implementation on the NVIDIA Jetson board. Publisher Copyright: © 2022 Owner/Author. Mobile Augmented Reality (AR) demands realistic rendering of virtual content that seamlessly blends into the physical environment. For this reason, AR headsets and recent smartphones are increasingly equipped with Time-of-Flight (ToF) cameras to acquire depth maps of a scene in real-time. ToF cameras are cheap and fast, however, they suffer from several issues that affect the quality of depth data, ultimately hampering their use for mobile AR. Among them, scale errors of virtual objects - appearing much bigger or smaller than what they should be - are particularly noticeable and unpleasant. This article specifically addresses these challenges by proposing InDepth, a real-time depth inpainting system based on edge computing. InDepth employs a novel deep neural network (DNN) architecture to improve the accuracy of depth maps obtained from ToF cameras. The DNN fills holes and corrects artifacts in the depth maps with high accuracy and eight times lower inference time than the state of the art. An extensive performance evaluation in real settings shows that InDepth reduces the mean absolute error by a factor of four with respect to ARCore DepthLab. Finally, a user study reveals that InDepth is effective in rendering correctly-scaled virtual objects, outperforming DepthLab.

Although researchers have discussed big data for years, they have thus far has scarcely touched on directly selling and monetizing data assets. This aspect has particular relevance given recent concerns about data privacy and security and the simultaneous explosion in the use of data for marketing and service-development purposes. In this paper, we describe an empirical study on companies’ initiatives about selling and monetizing data. We categorize the relevant business models based on several customer-refinement and scalability dimensions. We found several constraints (organization type, business type, data characteristics, privacy, and security) that companies should address to move from internally using data and supporting existing customers to generating new business through selling data. Based on the findings, we propose ways for practitioners to benefit from the data. For researchers, we provide directions for future studies that include developing strategies that foster compliance between companies’ aspirations and consumer/societal restrictions and that facilitate data-based innovation and revenue generation.

Motivation RNA viruses exhibit a high mutation rate and thus they exist in infected cells as a population of closely related strains called viral quasispecies. The viral quasispecies assembly problem asks to characterize the quasispecies present in a sample from high-throughput sequencing data. We study the de novo version of the problem, where reference sequences of the quasispecies are not available. Current methods for assembling viral quasispecies are either based on overlap graphs or on de Bruijn graphs. Overlap graph-based methods tend to be accurate but slow, whereas de Bruijn graph-based methods are fast but less accurate. Results We present viaDBG, which is a fast and accurate de Bruijn graph-based tool for de novo assembly of viral quasispecies. We first iteratively correct sequencing errors in the reads, which allows us to use large k-mers in the de Bruijn graph. To incorporate the paired-end information in the graph, we also adapt the paired de Bruijn graph for viral quasispecies assembly. These features enable the use of long-range information in contig construction without compromising the speed of de Bruijn graph-based approaches. Our experimental results show that viaDBG is both accurate and fast, whereas previous methods are either fast or accurate but not both. In particular, viaDBG has comparable or better accuracy than SAVAGE, while being at least nine times faster. Furthermore, the speed of viaDBG is comparable to PEHaplo but viaDBG is able to retrieve also low abundance quasispecies, which are often missed by PEHaplo. Availability and implementation viaDBG is implemented in C++ and it is publicly available at https://bitbucket.org/bfreirec1/viadbg. All datasets used in this article are publicly available at https://bitbucket.org/bfreirec1/data-viadbg/. Supplementary information Supplementary data are available at Bioinformatics online.

We develop GeoMatch as a novel, scalable, and efficient big-data pipeline for large-scale map matching on Apache Spark. GeoMatch improves existing spatial big-data solutions by utilizing a novel spatial partitioning scheme inspired by Hilbert space-filling curves. Thanks to its partitioning scheme, GeoMatch can effectively balance operations across different processing units and achieve significant performance gains. GeoMatch also incorporates a dynamically adjustable error-correction technique that provides robustness against positioning errors. We demonstrate the effectiveness of GeoMatch through rigorous and extensive empirical benchmarks that consider large-scale urban spatial datasets ranging from 166,253 to 3.78B location measurements. We separately assess execution performance and accuracy of map matching and develop a benchmark framework for evaluating large-scale map matching. Results of our evaluation show up to 27.25-fold performance improvements compared to previous works while achieving better processing accuracy than current solutions. We also showcase the practical potential of GeoMatch with two urban management applications. GeoMatch and our benchmark framework are open-source.

With the expansion of cyber-physical systems (CPSs) across critical and regulated industries, systems must be continuously updated to remain resilient. At the same time, they should be extremely secure and safe to operate and use. The DevOps approach caters to business demands of more speed and smartness in production, but it is extremely challenging to implement DevOps due to the complexity of critical CPSs and requirements from regulatory authorities. In this study, expert opinions from 33 European companies expose the gap in the current state of practice on DevOps-oriented continuous development and maintenance. The study contributes to research and practice by identifying a set of needs. Subsequently, the authors propose a novel approach called Secure DevOps and provide several avenues for further research and development in this area. The study shows that, because security is a cross-cutting property in complex CPSs, its proficient management requires system-wide competencies and capabilities across the CPSs development and operation.

Graphical user interfaces are widely common and present in everyday human–computer interaction, dominantly in computers and smartphones. Today, various actions are performed via graphical user interface elements, e.g., windows, menus and icons. An attractive user interface that adapts to user needs and preferences is progressively important as it often allows personalized information processing that facilitates interaction. However, practitioners and scholars have lacked an instrument for measuring user perception of aesthetics within graphical user interface elements to aid in creating successful graphical assets. Therefore, we studied dimensionality of ratings of different perceived aesthetic qualities in GUI elements as the foundation for the measurement instrument. First, we devised a semantic differential scale of 22 adjective pairs by combining prior scattered measures. We then conducted a vignette experiment with random participant (n = 569) assignment to evaluate 4 icons from a total of pre-selected 68 game app icons across 4 categories (concrete, abstract, character and text) using the semantic scales. This resulted in a total of 2276 individual icon evaluations. Through exploratory factor analyses, the observations converged into 5 dimensions of perceived visual quality: Excellence/Inferiority, Graciousness/Harshness, Idleness/Liveliness, Normalness/Bizarreness and Complexity/Simplicity. We then proceeded to conduct confirmatory factor analyses to test the model fit of the 5-factor model with all 22 adjective pairs as well as with an adjusted version of 15 adjective pairs. Overall, this study developed, validated, and consequently presents a measurement instrument for perceptions of visual qualities of graphical user interfaces and/or singular interface elements (VISQUAL) that can be used in multiple ways in several contexts related to visual human-computer interaction, interfaces and their adaption.

Over the past 20 years, open source has become a widely adopted approach to develop software. Code repositories provide software to power cars, phones, and other things that are considered proprietary. In parallel, proprietary development has evolved from rigid, centralized waterfall approaches to agile, iterative development. In this paper, we share our experiences regarding this co-evolution of open and closed source from the viewpoints of tools, practices, and organizing the development work, concluding that today’s bazaars and cathedrals have much more common characteristics than those that separate them.

A total domatic k-partition of a graph is a partition of its vertex set into k subsets such that each intersects the open neighborhood of each vertex. The maximum k for which a total domatic k-partition exists is known as the total domatic number of a graph G, denoted by d t ( G ) . We extend considerably the known hardness results by showing it is -complete to decide whether d t ( G ) ≥ 3 where G is a bipartite planar graph of bounded maximum degree. Similarly, for every k ≥ 3 , it is -complete to decide whether d t ( G ) ≥ k , where G is split or k-regular. In particular, these results complement recent combinatorial results regarding d t ( G ) on some of these graph classes by showing that the known results are, in a sense, best possible. Finally, for general n-vertex graphs, we show the problem is solvable in 2 n n O ( 1 ) time, and derive even faster algorithms for special graph classes.

We present three new standardised network concept map (CM) measures that can provide unique information about learning-related progress, which cannot be determined from previously known measures. Grounded in cognitive development theory on the one hand, and network theory on the other hand, our measures reveal how knowledge is stored, distributed and retrieved. We validated the new measures by testing their ability to discriminate between CMs of respondents with different levels of competency in statistics (students before and after taking an introductory statistics course and experts in the field of statistics). We also validated our measures against the most commonly used traditional and network measures. Based on a small sample of respondents, we show that two of the newly proposed compound measures reveal significant differences between experts and novices in the field, with higher values for experts, showing that expert knowledge is better distributed, more connected and balanced. More importantly, our measures were sensitive enough to show learning-related progress for students, albeit statistically non-significant, while common indicators from network theory did not demonstrate these small shifts. The validity of our new measures can be inferred from the consistency of the results from different sets of measures.

Understanding causal relationships is a central challenge in many research endeavours. Recent research has shown the importance of accounting for feedback (cycles) and latent confounding variables, as they are prominently present in many data analysis settings. However, allowing for cycles and latent confounders makes the structure learning task especially challenging. The constraint-based approach is able to learn causal graphs even over such general search spaces, but to obtain high accuracy, the conflicting (in)dependence information in sample data need to be resolved optimally. In this work, we develop a new practical algorithmic approach to solve this computationally challenging combinatorial optimization problem. While recent advances in exact algorithmic approaches for constraint-based causal discovery build upon off-the-shelf declarative optimization solvers, we propose a first specialized branch-and-bound style exact search algorithm. Our problem-oriented approach enables directly incorporating domain knowledge for developing a wider range of specialized search techniques for the problem, including problem-specific propagators and reasoning rules, and branching heuristics together with linear programming based bounding techniques, as well as directly incorporating different constraints on the search space, such as sparsity and acyclicity constraints. We empirically evaluate our implementation of the approach, showing that it outperforms current state of art in exact constraint-based causal discovery on real-world instances.

This paper introduces online learning related key considerations for asynchronous health information dissemination during the COVID-19 pandemic. The findings are based on 1.5 years of real-time massive scale learning intervention during this public health emergency and on related literature reviews. Meta-data analysis on World Health Organization’s (WHO) open access online learning platform OpenWHO and review on health emergency learning interventions literature. The study sought to operationalize the key considerations related to the health information dissemination as an asynchronous online learning delivery. Statistics driven findings were made based on open-source learning platform OpenWHO use case and scientific literature from the similar recorded experiences. The paper presents analysis from the recent literature and couples it with the real-time pandemic learning response results. The study suggests establishing key considerations for health emergency related learning dissemination for mass audiences: Real-time learning provision in free access, low-bandwidth and offline use formats, national and local language provision, choice of format for learners and adjustment of the learning content based on adult learning principles. The key considerations of the online learning delivery in mass mode in health emergencies emerged from the study and are recommended way forward for any international learning provided in health emergencies.

IMPORTANCE COVID-19 has highlighted widespread chronic underinvestment in digital health that hampered public health responses to the pandemic. Recognizing this, the Riyadh Declaration on Digital Health, formulated by an international interdisciplinary team of medical, academic, and industry experts at the Riyadh Global Digital Health Summit in August 2020, provided a set of digital health recommendations for the global health community to address the challenges of current and future pandemics. However, guidance is needed on how to implement these recommendations in practice. OBJECTIVE To develop guidance for stakeholders on how best to deploy digital health and data and support public health in an integrated manner to overcome the COVID-19 pandemic and future pandemics. EVIDENCE REVIEW Themes were determined by first reviewing the literature and Riyadh Global Digital Health Summit conference proceedings, with experts independently contributing ideas. Then, 2 rounds of review were conducted until all experts agreed on the themes and main issues arising using a nominal group technique to reach consensus. Prioritization was based on how useful the consensus recommendation might be to a policy maker. FINDINGS A diverse stakeholder group of 13 leaders in the fields of public health, digital health, and health care were engaged to reach a consensus on how to implement digital health recommendations to address the challenges of current and future pandemics. Participants reached a consensus on high-priority issues identified within 5 themes: team, transparency and trust, technology, techquity (the strategic development and deployment of technology in health care and health to achieve health equity), and transformation. Each theme contains concrete points of consensus to guide the local, national, and international adoption of digital health to address challenges of current and future pandemics. CONCLUSIONS AND RELEVANCE The consensus points described for these themes provide a roadmap for the implementation of digital health policy by all stakeholders, including governments. Implementation of these recommendations could have a significant impact by reducing fatalities and uniting countries on current and future battles against pandemics.

Gamification refers to the attempt to transform different kinds of systems to be able to better invoke positive experiences such as the flow state. However, the ability of such intervention to invoke flow state is commonly believed to depend on several moderating factors including the user’s traits. Currently, there is a dearth of research on the effect of user traits on the results of gamification. Gamer types (personality traits related to gaming styles and preferences) are considered some of the most relevant factors affecting the individual’s susceptibility to gamification. Therefore, in this study we investigate how gamer types from the BrainHex taxonomy (achiever, conqueror, daredevil, mastermind, seeker, socializer and survivor) moderate the effects of personalized/non-personalized gamification on users’ flow experience (challenge-skill balance, merging of action and awareness, clear goals, feedback, concentration, control, loss of self-consciousness and autotelic experience), enjoyment, perception of gamification and motivation. We conducted a mixed factorial within-subject experiment involving 121 elementary school students comparing a personalized version against a non-personalized version of a gamified education system. There were no main effects between personalization and students’ flow experience, perception of gamification and motivation, and enjoyment. Our results also indicate patterns of characteristics that can lead students to the high flow experience (e.g., those who prefer to play multiplayer have a high flow experience in both personalized and non-personalized versions). Based on our results, we provided recommendations to advance the design of gamifed educational systems.

This article presents a fault recognition strategy using multivariate hierarchical dispersion entropy to monitor the conditions of rolling bearing. First, the vibration data would be measured from multi-channel sensors synchronously. Then, the proposed mvHDE is employed to capture fault information from the collected data. Finally, the fault features are input into the ELM classifier to automatically identify fault types of bearing. The feasibility and effectiveness of the presented intelligent fault diagnosis schemes are verified through experimental studies.

This study aims at better understanding of the use of self-assessment to support high-achieving students in first-year university mathematics. The students, who had not previously self-assessed their skills and knowledge in mathematics, were given two self-assessment exercises during a calculus course: they assessed their prior knowledge and learning goals in the beginning of the course and the quality of their learning outcomes in the end. Their approaches to learning and perceptions of self-assessment were studied with questionnaires in the beginning and at the end of the course. The students felt that they were able to assess their performance and that self-assessment exercises helped them to learn. Their self-ratings agreed well with the teacher's grading. Self-assessment was implemented to support novice students to adopt a deep approach to learning, and the results showing a statistically significant decrease in unreflective approach give an encouraging signal. publishedVersion

Tensions between the people and parliament over representation are a normal feature of representative democracies. In this paper, we demonstrate how digital humanities analysis tools help in answering questions about the timing of debates on popular representation, tensions over its realization, and representatives’ changing perceptions on their parliamentary role. Our long-term approach to the conceptual history of political representation is based on the analysis of digitized parliamentary debates as nexuses of multi-sited political discourse. We combine computer-assisted distant and context-sensitive close reading to consider diachronic trends and synchronic political struggles surrounding political representation. Collocation analyses and visualizations of relative term frequencies reveal long-term patterns and anomalies, lead to new research questions, and justify the selection of cases for qualitative analysis. Here we present the first steps in the construction of a comparative interface, People and Parliament, that will include debates from several Northwest European parliaments. The interface is built on I-Analyzer, a web-based text and data mining application developed by the Utrecht University Digital Humanities Lab. We illustrate its potential with an example from the British parliament since the 2000s to demonstrate how, under an unwritten constitution, various forms of participatory democracy ranging from e-democracy to referendums have gained ground against representative democracy. While the Brexit referendum first appeared as a response to calls for strengthening direct democracy, it revealed difficulties in reconciling representative and participatory democracy. peerReviewed

Morphological and syntactic changes in word usage (as captured, e.g., by grammatical profiles) have been shown to be good predictors of a word's meaning change. In this work, we explore whether large pre-trained contextualised language models, a common tool for lexical semantic change detection, are sensitive to such morphosyntactic changes. To this end, we first compare the performance of grammatical profiles against that of a multilingual neural language model (XLM-R) on 10 datasets, covering 7 languages, and then combine the two approaches in ensembles to assess their complementarity. Our results show that ensembling grammatical profiles with XLM-R improves semantic change detection performance for most datasets and languages. This indicates that language models do not fully cover the fine-grained morphological and syntactic signals that are explicitly represented in grammatical profiles. An interesting exception are the test sets where the time spans under analysis are much longer than the time gap between them (for example, century-long spans with a one-year gap between them). Morphosyntactic change is slow so grammatical profiles do not detect in such cases. In contrast, language models, thanks to their access to lexical information, are able to detect fast topical changes., 3rd International Workshop on Computational Approaches to Historical Language Change 2022 (LChange'22)

In this article, we provide an extensive overview of a wide range of quantum games and interactive tools that have been employed by the community in recent years. The paper presents selected tools, as described by their developers. The list includes Hello Quantum, Hello Qiskit, Particle in a Box, Psi and Delta, QPlayLearn, Virtual Lab by Quantum Flytrap, Quantum Odyssey, ScienceAtHome, and The Virtual Quantum Optics Laboratory. Additionally, we present events for quantum game development: hackathons, game jams, and semester projects. Furthermore, we discuss the Quantum Technologies Education for Everyone (QUTE4E) pilot project, which illustrates an effective integration of these interactive tools with quantum outreach and education activities. Finally, we aim at providing guidelines for incorporating quantum games and interactive tools in pedagogic materials to make quantum technologies more accessible for a wider population., 39 pages, 17 figures

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In this paper we study the problem of comparing a set of data between two parties in a peer-To-peer network to determine the number of common friends. Several protocols for private set intersection are presented in the literature. When the sets are large these tend to be too slow for many purposes. We consider the problem of two parties finding out how many common friends they have in a privacy preserving way. This problem has arisen in designing a peer-To-peer platform called HELIOS. We present our solution for the problem that is more efficient than older protocols but still sufficiently privacy-friendly for our purposes. The solution is based on iteratively revealing information about the hash values of friends' identities in small increments.

Despite recent advances in the personalization of education, it is still unknown how different kinds of personalization affect students’ experiences. To advance this literature, in this article, we present an experimental study with 307 participants investigating the effects of gender stereotype-based interfaces (in terms of colors and avatars stereotypes) on users’ flow experience (i.e., challenge–skill balance, merging of action and awareness, clear goals, feedback, concentration, control, loss of self-consciousness, andautotelicexperience), and performance in a gamified educational system. The main results indicate that gender stereotype-based interfaces affect users’ action–awareness merging, however, do not affect users’ performance and overall flow experience. We contribute with the basis for new studies and challenge thorough future research attempts.

The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, network operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network service the traffic needs for maintaining an acceptable quality of experience. Therefore, end devices are a natural fit for performing traffic classification since end devices have more contextual information about the device usage and traffic. This paper proposes a novel approach that classifies multimedia traffic types produced and consumed on mobile devices. The technique relies on a mobile device's detection of its multimedia context characterized by its utilization of different media input/output components, e.g., camera, microphone, and speaker. We develop an algorithm, MediaSense, which senses the states of multiple I/O components and identifies the specific multimedia context of a mobile device in real-time. We demonstrate that MediaSense classifies encrypted multimedia traffic in real-time as accurately as deep learning approaches and with even better generalizability.

The goal of this study was to evaluate the effect of using GeoGebra with the ARCS (Attention, Relevance, Confidence, and Satisfaction) model on academic achievement and motivation. In this regard, an experimental and a control group were constituted. The academic motivation questionnaire (Harter, 1981) was used to measure participant’s motivation. Further, two instances of a multiple-choice questions test on a topic in Geometry were designed to measure student’s academic achievement. In order to collect data, the pre-tests were applied to each group at the beginning of the lessons. The experimental group was taught using GeoGebra and the control group was trained with the traditional teaching method. At the end of the lessons, the post-tests were administered to both groups. The statistical difference between participant’s post-test academic motivation and learning of the experimental and control group was analyzed with ANCOVA after examining the assumptions of this test, namely normality and homogeneity in each group. Results of the study indicated that the scores of academic achievement and motivation in the experimental group were significantly more than that of the control group. publishedVersion

The workshop Digital Parliamentary Data in Action (DiPaDA 2022) was organised in Uppsala on March 15, 2022, co-located with the 6th Digital Humanities in the Nordic and Baltic Countries Conference (DHNB). These workshop proceedings reflect the aims of the workshop to foster interaction and stimulate conversations between humanities, social sciences, and computational sciences – representing scholars from the Nordic region and beyond that work with digital parliamentary data. The contributions in the proceedings present results of ongoing research on creating and using historical and present parliamentary data to study parliamentary culture, politics, language use, and the media. Moreover, the contributions offer novel perspectives on applying, curating, and representing this key societal data, and discuss the future opportunities and challenges in such research.

Nation and nationhood are among the most frequently studied concepts in the field of intellectual history. At the same time, the word ‘nation’ and its historical usage are very vague. The aim in this article was to develop a data-driven method using dependency parsing and neural word embeddings to clarify some of the vagueness in the evolution of this concept. To this end, we propose the following two-step method. First, using linguistic processing, we create a large set of words pertaining to the topic of nation. Second, we train diachronic word embeddings and use them to quantify the strength of the semantic similarity between these words and thereby create meaningful clusters, which are then aligned diachronically. To illustrate the robustness of the study across languages, time spans, as well as large datasets, we apply it to the entirety of five historical newspaper archives in Dutch, Swedish, Finnish, and English. To our knowledge, thus far there have been no large-scale comparative studies of this kind that purport to grasp long-term developments in as many as four different languages in a data-driven way. A particular strength of the method we describe in this article is that, by design, it is not limited to the study of nationhood, but rather expands beyond it to other research questions and is reusable in different contexts.

We consider shared response modeling, a multi-view learning problem where one wants to identify common components from multiple datasets or views. We introduce Shared Independent Component Analysis (ShICA) that models each view as a linear transform of shared independent components contaminated by additive Gaussian noise. We show that this model is identifiable if the components are either non-Gaussian or have enough diversity in noise variances. We then show that in some cases multi-set canonical correlation analysis can recover the correct unmixing matrices, but that even a small amount of sampling noise makes Multiset CCA fail. To solve this problem, we propose to use joint diagonalization after Multiset CCA, leading to a new approach called ShICA-J. We show via simulations that ShICA-J leads to improved results while being very fast to fit. While ShICA-J is based on second-order statistics, we further propose to leverage non-Gaussianity of the components using a maximum-likelihood method, ShICA-ML, that is both more accurate and more costly. Further, ShICA comes with a principled method for shared components estimation. Finally, we provide empirical evidence on fMRI and MEG datasets that ShICA yields more accurate estimation of the components than alternatives., Comment: Accepted at NeurIPS 2021