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This study investigates the interplay between open access (OA), coauthorship, and international research collaboration. While previous research has dealt with these factors separately, there is a knowledge gap in how these interact within a single data set. The data includes all Scopus-indexed journal articles published over 11 years (2009–2019) where at least one of the authors has an affiliation to a United Arab Emirates (UAE) institution (30,400 articles in total). To assess the OA status of articles, the study utilized Unpaywall data for articles with a digital object identifier, and manual web searches for articles without. There was consistently strong growth in publication volume counts as well as shares of OA articles across the years. The analysis provides statistically significant results supporting a positive relationship between a higher number of coauthors (in particular international) and the OA status of articles. Further research is needed to investigate potentially explaining factors for the relationship between coauthorship and increased OA rate, such as implementation of national science policy initiatives, varying availability of funding for OA publishing in different countries, patterns in adoption of various OA types in different coauthorship constellations, and potentially unique discipline-specific patterns as they relate to coauthorship and OA rate.

High-level synthesis (HLS) enables automated conversion of high-level language algorithms into synthesizable register-transfer level code, allowing computation intensive algorithms to be accelerated on FPGAs. Most HLS tools have C++ as their input language, as it is widely known in both software and hardware industry. However, even though C++ receives a new standard every three years, the HLS tool vendors have mostly provided support and examples using C++98/03. Limiting to early C++ standards imposes a productivity penalty, since the newer standards provide both compilation time reductions and more concise, expressive, and maintainable way of writing code. In this study, we make the case for adopting modern C++ in HLS. We inspect the language features of C++11 and forward, and consider their benefits for HLS. We also test the present support for the modern language features with two state-of-the-art commercial HLS tools. Finally, we provide an extended example, demonstrating the increased clarity of code achieved using the newer standards. We note that the investigated HLS tools already have good support for modern C++ features, and urge their adoption to increase designer productivity. publishedVersion

Funding Information: This research was funded by Academy of Finland grants no. 314573 , 314602 , 332962 , and 335872 , and EU Horizon-2020 grant no. 957337 MARVEL. The authors would like to thank the APPLE consortium for the help in the project. Publisher Copyright: © 2023 The Author(s) | openaire: EC/H2020/957337/EU//MARVEL In order to study how early emotional experiences shape infant development, one approach is to analyze the emotional content of speech heard by infants, as captured by child-centered daylong recordings, and as analyzed by automatic speech emotion recognition (SER) systems. However, since large-scale daylong audio is initially unannotated and differs from typical speech corpora from controlled environments, there are no existing in-domain SER systems for the task. Based on existing literature, it is also unclear what is the best approach to deploy a SER system for a new domain. Consequently, in this study, we investigated alternative strategies for deploying a SER system for large-scale child-centered audio recordings from a neonatal hospital environment, comparing cross-corpus generalization, active learning (AL), and domain adaptation (DA) methods in the process. We first conducted simulations with existing emotion-labeled speech corpora to find the best strategy for SER system deployment. We then tested howthe findings generalize to our new initially unannotated dataset. As a result, we found that the studied AL method provided overall the most consistent results, being less dependent on the specifics of the training corpora or speech features compared to the alternative methods. However, in situations without the possibility to annotate data, unsupervised DA proved to be the best approach. We also observed that deployment of a SER system for real-world daylong child-centered audio recordings achieved a SER performance level comparable to those reported in literature, and that the amount of human effort required for the system deployment was overall relatively modest.

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” Federated learning (FL) is a promising privacy-preserving solution to build powerful AI models. In many FL scenarios, such as healthcare or smart city monitoring, the user's devices may lack the required capabilities to collect suitable data which limits their contributions to the global model. We contribute social-aware federated learning as a solution to boost the contributions of individuals by allowing outsourcing tasks to social connections. We identify key challenges and opportunities, and establish a research roadmap for the path forward. Through a user study with N = 30 participants, we study collaborative incentives for FL showing that social-aware collaborations can significantly boost the number of contributions to a global model provided that the right incentive structures are in place.

The data produced by various services should be stored and managed in an appropriate format for gaining valuable knowledge conveniently. This leads to the emergence of various data models, including relational, semi-structured, and graph models, and so on. Considering the fact that the mature relational databases established on relational data models are still predominant in today's market, it has fueled interest in storing and processing semi-structured data and graph data in relational databases so that mature and powerful relational databases' capabilities can all be applied to these various data. In this survey, we review existing methods on mapping semi-structured data and graph data into relational tables, analyze their major features, and give a detailed classification of those methods. We also summarize the merits and demerits of each method, introduce open research challenges, and present future research directions. With this comprehensive investigation of existing methods and open problems, we hope this survey can motivate new mapping approaches through drawing lessons from eachmodel's mapping strategies, aswell as a newresearch topic - mapping multi-model data into relational tables.

Earlier research has shown that few studies in Natural Language Generation (NLG) evaluate their system outputs using an error analysis, despite known limitations of automatic evaluation metrics and human ratings. This position paper takes the stance that error analyses should be encouraged, and discusses several ways to do so. This paper is not just based on our shared experience as authors, but we also distributed a survey as a means of public consultation. We provide an overview of existing barriers to carry out error analyses, and proposes changes to improve error reporting in the NLG literature.

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

Model selection is one of the most central tasks in supervised learning. Validation set methods are the standard way to accomplish this task: models are trained on training data, and the model with the smallest loss on the validation data is selected. However, it is generally not obvious how much validation data is required to make a reliable selection, which is essential when labeled data are scarce or expensive. We propose a bootstrap-based algorithm, bootstrap validation (BSV), that uses the bootstrap to adjust the validation set size and to find the best-performing model within a tolerance parameter specified by the user. We find that BSV works well in practice and can be used as a drop-in replacement for validation set methods or k-fold cross-validation. The main advantage of BSV is that less validation data is typically needed, so more data can be used to train the model, resulting in better approximations and efficient use of validation data.

While we instantaneously recognize a face as attractive, it is much harder to explain what exactly defines personal attraction. This suggests that attraction depends on implicit processing of complex, culturally and individually defined features. Generative adversarial neural networks (GANs), which learn to mimic complex data distributions, can potentially model subjective preferences unconstrained by pre-defined model parameterization. Here, we present generative brain-computer interfaces (GBCI), coupling GANs with brain-computer interfaces. GBCI first presents a selection of images and captures personalized attractiveness reactions toward the images via electroencephalography. These reactions are then used to control a GAN model, finding a representation that matches the features constituting an attractive image for an individual. We conducted an experiment (N=30) to validate GBCI using a face-generating GAN and producing images that are hypothesized to be individually attractive. In double-blind evaluation of the GBCI-produced images against matched controls, we found GBCI yielded highly accurate results. Thus, the use of EEG responses to control a GAN presents a valid tool for interactive information-generation. Furthermore, the GBCI-derived images visually replicated known effects from social neuroscience, suggesting that the individually responsive, generative nature of GBCI provides a powerful, new tool in mapping individual differences and visualizing cognitive-affective processing.

Objective: Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. Automating this process with utmost accuracy is, therefore, highly desirable due to the advent of wearable ECG sensors. However, even with numerous deep learning approaches proposed recently, there is still a notable gap in the performance of global and patient-specific ECG classification performance. Methods: In this study, we propose a novel approach for inter-patient ECG classification using a compact 1D Self-ONN by exploiting morphological and timing information in heart cycles. We used 1D Self-ONN layers to automatically learn morphological representations from ECG data, enabling us to capture the shape of the ECG waveform around the R peaks. We further inject temporal features based on RR interval for timing characterization. The classification layers can thus benefit from both temporal and learned features for the final arrhythmia classification. Results: Using the MIT-BIH arrhythmia benchmark database, the proposed method achieves the highest classification performance ever achieved, i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N) segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10% recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs). Significance: As a pioneer application, the results show that compact and shallow 1D Self-ONNs with the feature injection can surpass all state-of-the-art deep models with a significant margin and with minimal computational complexity. Conclusion: This study has demonstrated that using a compact and superior network model, a global ECG classification can still be achieved with an elegant performance level even when no patient-specific information is used. publishedVersion

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.

Abstract. Emotions are an important element of human interactions, including those in social media. Despite the prominence of text-based messages in online communication, little is known about how the emotional tone of the messages affects the emotions of the recipients. With three experiments, the current study investigated these effects in the context of online news discussions. Participants first read news discussion threads with a negative, neutral, and positive tone, and then rated their subjective emotional state in terms of valence and arousal. Results showed that negatively toned discussions induced more negative valence and higher arousal ratings than the other conditions. Positive discussions had an opposite effect. Emotionally toned online comments evidently affect the quality and the intensity of the readers’ subjective emotions. We discuss implications for future research and emotion mitigation strategies to improve online discussion quality.

Detailed 3D quantification of tree structure plays a crucial role in understanding tree- and plot-level biophysical processes. Light detection and ranging (LiDAR) has led to a revolution in tree structural measurements and its 3D data are increasingly becoming publicly available. Yet, calculating structural metrics from LiDAR data can often be complex and time-consuming and potentially requires expert knowledge. We present the R package Individual Tree Structural Metrics (ITSMe), a toolbox that works with LiDAR tree point clouds and quantitative structure models (QSMs) derived from LiDAR point clouds to obtain individual tree structural metrics. It serves as a robust synthesis framework for researchers who want to readily obtain structural information from 3D data of individual trees. The package includes functions to determine basic structural metrics (tree height, diameter at breast height, diameter above buttresses, projected crown area, 3D alpha crown volume) from individual tree point clouds, as well as more complex structural metrics (individual tree component volumes, branch angle-, radius- and length-related metrics) from QSMs. The ITSMe package is an open-source package hosted on GitHub that will make the use of 3D data more straightforward and transparent for a range of end-users interested in exploiting tree structure information. publishedVersion

Interpersonal touch is critical for social-emotional development and presents a powerful modality for communicating emotions. Virtual agents of the future could capitalize on touch to establish social bonds with humans and facilitate cooperation in virtual reality (VR). We studied whether the emotional expression of a virtual agent would affect the way humans touch the agent. Participants were asked to hold a pressure-sensing tube presented as the agent's arm in VR. Upon seeing the agent's emotional expression change, participants briefly squeezed the arm. The effect of emotional expressions on affective state was measured using self-reported valence and arousal as well as physiology-based indices. Onset, duration, and intensity of the squeeze were recorded to examine the haptic responses. Emotional expression of agents affected squeeze intensity and duration through changes in emotional perception and experience. Haptic responses may thus provide an implicit measure of persons' experience towards their virtual companion.

Dangers associated with poor air quality are driving deployments of air quality monitoring technology worldwide. Having a comprehensive understanding of the health effects of pollutants requires understanding both the distribution and dispersion of pollutants in the environment, but currently this information is highly difficult to capture. This article presents a vision for city-scale air pollution monitoring that uses unmanned aerial vehicles (UAVs) to complement current ground and infrastructure-based measurements with a vertical profile of pollutants. We highlight the key requirements and research challenges, demonstrate the benefits UAVs bring through measurements from an industrial and a residential location, and establish a research roadmap for the path forward.

PurposeThis article aims to elaborate the context-sensitive nature of credibility assessment by examining how such judgments are made in online discussion in times of uncertainty caused by Finland's intent to join the North Atlantic Treaty Organization (NATO) in spring 2022.Design/methodology/approachThe empirical findings draw on the qualitative content analysis of 3,324 posts submitted to a Finnish online discussion in February–March 2022. It was examined how the participants of online discussion assess the credibility of information sources referred to in debates on the NATO membership. It is assumed that the believability of the author of information is indicative of his or her expert power, for example based on the credentials of a scholar, while the credibility of information content, for example the provision of factual evidence is indicative of the source's informational power.FindingsPolitical decision-makers, particularly the President of Finland were assessed as most credible information sources, due to their access to confidential knowledge and long-time experience in politics. The credibility assessments differed more strongly while judging the believability of researchers. On the one hand, their expertise was praised; on the other hand, doubts were presented about their partiality. Fellow participants of online discussion were assessed most negatively because information sources of these types are associated with low expert and informational power.Research limitations/implicationsAs the study concentrated on credibility assessments made in a Finnish online discussion group, the findings cannot be extended to concern the credibility judgments occurring information in other contexts.Originality/valueThe study is among the first to characterize the role of expert and informational power in credibility assessment in times of uncertainty.

PurposeThe purpose of this paper is to examine what types of task information media scholars need while gathering research data to create new knowledge.Design/methodology/approachThe research design is qualitative and user-oriented. A total of 25 media scholars were interviewed about their research processes and interactions with their research data. The interviews were semi-structured, complemented by critical incident interviews. The analysis focused on the activity of gathering research data. A typology of information (task, domain and task-solving information) guided the analysis of information types related to data gathering, with further analysis focusing only on task information types.FindingsMedia scholars needed the following task information types while gathering research data to create new knowledge: (1) information about research data (aboutness of data, characteristics of data, metadata and secondary information about data), (2) information about sources of research data (characteristics of sources, local media landscapes) and (3) information about cases and their contexts (case information, contextual information). All the task information types should be considered when building data services and tools to support media scholars' work.Originality/valueThe paper increases understanding of the concept of task information in the context of gathering research data to create new knowledge and thereby informs the providers of research data services about the task information types that researchers need.

Humans tend to spend most of their life indoors, making the quality of indoor environments essential for human health and wellbeing. While several solutions for monitoring the indoor environment have been proposed, ranging from infrastructure-based monitoring solutions to cameras, these tend to require separate installation, making the sensors difficult to maintain and upgrade. In this article, we introduce the idea of using smart plants as an easy-to-deploy and affordable solution for monitoring the indoor environment. Plants are typically deployed close to humans and they increasingly are placed in containers that integrate sensors, such as soil moisture, temperature, humidity, and CO2 sensors. We demonstrate how these sensors can be used as an alternative technology for monitoring-and enriching-indoor spaces without needing to install proprietary sensors or other technology. Specifically, we show how smart plants can be used to estimate overall CO2 accumulation, occupancy information, and whether people use protective face masks or not. We also establish a research roadmap for the use of smart plants to monitor indoor environments.

Versatile Video Coding (VVC/H.266) is the latest video coding standard designed for a broad range of next-generation media applications. This paper explores the design space of practical VVC encoding by profiling the Fraunhofer Versatile Video Encoder (VVenC). All experiments were conducted over five 2160p video sequences and their downsampled versions under the random access (RA) condition. The exploration was performed by analyzing the rate-distortion-complexity (RDC) of the VVC block structure and coding tools. First, VVenC was profiled to provide a breakdown of coding block distribution and coding tool utilization in it. Then, the usefulness of each VVC coding tool was analyzed for its individual impact on overall RDC performance. Finally, our findings were elevated to practical implementation guidelines: the highest coding gains come with the multi type tree (MTT) structure, adaptive loop filter (ALF), cross component linear model (CCLM), and bi-directional optical flow (BDOF) coding tools, whereas multi transform selection (MTS) and affine motion estimation are the primary candidates for complexity reduction. To the best of our knowledge, this is the first work to provide a comprehensive RDC analysis for practical VVC encoding. It can serve as a basis for practical VVC encoder implementation or optimization on various computing platforms. publishedVersion

Existing hedging strategies are typically based on specific financial models: either the strategies are directly based on a given option pricing model or stock price and volatility models are used indirectly by generating synthetic data on which an agent is trained with reinforcement learning. In this paper, we train an agent in a pure data-driven manner. Particularly, we do not need any specifications on volatility or jump dynamics but use large empirical intra-day data from actual stock and option markets. The agent is trained for the hedging of derivative securities using deep reinforcement learning (DRL) with continuous actions. The training data consists of intra-day option price observations on S&P500 index over 6 years, and top of that, we use other data periods for validation and testing. We have two important empirical results. First, a DRL agent trained using synthetic data generated from a calibrated stochastic volatility model outperforms the classic Black–Scholes delta hedging strategy. Second, and more importantly, we find that a DRL agent, which is empirically trained directly using actual intra-day stock and option prices without the prior specification of the underlying volatility or jump processes, has superior performance compared with the use of synthetic data. This implies that DRL can capture the dynamics of S&P500 from the actual intra-day data and to self-learn how to hedge actual options efficiently. publishedVersion

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Linear discriminant analysis (LDA) is a classical statistical machine-learning method, which aims to find a linear data transformation increasing class discrimination in an optimal discriminant subspace. Traditional LDA sets assumptions related to the Gaussian class distributions and single-label data annotations. In this article, we propose a new variant of LDA to be used in multilabel classification tasks for dimensionality reduction on original data to enhance the subsequent performance of any multilabel classifier. A probabilistic class saliency estimation approach is introduced for computing saliency-based weights for all instances. We use the weights to redefine the between-class and within-class scatter matrices needed for calculating the projection matrix. We formulate six different variants of the proposed saliency-based multilabel LDA (SMLDA) based on different prior information on the importance of each instance for their class(es) extracted from labels and features. Our experiments show that the proposed SMLDA leads to performance improvements in various multilabel classification problems compared to several competing dimensionality reduction methods. publishedVersion

Publisher Copyright: © 2023 The Author(s) Context: Software startups are important drivers of economy on a global scale, and have become associated with innovation and high growth. However, the overwhelming majority of startups ends in failure. Many of these startup failures ultimately stem from software engineering issues, and requirements engineering (RE) ones in particular. Despite the emphasis placed on the importance of RE activities in the startup context, many startups continue to develop software without a clear market or customer, having never had meaningful contact with their would-be customer. Objective: We develop a method aimed at early-stage startups that is intended to help startups through the initial stages of the startup process: StartCards. The method emphasizes the importance of idea and product validation activities in particular in order to tackle anti-patterns related to (a lack of) RE in startups. This method is based on existing literature, both grey and academic literature. Method: StartCards was developed using the Canonical Action Research (CAR) approach, over the course of 4 AR cycles. During the AR process, the method was used by 44 student startup teams in a practical course setting. Data from the use of the method was collected through self-reporting in the form of modified learning diaries, mentoring meetings with the startup teams, and a qualitative survey. Results: We consider the current version of StartCards useful for early-stage startups based on the data we have collected. The method can also be used as a pedagogical tool in startup education. Conclusions: The paper presents the first published version of the method. While work on the method continues, the method is deemed ready for use.

Context: Tertiary education institutions aim to prepare their computer science and software engineering students for working life. While much of the technical principles are covered in lower-level courses, team-based capstone courses are a common way to provide students with hands-on experience and teach soft skills. Objective: This paper explores the characteristics of project-based software engineering capstone courses presented in the literature. The goal of this work is to understand the pros and cons of different approaches by synthesising the various aspects of software engineering capstone courses and related experiences. Method: In a systematic literature review for 2007–2022, we identified 127 articles describing real-world capstone courses. These articles were analysed based on their presented course characteristics and the reported course outcomes. Results: The characteristics were synthesised into a taxonomy consisting of duration, team sizes, client and project sources, project implementation, and student assessment. We found out that capstone courses generally last one semester and divide students into groups of 4–5 where they work on a project for a client. For a slight majority of courses, the clients are external to the course staff and students are often expected to produce a proof-of-concept level software product as the main end deliverable. The courses generally include various forms of student assessment both during and at the end of the course. Conclusions: This paper provides researchers and educators with a classification of characteristics of software engineering capstone courses based on previous research. We also further synthesise insights on the reported course outcomes. Our review study aims to help educators to identify various ways of organising capstones and effectively plan and deliver their own capstone courses. The characterisation also helps researchers to conduct further studies on software engineering capstones. Context: Tertiary education institutions aim to prepare their computer science and software engineering students for working life. While much of the technical principles are covered in lower-level courses, team-based capstone courses are a common way to provide students with hands-on experience and teach soft skills. Objective: This paper explores the characteristics of project-based software engineering capstone courses presented in the literature. The goal of this work is to understand the pros and cons of different approaches by synthesising the various aspects of software engineering capstone courses and related experiences. Method: In a systematic literature review for 2007–2022, we identified 127 articles describing real-world capstone courses. These articles were analysed based on their presented course characteristics and the reported course outcomes. Results: The characteristics were synthesised into a taxonomy consisting of duration, team sizes, client and project sources, project implementation, and student assessment. We found out that capstone courses generally last one semester and divide students into groups of 4–5 where they work on a project for a client. For a slight majority of courses, the clients are external to the course staff and students are often expected to produce a proof-of-concept level software product as the main end deliverable. The courses generally include various forms of student assessment both during and at the end of the course. Conclusions: This paper provides researchers and educators with a classification of characteristics of software engineering capstone courses based on previous research. We also further synthesise insights on the reported course outcomes. Our review study aims to help educators to identify various ways of organising capstones and effectively plan and deliver their own capstone courses. The characterisation also helps researchers to conduct further studies on software engineering capstones.

Experience has shown that word-by-word translation between two languages is a utopia. Although languages are composed of words, the meanings that the words convey cannot be translated to another language by simply translating the individual words. Languages contain many types of word clusters that must be handled as special units, each cluster type requiring its special ways of treatment. Because the compilation of multi-word expressions (MWE) is tedious and time-consuming work, there is motivation to re-use the MWEs in other applications, including the translation between other language pairs. It is evident that only part of the MWEs in one application cand be directly converted to another application. The reason is that a large part of MWEs is dependent on the language pair, for which they have been identified. The translation between closely related languages requires less MWEs than the translation between very different languages. In this report I will investigate whether the MWEs in English to Finnish machine translation can be converted into MWEs in Finnish to English machine translation. It is obvious that only part of the cases are still MWEs after conversion. For example, if a MWE is a many-to-one type, meaning that a word cluster in source language maps to a single word in target language, after conversion it is not a MWE any more. Because the list of MWEs in the current translation system is in the form of Constraint Grammar rules, the conversion process is far from trivial. I will show the conversion process in detail.

In the last few years, the field of quantum computing has experienced remarkable progress. The prototypes of quantum computers already exist and have been made available to users through cloud services (e.g., IBM Q experience, Google quantum AI, or Xanadu quantum cloud). While fault-tolerant and large-scale quantum computers are not available yet (and may not be for a long time, if ever), the potential of this new technology is undeniable. Quantum algorithms have the proven ability to either outperform classical approaches for several tasks, or are impossible to be efficiently simulated by classical means under reasonable complexity-theoretic assumptions. Even imperfect current-day technology is speculated to exhibit computational advantages over classical systems. Recent research is using quantum computers to solve machine learning tasks. Meanwhile, the database community already successfully applied various machine learning algorithms for data management tasks, so combining the fields seems to be a promising endeavour. However, quantum machine learning is a new research field for most database researchers. In this tutorial, we provide a fundamental introduction to quantum computing and quantum machine learning and show the potential benefits and applications for database research. In addition, we demonstrate how to apply quantum machine learning to the optimization of join order problem for databases. Non

This work presents an unsupervised method of selecting filters and threshold values for the OpusFilter parallel corpus cleaning toolbox. The method clusters sentence pairs into noisy and clean categories and uses the features of the noisy cluster center as filtering parameters. Our approach utilizes feature importance analysis to disregard filters that do not differentiate between clean and noisy data. A randomly sampled subset of a given corpus is used for filter selection and ineffective filters are not run for the full corpus. We use a set of automatic evaluation metrics to assess the quality of translation models trained with data filtered by our method and data filtered with OpusFilter’s default parameters. The trained models cover English-German and English-Ukrainian in both directions. The proposed method outperforms the default parameters in all translation directions for almost all evaluation metrics. This work presents an unsupervised method of selecting filters and threshold values for the OpusFilter parallel corpus cleaning toolbox. The method clusters sentence pairs into noisy and clean categories and uses the features of the noisy cluster center as filtering parameters. Our approach utilizes feature importance analysis to disregard filters that do not differentiate between clean and noisy data. A randomly sampled subset of a given corpus is used for filter selection and ineffective filters are not run for the full corpus. We use a set of automatic evaluation metrics to assess the quality of translation models trained with data filtered by our method and data filtered with OpusFilter’s default parameters. The trained models cover English-German and English-Ukrainian in both directions. The proposed method outperforms the default parameters in all translation directions for almost all evaluation metrics.

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.

PurposeData economy is pervasively present in our everyday lives. Still, ordinary laypersons' chances to genuine communication with other stakeholders are scarce. This paper aims to raise awareness about communication patterns in the context of data economy and initiate a dialogue about laypersons' position in data economy ecosystems.Design/methodology/approachThis conceptual paper covers theory-based critical reflection with ethical- and empirical-based remarks. It provides novel perspectives both for research and stakeholder collaboration.FindingsThe authors suggest invitational rhetoric and Habermasian discourse as instruments towards understanding partnership between all stakeholders of the data economy to enable laypersons to transfer from subjectivity to the agency.Originality/valueThe authors provide (1) theory-based critical reflection concerning communication patterns in the data economy; (2) both ethical and empirical-based remarks about laypersons' position in data economy and (3) ideas for interdisciplinary research and stakeholder collaboration practices by using invitational rhetoric and rational discourse. By that, this paper suggests taking a closer look at communication practices and ethics alike in the data economy. Moreover, it encourages clear, rational and justified arguments between stakeholders in a respectful and equal environment in the data economy ecosystems.

We quantify and derive a general model for the collaboration stability of human mobility and demonstrate its importance for networking applications. Our results demonstrate that collaboration opportunities are highly dependent on the context where they take place, with diurnal patterns and spatial characteristics being particularly important.

The paper analyses Library and Information Science (LIS) articles published in leading international LIS journals based on their authors’ disciplinary backgrounds. The study combines content analysis of articles with authors’ affiliation analysis. The main research question is: Are authors’ disciplinary backgrounds associated with choice of research topics and methods in LIS articles? The study employs a quantitative content analysis of articles published in 30 + scholarly LIS journals in 2015, focusing on research topics and methods. The articles are also assigned to three disciplinary categories based on authors’ affiliations: External (no authors from LIS institutions), Internal (all authors from LIS institutions), and Mixed (some authors from LIS institutions, some from outside). The association of articles’ disciplinary categories with article research topics and methods is analysed quantitatively. Most research contributions to LIS come from external articles (57%). However, LIS scholars have a clear majority in research on L&I services and institutions (68%), while external scholars dominate the contributions in Information retrieval (73%) and Scientific communication (Scientometrics, 69%). Internal articles tend to have an intermediary’s (29%) or end-user’s (22%) viewpoint on information dissemination while the external ones have developer’s viewpoint (27%) or no dissemination viewpoint (49%). Among research strategies, survey (29%) and concept analysis (23%) dominate internal articles, survey (28%) and citation analysis (19%) dominate mixed articles, and survey (20%) and citation analysis (19%) dominate external articles. The application profiles of research strategies varied somewhat between disciplinary categories and main topics. Consequently, the development of LIS in the areas of Information retrieval, Information seeking, and Scientific communication seems highly dependent on the contribution of other disciplines. As a small discipline, LIS may have difficulties in responding to the challenges of other disciplines interested in research questions in these three areas.

The aim of this article is in developing unifying frequency domain theory for robust regulation of multi-input multi-output systems. The main theoretical results achieved are a new formulation of the internal model principle, solvability conditions for the robust regulation problem, and a parameterization of all robustly regulating controllers. The main results are formulated with minimal assumptions and without using coprime factorizations, thus guaranteeing their applicability with a very general class of systems. In addition to theoretical results, the design of robust controllers is addressed. The results are illustrated by two examples involving a delay and a heat equation. publishedVersion

Littering is a significant challenge for environmental sustainability and a major burden for cities and densely populated areas. Current solutions for litter monitoring, such as litter watch campaigns and city-operated litter collection, are costly and challenging to conduct at a large scale. This article presents a vision for using autonomous ground vehicles (AGVs) for litter monitoring and removal and introduces a mechanism for AGVs that uses thermal dissipation resulting from sunlight to identify and remove litter objects. We identify and highlight key challenges for deploying the envisioned solution on a city scale, and demonstrate the feasibility of the solution through extensive experiments.

As software is becoming a central element in our lives, more stakeholders have concerns. Unlike today, when developers stop their coding activities to satisfy these stakeholder concerns, we propose dealing with them as a part of the coding workflow.

Accompanied by the upsurging adoption of open source software (OSS) in a wide range of complex software-intensive systems, practitioners often raise multiple concerns about their maintainability and sustainability. A system that embeds unmaintained components is fragile towards various risks which could result in dire consequences. To address such concerns, this paper introduces the OSS Abandonment Risk Assessment (OSSARA) model, which evaluates the abandonment risk by synthesizing the prediction of potential abandonment of the embedded OSS components. publishedVersion

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.

We propose to leverage recommender systems from machine learning to build large-scale community distribution models for the mammalian fossil record. Recommender systems are behind most online life today, from shopping to news personalisation, online dating, or the selection of study programmes or fastest routes. Many recommender systems work by predicting user preferences from items that occur together in user profiles. Technically, this setting closely resembles co-occurrence of species in natural environments. Here we frame community distribution modelling as a recommender systems task, tailor existing recommender techniques for this purpose and propose optimisation criteria for fitting the models in the ecological context. The predictive power comes from species co-occurrences. We demonstrate the potential of this approach for analysing past ecosystems on a case study of Miocene fossil sites in Europe, where we use the proposed community distribution modelling for reconstructing companionships and relative abundances of large mammals. The proposed approach to community distribution modelling, although not climatically explicit, can help to reconstruct past ecosystems and analyse their structure and dynamics over time and space. It also allows, even coarsely, to predict relative abundances of fossil species from presence-absence data. More generally, the proposed perspective is a means for analysis of fossil communities and the relationships between their ecological contexts.

PurposeLocation-based games (LBGs) have afforded novel information technology (IT) developments in how people interact with the physical world. Namely, LBGs have spurred a wave of territoriality (i.e. controlling) and exploration (i.e. discovering) of augmented physical space that are driven by different social dynamics related to group formation, social connectivity and altruism. The aim of this study is to investigate this dynamic and how it is further related to the use intensity of location-based IT.Design/methodology/approachThis work presents a structural equation model that connects social dimensions of play to territorial control and exploration, and playing intensity. The model was tested with psychometric data gathered from a global sample of Pokémon GO players (N = 515).FindingsIn the tested sample, players' social self-efficacy and altruism were positively associated with team identification. Team identification, in turn, was positively associated with both territorial control and exploration tendency. Territorial control had a significant relationship with playing intensity; however, exploration tendency did not. This implies territorial control is the stronger predictor of playing intensity.Practical implicationsThe findings suggest that human primal urges to conquer and control geographical territory may surface in the digital reimagination of physical space. LBGs offer opportunities for making use of new forms of play (territorial control and exploration) in motivating locative behaviours.Originality/valueThis research quantifies the relationships between a social predisposition, team identification, territorial control, exploration tendency and playing intensity in the context of Pokémon GO. It contributes new knowledge to the understanding of territorial behaviour (control and exploration) in location-based IT.

The study analyses contributions to Library and Information Science (LIS) by researchers representing various disciplines. How are such contributions associated with the choice of research topics and methodology? The study employs a quantitative content analysis of articles published in 31 scholarly LIS journals in 2015. Each article is seen as a contribution to LIS by the authors' disciplines, which are inferred from their affiliations. The unit of analysis is the article-discipline pair. Of the contribution instances, the share of LIS is one third. Computer Science contributes one fifth and Business and Economics one sixth. The latter disciplines dominate the contributions in information retrieval, information seeking, and scientific communication indicating strong influences in LIS. Correspondence analysis reveals three clusters of research, one focusing on traditional LIS with contributions from LIS and Humanities and survey-type research; another on information retrieval with contributions from Computer Science and experimental research; and the third on scientific communication with contributions from Natural Sciences and Medicine and citation analytic research. The strong differentiation of scholarly contributions in LIS hints to the fragmentation of LIS as a discipline. publishedVersion

Cartograms are popular for visualizing numerical data for administrative regions in thematic maps. When there are multiple data values per region (over time or from different datasets) shown as animated or juxtaposed cartograms, preserving the viewer's mental map in terms of stability between multiple cartograms is another important criterion alongside traditional cartogram criteria such as maintaining adjacencies. We present a method to compute stable stable Demers cartograms, where each region is shown as a square scaled proportionally to the given numerical data and similar data yield similar cartograms. We enforce orthogonal separation constraints using linear programming, and measure quality in terms of keeping adjacent regions close (cartogram quality) and using similar positions for a region between the different data values (stability). Our method guarantees the ability to connect most lost adjacencies with minimal-length planar orthogonal polylines. Experiments show that our method yields good quality and stability on multiple quality criteria. publishedVersion

Publisher Copyright: Copyright: © 2023 Passmore et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For a single species, human kinship organization is both remarkably diverse and strikingly organized. Kinship terminology is the structured vocabulary used to classify, refer to, and address relatives and family. Diversity in kinship terminology has been analyzed by anthropologists for over 150 years, although recurrent patterning across cultures remains incompletely explained. Despite the wealth of kinship data in the anthropological record, comparative studies of kinship terminology are hindered by data accessibility. Here we present Kinbank, a new database of 210,903 kinterms from a global sample of 1,229 spoken languages. Using open-access and transparent data provenance, Kinbank offers an extensible resource for kinship terminology, enabling researchers to explore the rich diversity of human family organization and to test longstanding hypotheses about the origins and drivers of recurrent patterns. We illustrate our contribution with two examples. We demonstrate strong gender bias in the phonological structure of parent terms across 1,022 languages, and we show that there is no evidence for a coevolutionary relationship between cross-cousin marriage and bifurcate-merging terminology in Bantu languages. Analysing kinship data is notoriously challenging; Kinbank aims to eliminate data accessibility issues from that challenge and provide a platform to build an interdisciplinary understanding of kinship.

World Health Organization's (WHO) emergency learning platform OpenWHO provided by Hasso Plattner Institut (HPI) delivered online learning in real-time and in multiple languages during the COVID-19 pandemic. The challenge was to move from manual transcription and translation to automated to increase the speed and quantity of materials and languages available. TransPipe tool was introduced to facilitate this task. We describe the TransPipe development, analyze its functioning and report key results achieved. TransPipe successfully connects existing services and provides a suitable workflow to create and maintain video subtitles in different languages. By the end of 2022, the tool transcribed nearly 4,700 minutes of video content and translated 1,050,700 characters of video subtitles. Automated transcription and translation have enormous potential as a public health learning tool, allowing the near-simultaneous availability of video subtitles on OpenWHO in many languages, thus improving the usability of the learning materials in multiple languages for wider audiences. publishedVersion

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

Language label tokens are often used in multilingual neural language modeling and sequence-to-sequence learning to enhance the performance of such models. An additional product of the technique is that the models learn representations of the language tokens, which in turn reflect the relationships between the languages. In this paper, we study the learned representations of dialects produced by neural dialect-to-standard normalization models. We use two large datasets of typologically different languages, namely Finnish and Norwegian, and evaluate the learned representations against traditional dialect divisions of both languages. We find that the inferred dialect embeddings correlate well with the traditional dialects. The methodology could be further used in noisier settings to find new insights into language variation.

This paper presents Murreviikko, a dataset of dialectal Finnish tweets which have been dialectologically annotated and manually normalized to a standard form. The dataset can be used as a test set for dialect identification and dialect-to-standard normalization, for instance. We evaluate the dataset on the normalization task, comparing an existing normalization model built on a spoken dialect corpus and three newly trained models with different architectures. We find that there are significant differences in normalization difficulty between the dialects, and that a character-level statistical machine translation model performs best on the Murreviikko tweet dataset.

We present lemmatization experiments on the unstandardized low-resourced languages Low Saxon and Occitan using two machine-learning-based approaches represented by MaChAmp and Stanza. We show different ways to increase training data by leveraging historical corpora, small amounts of gold data and dictionary information, and discuss the usefulness of this additional data. In the results, we find some differences in the performance of the models depending on the language. This variation is likely to be partly due to differences in the corpora we used, such as the amount of internal variation. However, we also observe common tendencies, for instance that sequential models trained only on gold-annotated data often yield the best overall performance and generalize better to unknown tokens.

As the amount and complexity of software for automating heavy-duty mobile machinery is increasing, software engineering in this domain is becoming more important. To characterize the industry's current state of software engineering and its issues to guide future research, we performed an empirical exploratory study. We interviewed 16 software engineering professionals from 13 different companies conducting business in heavy-duty mobile machines and their automation. The interviews were analyzed qualitatively, and quantification of the analysis results is presented. We first create an overview of software engineering in the heavy-duty mobile machinery industry. We then identify problem areas affecting software development and discuss some of the possible solutions found in literature. Our findings indicate that the major problem areas faced in the industry that require more research are its digital transformation, autonomous machine functional safety, low availability of workforce for developing software for robotic mobile machines and the lack of established software standards. publishedVersion

This paper describes the research on the possibilities to control automatic text simplification with special tokens that allow modifying the length, paraphrasing degree, syntactic complexity, and the CEFR (Common European Framework of Reference) grade level of the output texts, i.e. the level of language proficiency a non-native speaker would need to understand them. The project is focused on Russian texts and aims to continue and broaden the existing research on controlled Russian text simplification. It is done by exploring available datasets for monolingual Russian machine translation (paraphrasing and simplification), experimenting with various model architectures, and adding control tokens that have not been used on Russian texts previously.

Understanding functions of astrocytes can be greatly enhanced by building and simulating computational models that capture their morphological details. Novel computational tools enable utilization of existing morphological data of astrocytes and building models that have appropriate level of details for specific simulation purposes. In addition to analyzing existing computational tools for constructing, transforming, and assessing astrocyte morphologies, we present here the CellRemorph toolkit implemented as an add-on for Blender, a 3D modeling platform increasingly recognized for its utility for manipulating 3D biological data. To our knowledge, CellRemorph is the first toolkit for transforming astrocyte morphologies from polygonal surface meshes into adjustable surface point clouds and vice versa, precisely selecting nanoprocesses, and slicing morphologies into segments with equal surface areas or volumes. CellRemorph is an open-source toolkit under the GNU General Public License and easily accessible via an intuitive graphical user interface. CellRemorph will be a valuable addition to other Blender add-ons, providing novel functionality that facilitates the creation of realistic astrocyte morphologies for different types of morphologically detailed simulations elucidating the role of astrocytes both in health and disease.