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Societies are complex systems, which tend to polarize into subgroups of individuals with dramatically opposite perspectives. This phenomenon is reflected—and often amplified—in online social networks, where, however, humans are no longer the only players and coexist alongside with social bots—that is, software-controlled accounts. Analyzing large-scale social data collected during the Catalan referendum for independence on October 1, 2017, consisting of nearly 4 millions Twitter posts generated by almost 1 million users, we identify the two polarized groups of Independentists and Constitutionalists and quantify the structural and emotional roles played by social bots. We show that bots act from peripheral areas of the social system to target influential humans of both groups, bombarding Independentists with violent contents, increasing their exposure to negative and inflammatory narratives, and exacerbating social conflict online. Our findings stress the importance of developing countermeasures to unmask these forms of automated social manipulation.

The field of learning has a reasonable claim of being a specified example on the way it could be improved or sustained by technology. Operating with technology for the sake of learning, users are expected to be taking longer period of time facing issues, struggling through these issues and in nearly all cases the interaction with technology is one of several effects on the achievement of success. This doesn't mean the fact that Internet and computing have had no sustainable impacts on how users learn and on the options that exist for learners. formal learning field has been experiencing a period of fast changes, and barriers between formal learning and informal one show falling away signs, partly because of changes in accessing information or alternate delivery modes. The impact technology has on pedagogy (structure or way of teaching) is complicated. There are rather few specified researches on how technological potentials and pedagogical responses to those operate to take advantage of life-long learners. In this paper a survey for last designed systems and platforms of e-learning based on open educational resource (OER) via combining evidence from research strands that are based on working in on-line and distant learning under formal settings, and in addition on open and free on-line learning that is typically of less formality. The presented study discusses numerous factors that are related to results of the instruction: the usually unpredictable learner motivations, the paths that those learners take during courses, and signs of success in formal learning and informal learning, based on each of technology and pedagogy. The results of practical activities are given for the sake of widening the access to education with using technology that indicates the fact that open education offers from alternate manners of supporting learners. Those propose an emphasis on design decisions which may be helpful in integrating learning process more thoroughly with how on-line systems are presently supporting learning and data which may be utilized for interpreting how well these designs work.

It is unknown whether screening for atrial fibrillation and subsequent treatment with anticoagulants if atrial fibrillation is detected can prevent stroke. Continuous electrocardiographic monitoring using an implantable loop recorder (ILR) can facilitate detection of asymptomatic atrial fibrillation episodes. We aimed to investigate whether atrial fibrillation screening and use of anticoagulants can prevent stroke in individuals at high risk. We did a randomised controlled trial in four centres in Denmark. We included individuals without atrial fibrillation, aged 70–90 years, with at least one additional stroke risk factor (ie, hypertension, diabetes, previous stroke, or heart failure). Participants were randomly assigned in a 1:3 ratio to ILR monitoring or usual care (control) via an online system in permuted blocks with block sizes of four or eight participants stratified according to centre. In the ILR group, anticoagulation was recommended if atrial fibrillation episodes lasted 6 min or longer. The primary outcome was time to first stroke or systemic arterial embolism. This study is registered with ClinicalTrials.gov, NCT02036450. From Jan 31, 2014, to May 17, 2016, 6205 individuals were screened for inclusion, of whom 6004 were included and randomly assigned: 1501 (25·0%) to ILR monitoring and 4503 (75·0%) to usual care. Mean age was 74·7 years (SD 4·1), 2837 (47·3%) were women, and 5444 (90·7%) had hypertension. No participants were lost to follow-up. During a median follow-up of 64·5 months (IQR 59·3–69·8), atrial fibrillation was diagnosed in 1027 participants: 477 (31·8%) of 1501 in the ILR group versus 550 (12·2%) of 4503 in the control group (hazard ratio [HR] 3·17 [95% CI 2·81–3·59]; p<0·0001). Oral anticoagulation was initiated in 1036 participants: 445 (29·7%) in the ILR group versus 591 (13·1%) in the control group (HR 2·72 [95% CI 2·41–3·08]; p<0·0001), and the primary outcome occurred in 318 participants (315 stroke, three systemic arterial embolism): 67 (4·5%) in the ILR group versus 251 (5·6%) in the control group (HR 0·80 [95% CI 0·61–1·05]; p=0·11). Major bleeding occurred in 221 participants: 65 (4·3%) in the ILR group versus 156 (3·5%) in the control group (HR 1·26 [95% CI 0·95–1·69]; p=0·11). In individuals with stroke risk factors, ILR screening resulted in a three-times increase in atrial fibrillation detection and anticoagulation initiation but no significant reduction in the risk of stroke or systemic arterial embolism. These findings might imply that not all atrial fibrillation is worth screening for, and not all screen-detected atrial fibrillation merits anticoagulation. Innovation Fund Denmark, The Research Foundation for the Capital Region of Denmark, The Danish Heart Foundation, Aalborg University Talent Management Program, Arvid Nilssons Fond, Skibsreder Per Henriksen, R og Hustrus Fond, The AFFECT-EU Consortium (EU Horizon 2020), Læge Sophus Carl Emil Friis og hustru Olga Doris Friis' Legat, and Medtronic.

In supervised learning it has been shown that a collection of weak classifiers can result in a strong classifier with error rates similar to those of more sophisticated methods. In unsupervised learning, namely in anomaly detection such a paradigm has not yet been demonstrated despite the fact that many methods have been devised as counterparts to supervised binary classifiers. This work partially fills the gap by showing that an ensemble of very weak detectors can lead to a strong anomaly detector with a performance equal to or better than state of the art methods. The simplicity of the proposed ensemble system (to be called Loda) is particularly useful in domains where a large number of samples need to be processed in real-time or in domains where the data stream is subject to concept drift and the detector needs to be updated on-line. Besides being fast and accurate, Loda is also able to operate and update itself on data with missing variables. Loda is thus practical in domains with sensor outages. Moreover, Loda can identify features in which the scrutinized sample deviates from the majority. This capability is useful when the goal is to find out what has caused the anomaly. It should be noted that none of these favorable properties increase Loda’s low time and space complexity. We compare Loda to several state of the art anomaly detectors in two settings: batch training and on-line training on data streams. The results on 36 datasets from UCI repository illustrate the strengths of the proposed system, but also provide more insight into the more general questions regarding batch-vs-on-line anomaly detection.

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.

Student engagement is an important factor for learning outcomes in higher education. Engagement with learning at campus-based higher education institutions is difficult to quantify due to the variety of forms that engagement might take (e.g. lecture attendance, self-study, usage of online/digital systems). Meanwhile, there are increasing concerns about student wellbeing within higher education, but the relationship between engagement and wellbeing is not well understood. Here we analyse results from a longitudinal survey of undergraduate students at a campus-based university in the UK, aiming to understand how engagement and wellbeing vary dynamically during an academic term. The survey included multiple dimensions of student engagement and wellbeing, with a deliberate focus on self-report measures to capture students' subjective experience. The results show a wide range of engagement with different systems and study activities, giving a broad view of student learning behaviour over time. Engagement and wellbeing vary during the term, with clear behavioural changes caused by assessments. Results indicate a positive interaction between engagement and happiness, with an unexpected negative relationship between engagement and academic outcomes. This study provides important insights into subjective aspects of the student experience and provides a contrast to the increasing focus on analysing educational processes using digital records.

This manuscript discusses the key characteristics of the Digital Ecosystems (DEs) model, which, we argue, is particularly appropriate for connecting and orchestrating the many heterogeneous and autonomous online systems, infrastructures, and platforms that constitute the bedrock of a digitally transformed society. Big Data and AI systems have enabled the implementation of the Digital Twin paradigm (introduced first in the manufacturing sector) in all the sectors of society. DEs promise to be a flexible and operative framework that allow the development of local, national, and international Digital Twins. In particular, the “Digital Twins of the Earth” may generate the actionable intelligence that is necessary to address global change challenges, facilitate the European Green transition, and contribute to realizing the UN Sustainable Development Goals (SDG) agenda. The case of the Destination Earth initiative and system is discussed in the manuscript as an example to address the broader DE concepts. In respect to the more traditional data and information infrastructural philosophy, DE solutions present important advantages as to flexibility and viability. However, designing and implementing an effective collaborative DE is far more difficult than a traditional digital system. DEs require the definition and the governance of a metasystemic level, which is not necessary for a traditional information system. The manuscript discusses the principles, patterns, and architectural viewpoints characterizing a thriving DE supporting the generation and operation of “Digital Twins of the Earth”. The conclusions present a set of conditions, best practices, and base capabilities for building a knowledge framework, which makes use of the Digital Twin paradigm and the DE approach to support decision makers with the SDG agenda implementation.

Online product reviews help consumers infer product quality, and the mean (average) rating is often used as a proxy for product quality. However, two self-selection biases, acquisition bias (mostly consumers with a favorable predisposition acquire a product and hence write a product review) and underreporting bias (consumers with extreme, either positive or negative, ratings are more likely to write reviews than consumers with moderate product ratings), render the mean rating a biased estimator of product quality, and they result in the well-known J-shaped (positively skewed, asymmetric, bimodal) distribution of online product reviews. To better understand the nature and consequences of these two self-selection biases, we analytically model and empirically investigate how these two biases originate from consumers’ purchasing and reviewing decisions, how these decisions shape the distribution of online product reviews over time, and how they affect the firm’s product pricing strategy. Our empirical results reveal that consumers do realize both self-selection biases and attempt to correct for them by using other distributional parameters of online reviews, besides the mean rating. However, consumers cannot fully account for these two self-selection biases because of bounded rationality. We also find that firms can strategically respond to these self-selection biases by adjusting their prices. Still, since consumers cannot fully correct for these two self-selection biases, product demand, the firm’s profit, and consumer surplus may all suffer from the two self-selection biases. This paper has implications for consumers to leverage online product reviews to infer true product quality, for commercial websites to improve the design of their online product review systems, and for product manufacturers to predict the success of their products.

This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.

Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LC)-mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5-10 min, depending on user experience; data processing typically takes 1-3 h, and data analysis takes â^¼30 min.