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Estimating affective polarization on a social network
Full text linkConcerns about polarization and hate speech on social media are widespread. Affective polarization, i.e., hostility among partisans, is crucial in this regard as it links political disagreements to hostile language online. However, only a few methods are available to measure how affectively polarized an online debate is, and the existing approaches do not investigate jointly two defining features of affective polarization: hostility and social distance. To address this methodological gap, we propose a network-based measure of affective polarization that combines both aspects – which allows them to be studied independently. We show that our measure accurately captures the relation between the level of disagreement and the hostility expressed towards others (affective component) and whom individuals choose to interact with or avoid (social distance component). Applying our measure to a large-scale Twitter data set on COVID-19, we find that affective polarization was low in February 2020 and increased to high levels as more users joined the Twitter discussion in the following months
SeagrassFinder: Deep learning for eelgrass detection and coverage estimation in the wild
No full textSeagrass meadows play a crucial role in marine ecosystems, providing benefits such as carbon sequestration, water quality improvement, and habitat provision. Monitoring the distribution and abundance of seagrass is essential for environmental impact assessments and conservation efforts. However, the current manual methods of analyzing underwater video data to assess seagrass coverage are time-consuming and subjective. This work explores the use of deep learning models to automate the process of seagrass detection and coverage estimation from underwater video data. We create a new dataset of over 8,300 annotated underwater images, and subsequently evaluate several deep learning architectures, including ResNet, InceptionNetV3, DenseNet, and Vision Transformer for the task of binary classification on the presence and absence of seagrass by transfer learning. The results demonstrate that deep learning models, particularly Vision Transformers, can achieve high performance in predicting eelgrass presence, with AUROC scores exceeding 0.95 on the final test dataset. The application of underwater image enhancement further improved the models’ prediction capabilities. Furthermore, we introduce a novel approach for estimating seagrass coverage from video data, showing promising preliminary results that align with expert manual labels, and indicating potential for consistent and scalable monitoring. The proposed methodology allows for the efficient processing of large volumes of video data, enabling the acquisition of much more detailed information on seagrass distributions in comparison to current manual methods. This information is crucial for environmental impact assessments and monitoring programs, as seagrasses are important indicators of coastal ecosystem health. This project demonstrates the value that deep learning can bring to the field of marine ecology and environmental monitoring
Multi-Dimensional Exploration of Media Collection Metadata
No full textThis demonstration proposes a multi-dimensional exploration of media collection metadata. Two user interfaces, a virtual reality explorer and a web-based explorer, will be used to explore three different collections of lifelog images, general video clips, and music using a variety of metadata attributes.This demonstration proposes a multi-dimensional exploration of media collection metadata. Two user interfaces, a virtual reality explorer and a web-based explorer, will be used to explore three different collections of lifelog images, general video clips, and music using a variety of metadata attributes
Distributed Computation with Local Advice
No full textAlgorithms with advice have received ample attention in the distributed and online settings, and they have recently proven useful also in dynamic settings. In this work we study local computation with advice: the goal is to solve a graph problem Π with a distributed algorithm in T(Δ) communication rounds, for some function T that only depends on the maximum degree Δ of the graph, and the key question is how many bits of advice per node are needed. Some of our results regard Locally Checkable Labeling problems (LCLs), which is an important family of problems that includes various coloring and orientation problems on finite-degree graphs. These are constraint-satisfaction graph problems that can be defined with a finite set of valid input/output-labeled neighborhoods.Our main results are: 1) Any locally checkable labeling problem can be solved with only 1 bit of advice per node in graphs with sub-exponential growth (the number of nodes within radius r is sub-exponential in r; for example, grids are such graphs). Moreover, we can make the set of nodes that carry advice bits arbitrarily sparse. As a corollary, any locally checkable labeling problem admits a locally checkable proof with 1 bit per node in graphs with sub-exponential growth. 2) The assumption of sub-exponential growth is complemented by a conditional lower bound: assuming the Exponential-Time Hypothesis, there are locally checkable labeling problems that cannot be solved in general with any constant number of bits per node. 3) In any graph we can find an almost-balanced orientation (indegrees and outdegrees differ by at most one) with 1 bit of advice per node, and again we can make the advice arbitrarily sparse. As a corollary, we can also compress an arbitrary subset of edges so that a node of degree d stores only d/2 + 2 bits, and we can decompress it locally, in T(Δ) rounds. 4) In any graph of maximum degree Δ, we can find a Δ-coloring (if it exists) with 1 bit of advice per node, and again, we can make the advice arbitrarily sparse. 5) In any 3-colorable graph, we can find a 3-coloring with 1 bit of advice per node. As a corollary, in bounded-degree graphs there is a locally checkable proof that certifies 3-colorability with 1 bit of advice per node, while prior work shows that this is not possible with a proof labeling scheme (PLS), which is a more restricted setting where the verifier can only see up to distance 1. Our work shows that for many problems the key threshold is not whether we can achieve 1 bit of advice per node, but whether we can make the advice arbitrarily sparse. To formalize this idea, we develop a general framework of composable schemas that enables us to build algorithms for local computation with advice in a modular fashion: once we have (1) a schema for solving Π₁ and (2) a schema for solving Π₂ assuming an oracle for Π₁, we can also compose them and obtain (3) a schema that solves Π₂ without the oracle. It turns out that many natural problems admit composable schemas, all of them can be solved with only 1 bit of advice, and we can make the advice arbitrarily sparse
On the evolution of agile software team work arrangements
No full textThe IT industry has undergone a significant transformation over the past years, and many companies and software teams have been experimenting with different policies and work arrangements. In this multiple-case study, we explore the evolution of organizational policies and the work arrangements of 28 agile teams, and report on their effects, based on interviews with seven individuals in leadership and support roles, from six companies. Our findings reveal the emergence of a dynamic and evolving spectrum of work arrangements and organizational policies, reflecting an increased flexibility in accommodating diverse work schedules and locations. We identified complex and interrelated impacts at the organizational, leadership, and team levels. At the organizational level, underutilized office spaces pose new challenges for resource management and strategic planning. At the leadership level, managers and team leaders reported diminished visibility and awareness of team activities under certain arrangements. At the team level, policies reshaped the physical and virtual workspace, influencing creativity, communication patterns, and coordination demands, with some arrangements requiring enhanced mechanisms for coordination. Our findings further substantiate concerns raised by both scholars and managers about the impacts of evolving organizational policies and work arrangements
Spreading Rebellion: Spanish Maritime Radicalism in the Americas, 1875–1930
No full textThe Atlantic world in the late nineteenth and early twentieth centuries was a space of contradiction and hope. From New York to Havana to Buenos Aires, Spreading Rebellion documents the activities of migrant laborers from Spain who contributed to the genesis, development, and consolidation of maritime unionism. The rise of steamships in the late nineteenth century brought a massive increase in global transport, along with a ballooning international workforce. Coexistence onboard through shared working and living conditions contributed to solidarity among seafarers, while power exercised through their mobility helped stimulate the process of organizing dockworkers on land. Alonso and Struthers focus keenly on how this multilingual and multiracial workforce imbued their movement with an anticapitalist ethos. These dynamics animated aggressive campaigns for legislative reforms, and fights for workplace control with unions such as the Industrial Workers of the World. Spreading Rebellion is, above all, a transnational history that moves away from statist frames of reference
Detecting bias in algorithms used to disseminate information in social networks and mitigating it using multiobjective optimization
No full textSocial connections are conduits through which individuals communicate, information propagates, and diseases spread. Identifying individuals who are more likely to adopt ideas and spread them is essential in order to develop effective information campaigns, maximize the reach of resources, and fight epidemics. Consequently, a lot of work has focused on identifying influencers in social networks with various influence maximization algorithms being proposed. Based on extensive computer simulations on synthetic and 10 diverse real-world social networks we show that seeding information in social networks using state-of-the-art influence maximization methods creates information gaps. Our results show that these algorithms select influencers who do not disseminate information equitably, threatening to create an increasingly unequal society. To overcome this issue, we devise a multiobjective algorithm which both maximizes influence and information equity. Our results demonstrate it is possible to reduce vulnerability at a relatively low trade-off with respect to spread. This highlights that in our search for maximizing the spread of information we do not need to compromise on information equality
The Uniqueness of IT Cost Risk: A Cross-Group Comparison of 23 Project Types
No full textThis article measures and explains cost risk for IT projects compared with 22 other project types. The null hypothesis is that IT projects are not different from other projects in terms of cost risk. The thesis is falsified. IT cost risk is found to be uniquely more risky than other project types. First, we review four extant explanations of high IT cost risk: immaturity, intangibility, goal ambiguity, and stakeholder resistance. Second, we describe data to measure cost risk across 23 project types, including IT. Third, we fit theoretical distributions to the data and test for median and tail risk, showing that tail risk dominates, with IT cost risk having a fatter tail than any other project type. Fourth, we develop a taxonomy of risk based on the Pareto 1 tail parameter α, documenting that IT is uniquely risky as the only project type with α ≤ 1, indicating infinite mean and variance and, thus, infinite and unpredictable risk. Fifth, we return to the four extant explanations and conclude that, although our data do not prove the explanations, they also do not falsify them. Based on our data, we suggest two further explanations of cost risk: Bespokeness and “think-fast” decision-making, which we argue drive extreme risk and are typical of IT, with modularity and “think slow” as antidotes. Finally, we identify areas for further research
Uncovering Anomalous Events for Marine Environmental Monitoring via Visual Anomaly Detection
No full textUnderwater video monitoring is a promising strategy for assessing marine biodiversity, but the vast volume of uneventful footage makes manual inspection highly impractical. In this work, we explore the use of visual anomaly detection (VAD) based on deep neural networks to automatically identify interesting or anomalous events. We introduce AURA, the first multi-annotator benchmark dataset for underwater VAD, and evaluate four VAD models across two marine scenes. We demonstrate the importance of robust frame selection strategies to extract meaningful video segments. Our comparison against multiple annotators reveals that VAD performance of current models varies dramatically and is highly sensitive to both the amount of training data and the variability in visual content that defines "normal" scenes. Our results highlight the value of soft and consensus labels and offer a practical approach for supporting scientific exploration and scalable biodiversity monitoring
Empowering Computing Education Researchers Through LLM-Assisted Content Analysis
No full textComputing education research (CER) is often instigated by practitioners wanting to improve both their own and the wider discipline's teaching practice. However, the latter is often difficult as many researchers lack the colleagues, resources, or capacity to conduct research that is generalisable or rigorous enough to advance the discipline. As a result, research methods that enable sense-making with larger volumes of qualitative data, while not increasing the burden on the researcher, have significant potential within CER.In this discussion paper, we propose such a method for conducting rigorous analysis on large volumes of textual data, namely a variation of LLM-assisted content analysis (LACA). This method combines content analysis with the use of large language models, empowering researchers to conduct larger-scale research which they would otherwise not be able to perform. Using a computing education dataset, we illustrate how LACA could be applied in a reproducible and rigorous manner. We believe this method has potential in CER, enabling more generalisable findings from a wider range of research. This, together with the development of similar methods, can help to advance both the practice and research quality of the CER discipline