Reviewerly/exHarmony huggingface dataset:
Topological Networks Using a Sequential Method

The paper shares preliminary results of a novel sequential method to expand existing topology-based generative design.The approach is applied to building an interactive community design system based on a mobile interface.In the process of building an interactive design system, one of the core problems is to harness the complex topological network formed by user demands.After decades of graph theory research in architecture, a consensus on self-organized complex networks has emerged.However, how to convert input complex topological data into spatial layouts in generative designs is still a diffi cult problem worth exploring.The paper proposes a way to simplify the problem: in some cases, the spatial network of buildings can be approximated as a collection of sequences based on circulation analysis.In the process of network serialization, the personalized user demands are transformed into activity patterns and further into serial spaces.This network operation gives architects more room to play with their work.Rather than just designing an algorithm that directly translates users' demands into shape, architects can be more actively involved in organizing spatial networks by setting up a catalogue of activity patterns of the residents, thus contributing to a certain balance of top-down order and bottom-up richness in the project.The research on data serialization lays a solid foundation for the future exploration of Recurrent Neural Network (RNN) applied to generative design.
How Machines Learn to Plan

This paper strives to interrogate the abilities of machine vision techniques based on a family of deep neural networks, called generative adversarial neural networks (GANs), to devise alternative planning solutions.The basis for these processes is a large database of existing planning solutions.For the experimental setup of this paper, these plans were divided into two separate learning classes: Modern and Baroque.The proposed algorithmic technique leverages the large amount of structural and symbolic information that is inherent to the design of planning solutions throughout history to generate novel unseen plans.In this area of inquiry, aspects of culture such as creativity, agency, and authorship are discussed, as neural networks can conceive solutions currently alien to designers.These can range from alien morphologies to advanced programmatic solutions.This paper is primarily interested in interrogating the second existing but uncharted territory.
ELAbot

This paper presents the design, control system, and elastic behavior of ELAbot: a robotic bending active textile hybrid (BATH) structure that can self-form and transform.In BATH structures, equilibrium emerges from interaction between tensile (form active) and elastically bent (bending active) elements (Ahlquist and Menges 2013;Lienhard et al. 2012).The integration of a BATH structure with a robotic actuation system that controls global deformations enables the structure to self-deploy and achieve multiple three-dimensional states.Continuous elastic material actuation is embedded within an adaptive cyber-physical network, creating a novel robotic architectural system capable of behaving autonomously.State-of-the-art BATH research demonstrates their structural efficiency, aesthetic qualities, and potential for use in innovative architectural structures (Suzuki and Knippers 2018).Due to the lack of appropriate motor-control strategies that exert dynamic loading deformations safely over time, research in this field has focused predominantly on static structures.Given the complexity of controlling the material behavior of nonlinear kinetic elastic systems at an architectural scale, this research focuses on the development of a cyber-physical design framework where physical elastic behavior is integrated into a computational design process, allowing the control of large deformations.This enables the system to respond to conditions that could be difficult to predict in advance and to adapt to multiple circumstances.Within this framework, control values are computed through continuous negotiation between exteroceptive and interoceptive information, and user/designer interaction.
The Use of a Stable Super-Resolution Generative Adversarial Network (SSRGAN) on Remote Sensing Images

Because of the complicated imaging conditions in space and the finite imaging systems on satellites, the resolution of remote sensing images is limited. The process of increasing an image’s resolution, image super-resolution, aims to obtain a clearer image. High-resolution (HR) images are affected by various input conditions, such as motion, imaging blur, down-sampling matrix, and various types of noise. Changes in these conditions seriously affect low-resolution (LR) images, so if the imaging process is a pathological problem, super-resolution reconstruction is a pathological anti-problem. To optimize the imaging quality of satellites without changing the optical system, we chose to reconstruct images acquired by satellites using deep learning. We changed the original super-resolution generative adversarial nets network, upgraded the generator’s network part to ResNet-50, and inserted an additional fully connected (FC) layer in the network of the discriminator part. We also modified the loss function by changing the weight of regularization loss from 2 × 10−8 to 2 × 10−9, aiming to preserve more detail. In addition, we carefully and specifically chose remote sensing images taken under low-light circumstances from GF-5 satellites to form a new dataset for training and validation. The test results proved that our method can obtain good results. The reconstruction peak signal-to-noise ratio (PSNR) at the scaling factors of 2, 3, and 4 reached 32.6847, 31.8191, and 30.5095 dB, respectively, and the corresponding structural similarity (SSIM) reached 0.8962, 0.8434, and 0.8124. The super-resolution speed was also satisfactory, making real-time reconstruction more probable.
If Cybersecurity Was… Pizza? A Visual and Rhetorical Approach to Exploring the Concept of Cybersecurity

ABSTRACT Understanding and defining cybersecurity‐related concepts for the general public can often prove challenging due to their inherent complexity; consequently, visual imaginary emerges as an effective device for articulating and elucidating these abstract ideas. In this study, we leveraged an adapted version of the existing iSquare approach to explore people's understanding of cybersecurity. We collected 499 iSquares from a broad‐spectrum backgrounds of participants attending a prominent international cybersecurity exhibition in Taiwan. Our preliminary findings revealed that metaphors of physical security and warfare were the most frequently employed in participants' conceptualizations of cybersecurity. Participants' work roles were also observed to influence their respective attitudes and perspectives as expressed in their pictorial representations of cybersecurity.
Humanities Scholars' Understanding of Data and the Implications for Humanities Data Curation

ABSTRACT This study addresses the need for a shared understanding of humanities data to enhance data curation. Through interviews with 27 scholars, it identifies two ways scholars conceptualize data ‐ by format or role in research. It highlights three unique aspects: diverse requirements of materiality and processing levels, significance of authorship and perspective, and the dual role of tertiary sources. The study suggests prioritizing provenance, facilitating data documentation, curating tertiary sources for wider use, and establishing scholarly communication mechanisms for effective data curation.
Teenagers and Videoconference Fatigue: A Preliminary Analysis from an Affordance‐based Approach

ABSTRACT The Covid‐19 pandemic has led to the widespread use of videoconference (VC) technologies, particularly in education and this trend is projected to continue. Prolonged VC usage can lead to “videoconference fatigue” (VCF). While research on factors contributing to VCF has been conducted among university students, there is limited research on younger students (especially teenagers). To fill this gap, this study adopts an affordance‐based approach to identify VC affordances (resources and constraints) contributing to VCF in teenagers. Specifically, the objectives are to examine if (a) VC affordances (visibility, information, and availability), and (b) demographic profiles (age and gender) have effects on VCF among teenagers. A large‐scale survey was conducted and responses from 491 teenagers were analyzed using Hierarchical Regression Analysis. Results indicate that the three identified VC affordances contribute to VCF in teenagers. In addition, teenage girls and older teenagers are more prone to VCF.
Toward <scp>Evidence‐Based</scp> Cataloging Ethics: Research, Practice and Training in Knowledge Organization

ABSTRACT This panel considers the bridge between research and practice in cataloging ethics. Cataloging ethics – including indexing and classification – is an important part of practice, yet cataloging ethics research and practice are not always clearly connected. The purpose of this panel is to build towards more evidence‐based cataloging ethics practice. Two main areas will be considered. The Cataloging Code of Ethics (2021) is a vital part of these discussions: this major codification of cataloging ethics was the result of both practitioner input and much research. This panel will discuss ways in which the Code can lead to more research‐informed practices. Teaching and training is a crucial – and under‐discussed – aspect of cataloging ethics, both within library and information science education and workplace training. Therefore, the panel will contemplate how training and teaching can germinate research‐based practices. The panel will be in three parts: a panel presentation about cataloging ethics, including each member's perspectives and experiences on teaching and training in cataloging ethics; small group discussions about real world cataloging ethics scenarios, utilizing the Code to generate discussion; and feedback to the whole group with a closing discussion about strengthening the relationship between practice and research in cataloging ethics.
Spontaneous Learning Environments: Manipulating Readability &amp; Cohesion in Support of Searching as Learning

ABSTRACT In this concept paper, we make the case that variables related to reading and comprehension are relevant to the design of searching as learning environments. We propose that measures of cohesion – the lexical and grammatical connectedness within and between texts – be used as signals in retrieval and ranking algorithms for such environments, as cohesion is an important factor in text comprehension and learning. In illustrating this concept, we introduce a use case for learning‐oriented search in which the task is to retrieve a multi‐document set that functions as a spontaneous learning environment . For this task, features of the document set as a whole are important in addition to features of individual documents. In this paper, we focus on the goals of achieving a mid‐range level of readability and cohesion across a set of texts in order to balance comprehensibility with challenge and stimulation.
Exploring Laypeople's Engagement with <scp>AI</scp> Painting: A Preliminary Investigation into <scp>Human‐AI</scp> Collaboration

ABSTRACT As advancements in artificial intelligence (AI) technology have opened new avenues for artistic expression and creation, human‐AI collaboration in creative activities has garnered increasing attention. Through semi‐structured interviews with 15 participants, we investigate the intrinsic and extrinsic factors that motivate laypeople's engagement with AI painting, as well as challenges and concerns faced by users. Our findings reveal that laypeople engage with AI painting for emotional needs like entertainment, aesthetics, surprise, and curiosity, personal utilitarian needs such as self‐expression and customization, and social interaction through sharing and communication. Despite the appeal of novelty and unpredictability they also encountered challenges related to technical and system functionality, personal and environmental factors, as well as concerns about algorithm bias, pornography misuse, employment risks, copyright disputes and ethical implications. Findings provide preliminary evidence of the potential and limitations of AI in democratizing creative activities, and offer implications for designing and developing of AI assistance tools.
Are We on the Same Page about Data Sharing? A Bibliometric Comparison Between Biomedicine and Information Science Literature

ABSTRACT Data sharing has been an extensively discussed subject in both the information science and biomedicine communities. However, the two communities do not seem to speak to each other. Such an absence of exchange of perspectives can lead to siloed knowledge, duplicated work, and hindered collaboration. To uncover existing knowledge gaps, this poster depicts the bibliometric relationships between relevant literature in the fields of information science and library science (ISLS) and biomedicine and health sciences (BMHS). The findings demonstrate stronger bibliographic couplings within ISLS publications and between ISLS and multidisciplinary science publications, compared to the BMHS group. In addition, the examined ISLS and BMHS publications exhibit distinct topical foci on open science. This poster is expected to serve as a first step toward stimulating more conversations in the information science community, as well as bringing the discussions to the biomedicine community in the near future.
Routine citizen Internet practices and cyber victimization: a state-wide study in Virginia

Cybercrime is a major concern across the world, in the US and in the state of Virginia.A better understanding of cybercrime activity is needed to target and prevent it more effectively, minimize its consequences, and provide support for victims.Research on cybercrime victimization has exploded in the past few years, but much of it relies on convenience samples and is largely descriptive in nature.The research presented here involves the collection of data from a large sample of Virginia households in 2022 (n=1,206).The data are analyzed to provide a partial test of routine activity theory to better understand fraud and theft via the internet.The data provide a solid baseline for describing the extent of cyber victimization across the state.Bivariate and multivariate analyses (logistic regressions) show support for routine activity theory and provide important insights for future research.In particular, dark web exploration, posting personal information, buying and selling things online, among other activities seem to better predict unique forms of cybervictimization than other activities or simply the length of time spent on the internet.We conclude that to better assess cybercrime victimization and engagement, better measurement and longitudinal panel data will be needed.
Investigating Academic <scp>Graph‐Based</scp> Factors behind Funding Success in National Institutes of Health

ABSTRACT While major funding agencies are striving for diversity and fairness, the mechanisms behind funding success have yet to be fully elucidated. Existing studies reveal valuable evidences about the effect of the applicant's individual attributes, e.g., gender and age, on the funding success. However, the relationship between funding success and academic activities, e.g., collaborator's characteristics, remains underexplored. This work collects massive scholarly data from open academic graphs and public data about National Institutes of Health awards to investigate the effect of various academic graph‐based factors on the “K to R” success. Leveraging a heterogeneous graph model for predicting the “K to R” success, we regard the gain in the model performance of a factor as a proxy variable for the magnitude of its effect on the “K to R” success. Our preliminary results suggest that interest by peers in the applicant's research and the timing of the interest are strongly correlated with the outcome. Meanwhile, the applicant's social connections, e.g., their collaborators, can also contribute to the outcome.
“Garbage Bags Full of Files”: Exploring Sociotechnical Perceptions of Formats within the Recovery and Reuse of Scientific Data

ABSTRACT This paper explores the social and technical perceptions of physical and digital formats as they relate to work in the recovery and reuse of scientific data, specifically historical, archival, and defunct data sources. Proprietary and obsolete formats, or formats that need significant transformation work, stand out as central challenges for scientists and data curators who are recovering reusable data from archival or legacy data sources. The challenges confronting data sharing and reuse of contemporary scientific data are already known to be myriad; formats often pose a major, compounding challenge to retrospective data curation research and practice. Based on 23 qualitative interviews with practitioners conducting data recovery and reuse, ranging from marine biologists to data librarians, we study how they understand, engage with, and utilize formats within their data curation work. This paper enumerates the formats deployed throughout the scientific data curation process and explores how practitioners creating and curating scientific data based on historical and archival materials encounter, make sense of, and utilize formats. The paper focuses on practitioner perceptions of formats around the following themes: how practitioners' historical relationships to certain challenging formats inform their ongoing curation practices; the importance of contexts in prioritizing or ignoring formats within scientific curation work; and how formats reveal larger sociotechnical issues. The paper concludes by with practical and theoretical implications of navigating formats within the recovery and reuse of scientific data and offers suggestions for reconfiguring formats within broader data curation lifecycles.
Tuning Out the Noise: Benchmarking Entity Extraction for Digitized Native American Literature

ABSTRACT Named Entity Recognition (NER), the automated identification and tagging of entities in text, is a popular natural language processing task, and has the power to transform restricted data into open datasets of entities for further research. This project benchmarks four NER models–Stanford NER, BookNLP, spaCy‐trf and RoBERTa–to identify the most accurate approach and generate an open‐access, gold‐standard dataset of human annotated entities. To meet a real‐world use case, we benchmark these models on a sample dataset of sentences from Native American authored literature, identifying edge cases and areas of improvement for future NER work.
Patterns of <scp>Community‐Based</scp> Data in the <scp>US State‐Level COVID</scp>‐19 Dashboards: Groupings, Inconsistencies and Gaps

ABSTRACT In this poster, we report the results of a research study examining the presence of demographic data and other community‐based data and their grouping in visualization on the COVID‐19 dashboards developed by the 50 state governments across the USA and the government of District of Columbia. It was found that while all dashboards included some level of demographic data, there is notable inconsistency in the groupings, and a very limited number of the state‐level dashboards included visualization filtering beyond the basic demographic attributes. Several dashboards included additional data grouping capabilities such as underlying health conditions, residence/business clusters, employment status, or social vulnerability index. Both the inconsistency/gaps in demographic grouping and the fact that only a handful dashboards contained further community‐based information shows the lack of awareness of state government on the importance of incorporating detailed grouping in demographic data as well as other community‐based datasets. Public health dashboards, including those reflecting emergency or crisis situations such as COVID‐19 dashboards, are in serious need to accurately, comprehensively, and inclusively represent and display the data patterns of all members of the community, especially the often overlooked and marginalized communities.
Curating for Contrarian Communities: Data Practices of Anthropogenic Climate Change Skeptics

ABSTRACT The open data movement is often touted as a sweeping strategy to democratize science, promote diverse data reuse, facilitate reproducibility, accelerate innovation, and much more. However, the potential perils of open data are seldom examined and discussed in equal measure to these promises. As we continue to invest in open data, we need to study the full spectrum of what open data facilitates in practice, which can then inform future policy and design decisions. This paper aims to address this gap by presenting an investigative digital ethnography of one contrarian community, anthropogenic climate change (ACC) skeptics, to describe how they process, analyze, preserve, and share data. Skeptics often engage in data reuse similar to conventional data reusers, albeit for unconventional purposes and with varying degrees of trust and expertise. The data practices of ACC skeptics challenge the assumption that open data is universally beneficial. These findings carry implications for data repositories and how they might curate data and design databases with this type of reuse in mind.
Online Incivility and Contextual Factors: <scp>Data‐Driven</scp> Detection and Analysis

ABSTRACT Uncivil behaviors like rude or hate speech have been a persistent problem on social media, which could lead to negative user experience or even affect the psychological well‐being of users. Automatic detection and moderation of such behaviors are critical to creating a supportive online community for effective user communication and positive user experience. In this tutorial, we propose methods to study online incivility, which includes data collection from a social media platform, i.e., Reddit, automatic detection of incivility with pretrained deep learning classifiers, and statistical and visual analytical methods to investigate the combination of community characteristics and users’ interactive patterns that relate to the occurrences of incivility. Similar methods can be applied to understand other information misbehaviors online, such as misinformation, dissemination of rumors, and cyberstalking. This panel is sponsored by SIG SM.
Making Sense of Responders' Evaluations of Question Answerability from Academic Q&amp;A Sites

ABSTRACT In academic Q&amp;A, question cues are characteristics of natural language for responders to understand askers' needs. However, knowledge is limited concerning responders' answerability evaluations which could be influenced by question cues (e.g., emotional expressiveness, complexity) and topic types. To address the gap, this research conducted an experiment to investigate how different question cues and topic types influence evaluations of question answerability. Sentiment analyses were conducted to assess responders' evaluations of question answerability. Results showed that responders favored answering complex questions rather than simple ones. Responders also held more divergent opinions regarding whether to answer STEM questions with different cues while holding a more inclusive answerability evaluation of non‐STEM questions.