Miracle-dz/UniFAR-FTUs huggingface dataset:
An algebraic semigroup method for discovering maximal frequent itemsets

Abstract Discovering maximal frequent itemsets is an important issue and key technique in many data mining problems such as association rule mining. In the literature, generating maximal frequent itemsets proves either to be NP-hard or to have O ( l 3 4 l ( m + n ) ) O\left({l}^{3}{4}^{l}\left(m+n)) complexity in the worst case from the perspective of generating maximal complete bipartite graphs of a bipartite graph, where m m , n n are the item number and the transaction number, respectively, and l l denotes the maximum of ∣ C ∣ ∣ Ψ ( C ) ∣ / ( ∣ C ∣ + ∣ Ψ ( C ) ∣ − 1 ) | C| | \Psi \left(C)| \hspace{0.1em}\text{/}\hspace{0.1em}\left(| C| +| \Psi \left(C)| -1) , with the maximum taken over all maximal frequent itemsets C C . In this article, we put forward a method for discovering maximal frequent itemsets, whose complexity is O ( 3 m n 2 β + 4 β n ) O\left(3mn{2}^{\beta }+{4}^{\beta }n) , lower than the known complexity both in the worst case, from the perspective of semigroup algebra, where β \beta is the number of items whose support is more than the minimum support threshold. Experiments also show that an algorithm based on the algebraic method performs better than the other three well-known algorithms. Meanwhile, we explore some algebraic properties with respect to items and transactions, prove that the maximal frequent itemsets are exactly the simplified generators of frequent itemsets, give a necessary and sufficient condition for a maximal i + 1 i+1 -frequent itemset being a subset of a closed i i -frequent itemset, and provide a recurrence formula of maximal frequent itemsets.
Impact of Annotator Demographics on Sentiment Dataset Labeling

As machine learning methods become more powerful and capture more nuances of human behavior, biases in the dataset can shape what the model learns and is evaluated on. This paper explores and attempts to quantify the uncertainties and biases due to annotator demographics when creating sentiment analysis datasets. We ask >1000 crowdworkers to provide their demographic information and annotations for multimodal sentiment data and its component modalities. We show that demographic differences among annotators impute a significant effect on their ratings, and that these effects also occur in each component modality. We compare predictions of different state-of-the-art multimodal machine learning algorithms against annotations provided by different demographic groups, and find that changing annotator demographics can cause >4.5 in accuracy difference when determining positive versus negative sentiment. Our findings underscore the importance of accounting for crowdworker attributes, such as demographics, when building datasets, evaluating algorithms, and interpreting results for sentiment analysis.
"You Just Assume It Is In There, I Guess": Understanding UK Families' Application and Knowledge of Smart Home Cyber Security

The Internet of Things (IoT) is increasingly present in many family homes, yet it is unclear precisely how well families understand the cyber security threats and risks of using such devices, and how possible it is for them to educate themselves on these topics. Using a survey of 553 parents and interviews with 25 families in the UK, we find that families do not consider home IoT devices to be significantly different in terms of threats than more traditional home computers, and believe the major risks to be largely mitigated through consumer protection regulation. As a result, parents focus on teaching being careful with devices to prolong device life use, exposing their families to additional security risks and modeling incorrect security behaviors to their children. This is a risk for the present and also one for the future, as children are not taught about the IoT, and appropriate cyber security management of such devices, at school. We go on to suggest that steps must be taken by manufacturers and governments or appropriate trusted institutions to improve the cyber security knowledge and behaviors of both adults and children in relation to the use of home IoT devices.
Binaural Audio in Hybrid Meetings: Effects on Speaker Identification, Comprehension, and User Experience

In 2020, we have witnessed global experimentation of remote co-working, co-learning, and co-habiting, leading to the re-emergence of a collective search for platforms and paradigms that can optimally coalesce the virtual and physical settings - what has been studied as "hybrid models". In this context, we examine the opportunities that the advances in Spatial Audio techniques can create to improve hybrid meetings. Concretely, we present a controlled study in which 84 participants used an online platform to follow six pre-recorded semi-scripted dialogues. The videos were around two minutes long and each of them simulated a piece of conversation in the physical meeting room among three actors who played the role of co-located attendees. The six videos represented six conditions: three auditive formats, (x2) once co-located attendees wore face masks, and once without masks. We compared the experiences of the participants (remote attendees) in these six conditions. Analyzing three types of data, namely, comprehension/memory test results, self-reported ratings, and eye-tracking, we have found reinforcing evidence that demonstrates the benefits of binaural audio in hybrid settings.
SIGNATURES OF PHOTON–AXION CONVERSION IN THE THERMAL SPECTRA AND POLARIZATION OF NEUTRON STARS

Conversion of photons into axions under the presence of a strong magnetic field can dim the radiation from magnetized astrophysical objects. Here we perform a detailed calculation aimed at quantifying the signatures of photon–axion conversion in the spectra, light curves, and polarization of neutron stars (NSs). We take into account the energy and angle dependence of the conversion probability and the surface thermal emission from NSs. The latter is computed from magnetized atmosphere models that include the effect of photon polarization mode conversion due to vacuum polarization. The resulting spectral models, inclusive of the general-relativistic effects of gravitational redshift and light deflection, allow us to make realistic predictions for the effects of photon to axion conversion on observed NS spectra, light curves, and polarization signals. We identify unique signatures of the conversion, such as an increase of the effective area of a hot spot as it rotates away from the observer line of sight. For a star emitting from the entire surface, the conversion produces apparent radii that are either larger or smaller (depending on axion mass and coupling strength) than the limits set by NS equations of state. For an emission region that is observed phase-on, photon–axion conversion results in an inversion of the plane of polarization with respect to the no-conversion case. While the quantitative details of the features that we identify depend on NS properties (magnetic field strength and temperature) and axion parameters, the spectral and polarization signatures induced by photon–axion conversion are distinctive enough to make NSs very interesting and promising probes of axion physics.
Thermal-magnetic noise measurement of spin-torque effects on ferromagnetic resonance in MgO-based magnetic tunnel junctions

Thermal-magnetic noise at ferromagnetic resonance (T-FMR) can be used to measure magnetic perpendicular anisotropy of nanoscale magnetic tunnel junctions (MTJs). For this purpose, T-FMR measurements were conducted with an external magnetic field up to 14 kOe applied perpendicular to the film surface of MgO-based MTJs under a dc bias. The observed frequency-field relationship suggests that a 20 A CoFeB free layer has an effective demagnetization field much smaller than the intrinsic bulk value of CoFeB, with 4πMeff=6.1±0.3 kOe. This value is consistent with the saturation field obtained from magnetometry measurements on extended films of the same CoFeB thickness. In-plane T-FMR on the other hand shows less consistent results for the effective demagnetization field, presumably due to excitations of more complex modes. These experiments suggest that the perpendicular T-FMR is preferred for quantitative magnetic characterization of nanoscale MTJs.
Collaboration Challenges and Technology Opportunities at the Intersection of Perinatal and Mental Health Journeys

Collaborative Care Programs (CCPs) integrate mental health services into primary care settings to help patients access much needed treatment. Technologies could increase the effectiveness of CCPs, but we know little about what collaboration challenges technologies must address in this complex clinical setting. To investigate these challenges and technology opportunities, we conducted interviews and contextual inquiries with 30 patients and providers in an obstetric CCP. Using the Parallel Journeys Framework as a lens, we uncover new collaboration challenges (e.g., weighing risks and benefits of treatment, conflicting opinions and ambiguous responsibilities) at the intersection of patients' obstetric and psychosocial care journeys. We discuss new CSCW implications and technology opportunities, such as the importance of addressing support gaps in cyclical experiences, and the need to resolve provider conflicts to refocus on patient needs. These contributions inform how technologies can support patient engagement and collaboration with providers to access and receive treatment, as well as improve health outcomes.
Thread With Caution: Proactively Helping Users Assess and Deescalate Tension in Their Online Discussions

Incivility remains a major challenge for online discussion platforms, to such an extent that even conversations between well-intentioned users can often derail into uncivil behavior. Traditionally, platforms have relied on moderators to---with or without algorithmic assistance---take corrective actions such as removing comments or banning users. In this work we propose a complementary paradigm that directly empowers users by proactively enhancing their awareness about existing tension in the conversation they are engaging in and actively guides them as they are drafting their replies to avoid further escalation. As a proof of concept for this paradigm, we design an algorithmic tool that provides such proactive information directly to users, and conduct a user study in a popular discussion platform. Through a mixed methods approach combining surveys with a randomized controlled experiment, we uncover qualitative and quantitative insights regarding how the participants utilize and react to this information. Most participants report finding this proactive paradigm valuable, noting that it helps them to identify tension that they may have otherwise missed and prompts them to further reflect on their own replies and to revise them. These effects are corroborated by a comparison of how the participants draft their reply when our tool warns them that their conversation is at risk of derailing into uncivil behavior versus in a control condition where the tool is disabled.These preliminary findings highlight the potential of this user-centered paradigm and point to concrete directions for future implementations.
From 'Friends with Benefits' to 'Sextortion:' A Nuanced Investigation of Adolescents' Online Sexual Risk Experiences

Sexual exploration is a natural part of adolescent development; yet, unmediated internet access has enabled teens to engage in a wider variety of potentially riskier sexual interactions than previous generations, from normatively appropriate sexual interactions to sexually abusive situations. Teens have turned to online peer support platforms to disclose and seek support about these experiences. Therefore, we analyzed posts (N=45,955) made by adolescents (ages 13--17) on an online peer support platform to deeply examine their online sexual risk experiences. By applying a mixed methods approach, we 1) accurately (average of AUC = 0.90) identified posts that contained teen disclosures about online sexual risk experiences and classified the posts based on level of consent (i.e., consensual, non-consensual, sexual abuse) and relationship type (i.e., stranger, dating/friend, family) between the teen and the person in which they shared the sexual experience, 2) detected statistically significant differences in the proportions of posts based on these dimensions, and 3) further unpacked the nuance in how these online sexual risk experiences were typically characterized in the posts. Teens were significantly more likely to engage in consensual sexting with friends/dating partners; unwanted solicitations were more likely from strangers and sexual abuse was more likely when a family member was involved. We contribute to the HCI and CSCW literature around youth online sexual risk experiences by moving beyond the false dichotomy of "safe" versus "risky". Our work provides a deeper understanding of technology-mediated adolescent sexual behaviors from the perspectives of sexual well-being, risk detection, and the prevention of online sexual violence toward youth.
RAPID COOLING OF THE NEUTRON STAR IN CASSIOPEIA A AND r-MODE DAMPING IN THE CORE

We proposed an alternative explanation to the rapid cooling of neutron star in Cas A. It is suggested that the star experiences the recovery period following the r-mode heating process assuming the star is differentially rotating. Like the neutron-superfluidity-triggering model, our model predicts that the rapid cooling will continue for several decades. However, the behavior of the two models has slight differences, and they might be distinguished by observations in the near future.
Our Browser Extension Lets Readers Change the Headlines on News Articles, and You Won't Believe What They Did!

Headlines play a critical role in how users perceive articles. But many headline publishers craft headlines in ways that either attract clicks in an attempt to earn ad revenue, or misinform users or manipulate their opinions for malicious intents. Such headlines can do harm since many users simply skim and share headlines without reading the articles in full. We present an exploratory browser extension that empowers users to suggest headlines they deem better for news articles. Users can view headlines suggested by other users that they follow as they browse websites. We conducted a study of 27 users who used the extension for one week to read news and suggest headlines. We found that users saw value in the tool and used it to change headlines that they found in need of improvement. We characterize the changes that people make to headlines if enabled. We also report on a followup study we conducted with 312 participants to evaluate headlines suggested by the tool. The purpose of the study was to examine whether headlines suggested by untrained users could be preferred over original headlines by professional editors. We found that a substantial number of the suggested headlines were indeed preferred. Our work explores the designs for, and opportunities and consequences of, empowering news consumers by giving them control over the content curation process.
"Money Doesn't Buy You Happiness": Negative Consequences of Using the Freemium Model for Mental Health Apps

As global rates of anxiety and depression increase, we observe millions of downloads of mobile apps addressing mental health that adopt 'freemium' charging models offering complex combinations of free and paid features. We explore potentially negative outcomes of deploying such freemium designs to vulnerable MH populations. We assess outcomes for 41 frequently downloaded mental health apps, by combining thematic analysis of 41K user reviews with audits to validate app descriptions and reviews. We propose a new analytic framework identifying three types of negative outcomes for freemium deployments: first vulnerable users currently experiencing crises may feel pressure to download unhelpful or costly apps. Second unintuitive descriptions of complex apps can lead to inappropriate treatments and expensive subscriptions. Third, limited duration offers may result in incomplete treatments or unexpected charges. We discuss ethical considerations and broader HCI consequences. We describe implications for mental health app design, including the need for greater transparency around free versus paid features, and propose new approaches to provide vulnerable users with validated information about these commonly downloaded apps.
"I Never Imagined Grandma Could Do So Well with Technology"

Older adults' technology learning is a long-term process, during which family members often play significant roles. Although much research has emphasized how family support is important, little research has dove into the evolution of family dynamics when older adults are learning to use new technology. Drawing on the results from a qualitative study that performed semi-structured interviews with 20 older adults and 18 younger adults in China, we unpack how family members were involved in technology learning over time. Our findings suggest that younger family members play transformative roles throughout older adults' learning stages, i.e., as influencers, supporters, protectors, and monitors. Younger family members' roles co-evolve with not only older adults' changing needs but also their perceptions of older adults' learning abilities and online behaviors. They may struggle to adjust their teaching strategies to accommodate older adults' needs and abilities during the process. They may also worry about older adults' online benefits and safety as many older adults become far more active online than anticipated. Challenges while teaching and tensions regarding protection may thus emerge during the support process. With these findings, we suggest that older adults' technology learning should be treated as a collaborative activity with family members rather than an activity they pursue alone. We also highlight older adults' technology learning as a recurrent, dynamic, and evolving process, and call attention to the unique culture of "xiaoshun" in China that acts as a buffer to the burdens and tensions found with family support.
Ethical Tensions in Applications of AI for Addressing Human Trafficking: A Human Rights Perspective

In the last two decades, human trafficking (where individuals are forcibly exploited for the profits of another) has seen increased attention from the artificial intelligence (AI) community. Clear focus on the ethical risks of this research is critical given that those risks are disproportionately born by already vulnerable populations. To understand and subsequently address these risks, we conducted a systematic literature review of computing research leveraging AI to combat human trafficking and apply a framework using principles from international human rights law to categorize ethical risks. This paper uncovers a number of ethical tensions including bias endemic in datasets, privacy risks stemming from data collection and reporting, and issues concerning potential misuse. We conclude by highlighting four suggestions for future research: broader use of participatory design; engaging with other forms of trafficking; developing best practices for harm prevention; and including transparent ethics disclosures in research. We find that there are significant gaps in what aspects of human trafficking researchers have focused on. Most research to date focuses on aiding criminal investigations in cases of sex trafficking, but more work is needed to support other anti-trafficking activities like supporting survivors, adequately address labor trafficking, and support more diverse survivor populations including transgender and nonbinary individuals.
Care Frictions: A Critical Reframing of Patient Noncompliance in Health Technology Design

Patient work encompasses a challenging set of activities necessary for learning about and managing chronic conditions over time. Many patient-centered health technology interventions focus on supporting types of patient work, such as symptom tracking, medication adherence, and information sharing between patients and providers. However, people may not always follow, or may actively resist, the activities prescribed by their formal patient role. In this paper, we present three case studies about patients with different chronic conditions to critically reflect on the types of patient behavior commonly taken up in health technology design as acts of "noncompliance. " Detailing conflicts that emerge when patients are caught between meeting their personal needs and following clinical best practices, we show how everyday life and health system goals are often misaligned in ways that can't be easily reconciled through current design approaches. As a way forward, we argue for alternative ways of understanding the tensions routinely shaping people's healthcare experiences. We introduce the term care frictions as a sensitizing concept useful for helping designers reframe "noncompliant" behaviors as legitimate forms of patient work. Our paper also offers design considerations-both on challenges and generative possibilities-for future CSCW research seeking to support a wider breadth of patient behavior. In this, we call attention to the value of designer and researcher reflexivity in making visible the problematic assumptions in health technology design that can lead to social and emotional patient harms.
The Impact of Governance Bots on Sense of Virtual Community

Bots are increasingly being used for governance-related purposes in online communities, yet no instrumentation exists for measuring how users assess their beneficial or detrimental impacts. In order to support future human-centered and community-based research, we developed a new scale called GOVernance Bots in Online communiTies (GOV-BOTs) across two rounds of surveys on Reddit (N=820). We applied rigorous psychometric criteria to demonstrate the validity of GOV-BOTs, which contains two subscales: bot governance (4 items) and bot tensions (3 items). Whereas humans have historically expected communities to be composed entirely of humans, the social participation of bots as non-human agents now raises fundamental questions about psychological, philosophical, and ethical implications. Addressing psychological impacts, our data show that perceptions of effective bot governance positively contribute to users' sense of virtual community (SOVC), whereas perceived bot tensions may only impact SOVC if users are more aware of bots. Finally, we show that users tend to experience the greatest SOVC across groups of subreddits, rather than individual subreddits, suggesting that future research should carefully re-consider uses and operationalizations of the term "community. "
"We're so much more than the in-game clan": Gaming Experiences and Group Management in Multi-Space Online Communities

The platforms that host online gaming groups and communities continue to evolve, and it has become possible to join, participate in, and consume content from groups that exist across multiple tools, platforms, and spaces at the same time. In this paper, we explore how groups use and rely upon assemblages of multiple online spaces to accomplish the "work" of participating in these gaming groups. We present an interview study with users of the100.io, a platform that hosts gaming community spaces, helps players find groups, and operates as a gaming event scheduling tool for its users. Contrary to our initial assumptions, we found that users relied upon the100 as a kind of glue for flexibly-interconnected, multi-space group configurations. These multi-space groups support our participants' desires to approach online gaming as a social practice, provide additional accountability among players, and enable multiple forms of social participation within those communities. Our findings point towards opportunities to expand social computing scholarship to better describe how users of online communities flexibly bridge across technical infrastructure.