Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Emerging Obfuscation Technologies in Visual Description Services for Blind and Low Vision People

Blind and low vision people use visual description services (VDS) to gain visual interpretation and build access in a world that privileges sight. Despite their many benefits, VDS have many harmful privacy and security implications. As a result, researchers are suggesting, exploring, and building obfuscation systems that detect and obscure private or sensitive materials. However, as obfuscation depends largely on sight to interpret outcomes, it is unknown whether Blind and low vision people would find such approaches useful. Our work aims to center the perspectives and opinions of Blind and low vision people on the potential of obfuscation to address privacy concerns in VDS. By reporting on interviews with 20 Blind and low vision people who use VDS, our findings reveal that popular research trends in obfuscation fail to capture the needs of Blind and low vision people. While obfuscation might be helpful in gaining more control, tensions around obfuscation misrecognition and confirmation are prominent. We turn to the framework of interdependence to unpack and understand obfuscation in VDS, enabling us to complicate privacy concerns, uncover the labor of Blind and low vision people, and emphasize the importance of safeguards. We provide design directions to move the trajectory of obfuscation research forward.
Mapping migration regions and their evolution from population-scale family trees: what can they tell us about cultural identities and regions today?

Using a population-scale family tree dataset, this paper proposes a study of migration regions and their evolution in the U.S. between 1789 and 1924. To extract migration events, we use the child ladder approach, which traces family moves based on changes in birthplaces of consecutive children in each individual family. We calculate a time series measure of migration rate and partition the time into optimal periods so that each period has a distinct migration network. We apply community detection to derive migration regions from each network of different periods. We map these regions and use a pair-counting measure to statistically compare the similarity of regions in consecutive time periods. Migration regions reveal the extent to which the strong regional identities we see today, and, in the past, which were rooted in migration. The North/South divide was pervasive not only in the early periods but throughout U.S. history. Migration regions are important for understanding the development of regional and national cultural forms such as music, literature, foodways, and dialects, as well as political divisions and events.
Comparing linguistic and cultural explanations for visual search strategies

Abstract Visual search studies have shown that East Asians rely more on information gathered through their extrafoveal (i.e., peripheral) vision than do Western Caucasians, who tend to rely more on information gathered using their foveal (i.e., central) vision. However, the reasons for this remain unclear. Cognitive linguists suggest that the difference is attributable linguistic variation, while cultural psychologists contend it is due to cultural factors. The current study used eye-tracking data collected during a visual search task to compare these explanations by leveraging a semantic difference against a cultural difference to determine which view best explained strategies used on the task. The task was administered to Chinese, American, and Japanese participants with a primary focus on the Chinese participants’ behaviors since the semantic difference aligned the Chinese participants with the Americans, while their cultural affiliation aligned them with the Japanese participants. The results indicated that the Chinese group aligned more closely with the American group on most measures, suggesting that semantic differences were more important than cultural affiliation on this particular task. However, there were some results that could not be accounted for by the semantic differences, suggesting that linguistic and cultural factors might affect visual search strategies concurrently.
Kinetic investigation of the extrinsic spin Hall effect induced by skew scattering

A study of the kinetics of the extrinsic spin Hall conductivity induced by skew scattering is performed using the fully microscopic kinetic spin Bloch equation approach to the (001) GaAs symmetric quantum well. In the steady state, the extrinsic spin Hall current/conductivity vanishes for the linear k dependent spin–orbit coupling and is very small for the cubic k dependent spin–orbit coupling. The spin precession induced by the Dresselhaus/Rashba spin–orbit coupling plays a very important role in the vanishing of the extrinsic spin Hall conductivity in the steady state. An in-plane spin polarization is induced by the skew scattering, with the help of the spin–orbit coupling. This spin polarization is very different from the current-induced spin polarization.
H i POWER SPECTRA AND THE TURBULENT INTERSTELLAR MEDIUM OF DWARF IRREGULAR GALAXIES

H i spatial power spectra were determined for a sample of 24 nearby dwarf irregular galaxies selected from the Local Irregulars That Trace Luminosity Extremes–The H i Nearby Galaxy Survey sample. The two-dimensional power spectral indices asymptotically become a constant for each galaxy when a significant part of the line profile is integrated. For narrow channel maps, the power spectra become shallower as the channel width decreases, and this shallowing trend continues to our single channel maps. This implies that even the highest velocity resolution of 1.8 km s−1 is not smaller than the thermal dispersion of the coolest, widespread H i component. The one-dimensional power spectra of azimuthal profiles at different radii suggest that the shallower power spectra for narrower channel width is mainly contributed by the inner disks, which indicates that the inner disks have proportionally more cooler H i than the outer disks. Galaxies with lower luminosity (MB > −14.5 mag) and star formation rate (SFR, log(SFR (M☉ yr−1)) < −2.1) tend to have steeper power spectra, which implies that the H i line-of-sight depths can be comparable with the radial length scales in low-mass galaxies. A lack of a correlation between the inertial-range spectral indices and SFR surface density implies that either non-stellar power sources are playing a fundamental role in driving the interstellar medium turbulent structure or the nonlinear development of turbulent structures has little to do with the driving sources.
MicroPress: Detecting Pressure and Hover Distance in Thumb-to-Finger Interactions

Thumb-to-finger interactions leverage the thumb for precise, eyes-free input with high sensory bandwidth. While previous research explored gestures based on touch contact and finger movement on the skin, interactions leveraging depth such as pressure and hovering input are still underinvestigated. We present MicroPress, a proof-of-concept device that can detect both, precise thumb pressure applied on the skin and hover distance between the thumb and the index finger. We rely on a wearable IMU sensor array and a bi-directional RNN deep learning approach to enable fine-grained control while preserving the natural tactile feedback and touch of the skin. We demonstrate MicroPress’ efficacy with two interactive scenarios that pose challenges for real-time input and we validate its design with a study involving eight participants. With short per-user calibration steps, MicroPress is capable of predicting hover distance with 0.57mm accuracy, and on-skin pressure with normalized pressure error at 6 locations on the index finger.
It's All Relative! A Method to Counter Human Bias in Crowdsourced Stance Detection of News Articles

Using human intelligence to identify news articles' political stances is common in research and practical applications. But human judgement can be biased and prone to errors stemming from the comprehension of tasks and political alignment. This paper proposes a relative rating method based on news articles' stances relative to raters' own stances to avoid comprehension inconsistency and to control for human bias in crowdsourced stance detection of news articles. We also show how to use the relative ratings to construct a measure for raters' stances on a political topic and to identify raters whose ratings are of higher quality than others. We implement our proposed methods in an online experiment that recruits Amazon Mechanical Turk users as raters for news articles on Gun Control. Using the data from the experiment, we find evidence that raters' own stances on Gun Control significantly impact ratings of related news articles, both at the individual levels and at the aggregate levels. We also present evidence that our relative-rating-based stance measure captures more information about raters' actual stances than their self-reported stance does.
Bifurcating Cognitive Attention from Visual Concentration: Utilizing Cooperative Audiovisual Sensing for Demarcating Inattentive Online Meeting Participants

The profuse popularity of video conferencing has led to a simultaneous rise in the opportunity for the participants to multitask. Productive multitasking, such as taking notes, browsing for relevant information, etc., can help promote the cognitive attentiveness of participants. However, existing approaches of tagging inattentive participants solely based on their visual concentration on the meeting app fail to work in such instances. This paper proposes EmotiConf -- a novel real-time framework to monitor participants' attentiveness and a non-real-time framework for visual multitask detection without explicitly relying on their visual concentration. EmotiConf utilizes an unconventional observation where the emotional states of attentive participants, captured through their facial expressions, correlate and also correspond to the vocal expression of the speaker and the intent of the speech. Accordingly, EmotiConf develops a software wrapper to tag the inattentive participants while also characterizing visual multitasking instances performed by them. A thorough evaluation of EmotiConf confirms its usability with a high score of >80.
Fingeret: A Wearable Fingerpad-Free Haptic Device for Mixed Reality

Haptic feedback to fingers is essential for enhancing the experience of human-computer interactions. In mixed-reality (MR) environments, haptic feedback to fingers should be achieved without covering the fingerpad because seamless interactions with both physical and virtual objects are required. However, previous haptic devices cannot provide a wide range of haptic feedback without interfering with physical interactions. Here, we propose a novel self-contained fingerpad-free haptic device named Fingeret, which enables a wide range of feedback and seamless interactions in MR environments. Specifically, we combined newly developed actuators for the sides of fingers, hereafter named finger side actuators (FSAs), with a conventional fingernail actuator (FNA) to obtain a wide range of haptic feedback. FSAs are suitable for relatively low-frequency haptic feedback, whereas FNA is suitable for relatively high-frequency haptic feedback. Our user study showed that the two actuators have complementary roles in covering a wide range of haptic feedback. Finally, we demonstrated an application scenario for designing molecules using an MR tool using Fingeret.
Understanding Instructors' Cultivation of Connectedness in K-12 Online Synchronous Culturally Responsive STEM and Computing Education

Culturally responsive STEM and computing initiatives aim to engage and embolden a diverse range of learners, center their identity and experiences in curriculum, and connect learners to each other and their communities. With an abrupt pivot to online learning at the beginning of 2020, more educational experiences have taken place virtually. We ran a virtual synchronous culturally responsive computing camp and saw that establishing the right environment online to support a good sense of connectedness was challenging. To investigate this further, we interviewed eight K-12 instructors of culturally responsive STEM and computing programs. Three themes emerged on defining and cultivating connectedness in learning experiences, the role of equity in supporting community online, and affordances of being online specific to culturally responsive perspectives. We support our thematic findings with vignettes from the camp data. In this study, we address K-12 culturally responsive STEM and computing instructors' beliefs, experiences, and approaches regarding cultivating connectedness online. This work fills a gap in understanding instructor perspectives on building in-program and broader community connections online from a culturally responsive STEM and computing lens.
Accessible Design is Mediated by Job Support Structures and Knowledge Gained Through Design Career Pathways

Digital designers often do not make their work accessible (e.g., websites failing criteria set by the W3C's Web Content Accessibility Guidelines), and accessible design research discusses many solutions to address this problem (e.g., teaching accessibility within university design and technical courses). However, prior research in this area typically does not acknowledge whether recommendations and resources to support accessible design are suitable for all digital designers due to different training pathways and job support structures (e.g., large-company vs. rural and self-employed designers or designers who learned their skills outside of formal education settings). We interviewed 20 digital designers from rural and urban areas, as well as working from home and remotely, to understand the challenges they experience in making accessible content within the context of their workplace. We find that job support structures mediate the effectiveness of current accessible design recommendations and resources, and we suggest how to improve accessible design support to meet the needs of under-resourced designers.
Building a Personalized Model for Social Media Textual Content Censorship

Social media users often suffer from the problem of content over-disclosure. Most existing studies attempt to solve this problem by recommending proper audiences for users when sharing content. However, the audience management strategy cannot filter out sensitive information from the post and narrow the scope of content permeation. On the contrary, this paper conducts research from the content perspective and aims to design a content censorship model to help users evaluate the publicity of a post and find the sensitive information from it. The user can revise the content accordingly to achieve goals of sensitive information protection and broader content permeation. For this intention, we first built a dataset to explore the factors related to the public level of a post and the sensitive information. Based on the findings, a novel personalized multi-task content censorship model was built using several state-of-the-art deep learning techniques such as Seq2Seq and Co-training. We also implemented a prototype, i.e. a Browser plugin-based content censorship tool, by utilizing Weibo as a research site. Our model and its prototype were evaluated through automatic and human evaluations. The automatic evaluation suggests that our model outperforms the baseline methods on several metrics including precision, recall, and F1-score. The human evaluation also reveals that our model and prototype play an important role in helping users identify sensitive information. Based on these results, we proposed several insights for the future design of the social media content censorship system.
"Give Everybody [..] a Little Bit More Equity": Content Creator Perspectives and Responses to the Algorithmic Demonetization of Content Associated with Disadvantaged Groups

Algorithmic systems help manage the governance of digital platforms featuring user-generated content, including how money is distributed to creators from the profits a platform earns from advertising on this content. However, creators producing content about disadvantaged populations have reported that these kinds of systems are biased, having associated their content with prohibited or unsafe content, leading to what creators believed were error-prone decisions to demonetize their videos. Motivated by these reports, we present the results of 20 interviews with YouTube creators and a content analysis of videos, tweets, and news about demonetization cases to understand YouTubers' perceptions of demonetization affecting videos featuring disadvantaged or vulnerable populations, as well as creator responses to demonetization, and what kinds of tools and infrastructure support they desired. We found creators had concerns about YouTube's algorithmic system stereotyping content featuring vulnerable demographics in harmful ways, for example by labeling it "unsafe'' for children or families -- creators believed these demonetization errors led to a range of economic, social, and personal harms. To provide more context to these findings, we analyzed and report on the technique a few creators used to audit YouTube's algorithms to learn what could cause the demonetization of videos featuring LGBTQ people, culture and/or social issues. In response to the varying beliefs about the causes and harms of demonetization errors, we found our interviewees wanted more reliable information and statistics about demonetization cases and errors, more control over their content and advertising, and better economic security.
Identifying correlations between LIGO’s astronomical range and auxiliary sensors using lasso regression

The range to which the Laser Interferometer Gravitational-Wave Observatory (LIGO) can observe astrophysical systems varies over time, limited by noise in the instruments and their environments. Identifying and removing the sources of noise that limit LIGO’s range enables higher signal-to-noise observations and increases the number of observations. The LIGO observatories are continuously monitored by hundreds of thousands of auxiliary channels that may contain information about these noise sources. This paper describes an algorithm that uses linear regression, namely lasso (least absolute shrinkage and selection operator) regression, to analyze all of these channels and identify a small subset of them that can be used to reconstruct variations in LIGO’s astrophysical range. Exemplary results of the application of this method to three different periods of LIGO Livingston data are presented, along with computational performance and current limitations.
Radical pair based magnetic field effects in cells: the importance of photoexcitation conditions and single cell measurements

A recent publication1 on the bioRxiv preprint server aims to replicate our observation of magnetic field effects on the autofluorescence of HeLa cells2, but is unable to reproduce the effects described in our original work. Here we examine this new study and demonstrate, based on a model of the reaction photocycle, why the differences in the measurement conditions used render the experiment unlikely to be able to observe the originally reported effect. In addition, we highlight substantial problems in the quality of the data in the replicate study and reiterate the advantages of the direct modulation, single cell measurement approach presented in the original work over a more standard statistical approach.
A Biologically Plausible Spiking Neural Network for Decoding Kinematics in the Hippocampus and Premotor Cortex

This work presents a spiking neural network for predicting kinematics from neural data towards accurate and energy-efficient brain machine interface. A brain machine interface is a technological system that interprets neural signals to allow motor impaired patients to control prosthetic devices. Spiking neural networks have the potential to improve brain machine interface technology due to their low power cost and close similarity to biological neural structures. The SNN in this study uses the leaky integrate-and-fire model to simulate the behavior of neurons, and learns using a local learning method that uses surrogate gradient to learn the parameters of the network. The network implements a novel continuous time output encoding scheme that allows for regression-based learning. The SNN is trained and tested offline on neural and kinematic data recorded from the premotor cortex of a primate and the hippocampus of a rat. The model is evaluated by finding the correlation between the predicted kinematic data and true kinematic data, and achieves peak Pearson Correlation Coefficients of 0.77 for the premotor cortex recordings and 0.80 for the hippocampus recordings. The accuracy of the model is benchmarked against a Kalman filter decoder and a LSTM network, as well as a spiking neural network trained with backpropagation to compare the effects of local learning.
Kinetic temperature of massive star-forming molecular clumps measured with formaldehyde. IV. The ALMA view of N113 and N159W in the LMC

For a general understanding of the physics involved in the star formation process, measurements of physical parameters such as temperature and density are indispensable. The chemical and physical properties of dense clumps of molecular clouds are strongly affected by the kinetic temperature. Therefore, this parameter is essential for a better understanding of the interstellar medium. Formaldehyde, a molecule which traces the entire dense molecular gas, appears to be the most reliable tracer to directly measure the gas kinetic temperature.We aim to determine the kinetic temperature with spectral lines from formaldehyde and to compare the results with those obtained from ammonia lines for a large number of massive clumps.Three 218 GHz transitions (JKAKC=303-202, 322-221, and 321-220) of para-H2CO were observed with the 15m James Clerk Maxwell Telescope (JCMT) toward 30 massive clumps of the Galactic disk at various stages of high-mass star formation. Using the RADEX non-LTE model, we derive the gas kinetic temperature modeling the measured para-H2CO 322-221/303-202and 321-220/303-202 ratios. The gas kinetic temperatures derived from the para-H2CO (321-220/303-202) line ratios range from 30 to 61 K with an average of 46 K. A comparison of kinetic temperature derived from para-H2CO, NH3, and the dust emission indicates that in many cases para-H2CO traces a similar kinetic temperature to the NH3 (2,2)/(1,1) transitions and the dust associated with the HII regions. Distinctly higher temperatures are probed by para-H2CO in the clumps associated with outflows/shocks. Kinetic temperatures obtained from para-H2CO trace turbulence to a higher degree than NH3 (2,2)/(1,1) in the massive clumps. The non-thermal velocity dispersions of para-H2CO lines are positively correlated with the gas kinetic temperature. The massive clumps are significantly influenced by supersonic non-thermal motions.