66 research outputs found
Pika: Empowering Non-Programmers to Author Executable Governance Policies in Online Communities
Internet users have formed a wide array of online communities with nuanced
and diverse community goals and norms. However, most online platforms only
offer a limited set of governance models in their software infrastructure and
leave little room for customization. Consequently, technical proficiency
becomes a prerequisite for online communities to build governance policies in
code, excluding non-programmers from participation in designing community
governance. In this paper, we present Pika, a system that empowers
non-programmers to author a wide range of executable governance policies. At
its core, Pika incorporates a declarative language that decomposes governance
policies into modular components, thereby facilitating expressive policy
authoring through a user-friendly, form-based web interface. Our user studies
with 17 participants show that Pika can empower non-programmers to author
governance policies approximately 2.5 times faster than programmers who author
in code. We also provide insights about Pika's expressivity in supporting
diverse policies that online communities want.Comment: Under revie
Distributed Semi-supervised Fuzzy Regression with Interpolation Consistency Regularization
Recently, distributed semi-supervised learning (DSSL) algorithms have shown
their effectiveness in leveraging unlabeled samples over interconnected
networks, where agents cannot share their original data with each other and can
only communicate non-sensitive information with their neighbors. However,
existing DSSL algorithms cannot cope with data uncertainties and may suffer
from high computation and communication overhead problems. To handle these
issues, we propose a distributed semi-supervised fuzzy regression (DSFR) model
with fuzzy if-then rules and interpolation consistency regularization (ICR).
The ICR, which was proposed recently for semi-supervised problem, can force
decision boundaries to pass through sparse data areas, thus increasing model
robustness. However, its application in distributed scenarios has not been
considered yet. In this work, we proposed a distributed Fuzzy C-means (DFCM)
method and a distributed interpolation consistency regularization (DICR) built
on the well-known alternating direction method of multipliers to respectively
locate parameters in antecedent and consequent components of DSFR. Notably, the
DSFR model converges very fast since it does not involve back-propagation
procedure and is scalable to large-scale datasets benefiting from the
utilization of DFCM and DICR. Experiments results on both artificial and
real-world datasets show that the proposed DSFR model can achieve much better
performance than the state-of-the-art DSSL algorithm in terms of both loss
value and computational cost
On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions
As Federated Learning (FL) has gained increasing attention, it has become
widely acknowledged that straightforwardly applying stochastic gradient descent
(SGD) on the overall framework when learning over a sequence of tasks results
in the phenomenon known as ``catastrophic forgetting''. Consequently, much FL
research has centered on devising federated increasing learning methods to
alleviate forgetting while augmenting knowledge. On the other hand, forgetting
is not always detrimental. The selective amnesia, also known as federated
unlearning, which entails the elimination of specific knowledge, can address
privacy concerns and create additional ``space'' for acquiring new knowledge.
However, there is a scarcity of extensive surveys that encompass recent
advancements and provide a thorough examination of this issue. In this
manuscript, we present an extensive survey on the topic of knowledge editing
(augmentation/removal) in Federated Learning, with the goal of summarizing the
state-of-the-art research and expanding the perspective for various domains.
Initially, we introduce an integrated paradigm, referred to as Federated
Editable Learning (FEL), by reevaluating the entire lifecycle of FL. Secondly,
we provide a comprehensive overview of existing methods, evaluate their
position within the proposed paradigm, and emphasize the current challenges
they face. Lastly, we explore potential avenues for future research and
identify unresolved issues.Comment: 7 pages, 1 figure, 2 tabel
"Is Reporting Worth the Sacrifice of Revealing What I Have Sent?": Privacy Considerations When Reporting on End-to-End Encrypted Platforms
User reporting is an essential component of content moderation on many online
platforms -- in particular, on end-to-end encrypted (E2EE) messaging platforms
where platform operators cannot proactively inspect message contents. However,
users' privacy concerns when considering reporting may impede the effectiveness
of this strategy in regulating online harassment. In this paper, we conduct
interviews with 16 users of E2EE platforms to understand users' mental models
of how reporting works and their resultant privacy concerns and considerations
surrounding reporting. We find that users expect platforms to store rich
longitudinal reporting datasets, recognizing both their promise for better
abuse mitigation and the privacy risk that platforms may exploit or fail to
protect them. We also find that users have preconceptions about the respective
capabilities and risks of moderators at the platform versus community level --
for instance, users trust platform moderators more to not abuse their power but
think community moderators have more time to attend to reports. These
considerations, along with perceived effectiveness of reporting and how to
provide sufficient evidence while maintaining privacy, shape how users decide
whether, to whom, and how much to report. We conclude with design implications
for a more privacy-preserving reporting system on E2EE messaging platforms.Comment: accepted to SOUPS 202
A Framework for Designing Fair Ubiquitous Computing Systems
Over the past few decades, ubiquitous sensors and systems have been an
integral part of humans' everyday life. They augment human capabilities and
provide personalized experiences across diverse contexts such as healthcare,
education, and transportation. However, the widespread adoption of ubiquitous
computing has also brought forth concerns regarding fairness and equitable
treatment. As these systems can make automated decisions that impact
individuals, it is essential to ensure that they do not perpetuate biases or
discriminate against specific groups. While fairness in ubiquitous computing
has been an acknowledged concern since the 1990s, it remains understudied
within the field. To bridge this gap, we propose a framework that incorporates
fairness considerations into system design, including prioritizing stakeholder
perspectives, inclusive data collection, fairness-aware algorithms, appropriate
evaluation criteria, enhancing human engagement while addressing privacy
concerns, and interactive improvement and regular monitoring. Our framework
aims to guide the development of fair and unbiased ubiquitous computing
systems, ensuring equal treatment and positive societal impact.Comment: 8 pages, 1 figure, published in 2023 ACM International Joint
Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International
Symposium on Wearable Computin
County level study of the interaction effect of PM2.5 and climate sustainability on mortality in China
IntroductionPM2.5 and climate change are two major public health concerns, with majority of the research on their interaction focused on the synergistic effect, particularly for extreme events such as hot or cold temperatures. The climate sustainability index (CLS) was introduced to comprehensively explore the impact of climate change and the interactive effect on human health with air pollution.MethodsIn this study, a county-level panel data in China was collected and used. The generalized additive model (GAM) and geographically and temporally weighted regression (GTWR) was used to explore the interactive and spatial effect on mortality between CLS and PM2.5.Results and discussionsIndividually, when CLS is higher than 150 or lower than 50, the mortality is higher. Moreover, when PM2.5 is more than 35 μg/m3, the influence on mortality is significantly increased as PM2.5 concentration rises; when PM2.5 is above 70 μg/m3, the trend is sharp. A nonlinear antagonistic effect between CLS and PM2.5 was found in this study, proving that the combined adverse health effects of climate change and air pollution, especially when CLS was lower (below 100) and PM2.5 was higher (above 35 μg/m3), the antagonistic effect was much stronger. From a spatial perspective, the impact of CLS and PM2.5 on mortality varies in different geographical regions. A negative and positive influence of CLS and PM2.5 was found in east China, especially in the northeastern and northern regions, -which were heavily polluted. This study illustrated that climate sustainability, at certain level, could mitigate the adverse health influence of air pollution, and provided a new perspective on health risk mitigation from pollution reduction and climate adaptation
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data
Sliding-Mode Active Disturbance Rejection Control for Electromagnetic Driven Compliant Micro-Positioning Platform
At the field of nanometer positioning and machining, high-precision tracking is a key technology of the micro-positioning platform which is driven by a voice coil motor. To improve the tracking accuracy and response speed, the sliding-mode active disturbance rejection control is proposed. The mathematical model of the micro-positioning platform control system is established, in which the perturbation and spring-damping force are set as the unknown terms, and an extended state observer is used to estimate and compensate for the unknown terms. To improve the robustness of the system, the equivalent sliding-mode term is constructed to replace the PD control term in the conventional active disturbance rejection. Further, the stability of the system is proved by the Lyapunov stability theory, and compared with the conventional sliding-mode controller, the effectiveness of the proposed control strategy is verified by simulation
Design on the Control System of a Gait Rehabilitation Training Robot Based on Brain-Computer Interface and Virtual Reality Technology
In this paper a control system of a gait rehabilitation training robot based on Brain-Computer Interface (BCI) and virtual reality technology is proposed, which makes the patients' rehabilitation training process more interesting. A technique for measuring the mental states of the human and associated applications based on normal brain signals are examined and evaluated firstly. Secondly, the virtual game starts with the information from the BCI and then it runs in the form of a thread, with the singleton design pattern as the main mode. Thirdly, through the synergistic cooperation with the main software, the virtual game can achieve quick and effective access to blood oxygen, heart rate and other physiological information of the patients. At the same time, by means of the hardware control system, the start-up of the gait rehabilitation training robot could be controlled accurately and effectively. Therefore, the plantar pressure information and the velocity information, together with the physiological information of the patients, would be properly reflected in the game lastly and the physical condition of the patients participating in rehabilitation training would also be reflected to a great extent
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