Miracle-dz/UniFAR-FTUs huggingface dataset:
MOSafely, Is that Sus? A Youth-Centric Online Risk Assessment Dashboard

Current youth online safety and risk detection solutions are mostly geared toward parental control. As HCI researchers, we acknowledge the importance of leveraging a youth-centered approach when building Artificial Intelligence (AI) tools for adolescents online safety. Therefore, we built the MOSafely, Is that ‘Sus’ (youth slang for suspicious)? a web-based risk detection assessment dashboard for youth (ages 13-21) to assess the AI risks identified within their online interactions (Instagram and Twitter Private conversations). This demonstration will showcase our novel system that embedded risk detection algorithms for youth evaluations and adopted the human–in–the loop approach for using youth evaluations to enhance the quality of machine learning models.
Cashflow Tracing: Detecting Online game bots leveraging financial analysis with Recurrent Neural Networks

Since game bots and Gold Farmer Group (GFG) create critical damage toward the ecosystem of MMORPGs, game companies have struggled to detect bot characters with various methods. Primarily, previous researches identified GFGs by analyzing particular behavior patterns of bots, but these methods have become easier to be neutralized as bots started to mimic normal characters. Moreover, the spread of mobile MMORPGs decreased the diversity of character behavior; thus, classification of behavior patterns between bots and users becomes a more challenging task. To address this problem, we propose a bot detection method which is generally applicable toward modern MMORPGs in both PC and mobile environment. We focused on the analogy that bot characters and normal characters show different patterns of financial activities. As game bots are born to collect game assets for Real Money Trade (RMT), they show patterned changes in financial status to maximize its efficiency. On the other hand, normal characters take various types of financial activity as users play various in-game contents, not only accumulate the asset. Throughout the study, our series of analysis propose contributions as follow. First, we clarified financial sequences of game bots are different from normal characters; therefore, the sequential form of financial features precisely describes the financial pattern of characters. Second, we established a bot detection model with Recurrent Neural Networks (RNN) trained with the aforementioned financial sequences. With the real-world log data extracted from three PC games (Lineage, Aion, Blade and Soul), and one mobile game (Lineage M), we validated the proposed detection model effectively identifies game bots from normal users. Lastly, our detection model is widely applicable in both PC MMORPG and mobile MMORPG.
The Spheres of Player Motivation: Understanding Players of Digital Games

Understanding player motivations is crucial for the games industry, as it empowers developers to improve how their games are designed. Each player is different and possesses their own motivations to play. Motivations can vary greatly, ranging from socializing with friends to the challenge of overcoming a difficult boss. Many studies have tried to quantify these desires into models to allow game developers to better understand what drives players to play their games. As the industry evolves, so too must these models in order to account for emerging motivations of play. This research will explore the development of a new typology, extending the outcomes of the last three decades of player motivation research.
ESCHER: expressive scheduling with ephemeral resources

As distributed applications become increasingly complex, so do their scheduling requirements. This development calls for cluster schedulers that are not only general, but also evolvable. Unfortunately, most existing cluster schedulers are not evolvable: when confronted with new requirements, they need major rewrites to support these requirements. Examples include gang-scheduling support in Kubernetes [6, 39] or task-affinity in Spark [39]. Some cluster schedulers [14, 30] expose physical resources to applications to address this. While these approaches are evolvable, they push the burden of implementing scheduling mechanisms in addition to the policies entirely to the application. ESCHER is a cluster scheduler design that achieves both evolvability and application-level simplicity. ESCHER uses an abstraction exposed by several recent frameworks (which we call ephemeral resources) that lets the application express scheduling constraints as resource requirements. These requirements are then satisfied by a simple mechanism matching resource demands to available resources. We implement ESCHER on Kubernetes and Ray, and show that this abstraction can be used to express common policies offered by monolithic schedulers while allowing applications to easily create new custom policies hitherto unsupported.
Mindful Garden: Supporting Reflection on Biosignals in a Co-Located Augmented Reality Mindfulness Experience

We contribute Mindful Garden, an Augmented Reality Lens for Co-Located Mindfulness. HCI research has increasingly supported designing technology to support mindfulness. Augmented reality and sensors which detect biosignals both have the potential to support creating mindful experiences, by transforming people’s environments into more relaxing spaces and offering some feedback to help people make sense of their physio-psychological states. We will demo Mindful Garden, a system for supporting reflection on biosignals in a mindfulness experience where two people are physically co-located. Mindful Garden has one person guide the other through meditation, representing the guided individual’s biosignals as flowers in a shared augmented reality environment. We leverage Snap Spectacles and a Muse 2 headband, showing the promise of ready-to-use consumer technologies for the purpose of mindfulness and well-being. To overcome technical limitations in accessing, our demo illustrates a novel pipeline for real-time biomarker data streaming from the Muse 2 to the AR lens in the Snap Spectacles.
Elastic cloud services: scaling snowflake's control plane

Snowflake's "Data Cloud", provided as Software-as-a-Service (SaaS), enables data storage, processing, and analytic solutions in a performant, easy to use, and flexible manner. Although cloud service providers provide the foundational infrastructure to run and scale a variety of workloads, operating Snowflake on cloud infrastructure presents interesting challenges. Customers expect Snowflake to be available at all times and to run their workloads with high performance. Behind the scenes, the software that runs customer workloads needs to be serviced and managed. Additionally, failures in individual components such as Virtual Machines (VM) need to be handled without disrupting running workloads. As a result, lifecycle management of compute artifacts, their scheduling and placement, software rollout (and rollback), replication, failure detection, automatic scaling, and load balancing become extremely important. In this paper, we describe the design and operation of Snowflake's Elastic Cloud Services (ECS) layer that manages cloud resources at global scale to meet the needs of the Snowflake Data Cloud. It provides the control plane to enable elasticity, availability, fault tolerance and efficient execution of customer workloads. ECS runs on multiple cloud service providers and provides capabilities such as cluster management, safe code rollout and rollback, management of pre-started pools of running VMs, horizontal and vertical autoscaling, throttling of incoming requests, VM placement, load-balancing across availability zones and cross-cloud and cross-region replication. We showcase the effect of these capabilities through empirical results on systems that execute millions of queries over petabytes of data on a daily basis.
WAVE EXTENSION PROBLEM FOR THE FRACTIONAL LAPLACIAN

We show that the fractional Laplacian can be viewed as a Dirichlet-to-Neumann map for a degenerate hyperbolic problem, namely, the wave equation with an additional diffusion term that blows up at time zero. A solution to this wave extension problem is obtained from the Schrodinger group by means of an oscillatory subordination formula, which also allows us to find kernel representations for such solutions. Asymptotics of related oscillatory integrals are analysed in order to determine the correct domains for initial data in the general extension problem involving non-negative self-adjoint operators. An alternative approach using Bessel functions is also described.
Linear Programming Formulations of Singular Stochastic Control Problems: Time-Homogeneous Problems

Conditions are established under which the optimal control of processes having both absolutely continuous and singular (with respect to time) controls are equivalent to linear programs over a space of measures on the state and control spaces. This paper considers long-term average and discounted criteria and includes budget and resource constraints. The linear programs optimize over measures representing the expected occupation measure of the state and absolutely continuous control processes and a similar expected occupation measure of the state and control when the singular action of the process occurs. The evolution of these processes is characterized through an adjoint equation which the measures must satisfy in relation to the absolutely continuous and singular generators of the process. Existence of optimal relaxed controls of feedback type are established in general while existence of an optimal form of strict control is proven under additional closedness and compactness conditions.
Misleading Tweets and Helpful Notes: Investigating Data Labor by Twitter Birdwatch Users

In response to concerns about misleading content on social media, Twitter launched the “Birdwatch” initiative that allows volunteers to label and add context to tweets. We study data from Birdwatch to understand how users are performing “data labor” for Twitter, with implications for other platforms that are similarly reliant on data labor. We conduct computational analyses of Birdwatch text data and perform machine learning experiments to see how Birdwatch contributions might be used for classification. We find that Birdwatch users discuss distinct topics in domains like politics and news. While using Birdwatch data for content-only predictions may provide only a small amount of predictive power, in some cases Birdwatch data may be able to support ML systems. Furthermore, we see that the continuous flow of Birdwatch contributions provides great value in terms of supporting a “guess most frequent“ baseline for classifying Twitter content.
Smoothing effects and decay estimate of the solution of the linearized two species Landau equation

We study the Landau equation for a mixture of two species in the whole space, with initial condition of one species near a vacuum and the other near a Maxwellian equilibrium state. For the linearized level, without any smoothness assumption on the initial data, it is shown that the solution becomes smooth instantaneously in both the space and momentum variables. Moreover, the large time behavior of the solution is also obtained.
Workload consolidation in alibaba clusters: the good, the bad, and the ugly

Web companies typically run latency-critical long-running services and resource-intensive, throughput-hungry batch jobs in a shared cluster for improved utilization and reduced cost. Despite many recent studies on workload consolidation, the production practice remains largely unknown. This paper describes our efforts to efficiently consolidate the two types of workloads in Alibaba clusters to support the company's e-commerce businesses. At the cluster level, the host and GPU memory are the bottleneck resources that limit the scale of consolidation. Our system proactively reclaims the idle host memory pages of service jobs and dynamically relinquishes their unused host and GPU memory following the predictable diurnal pattern of user traffic, a technique termed tidal scaling. Our system further performs node-level micro-management to ensure that the increased workload consolidation does not result in harmful resource contention. We briefly share our experience in handling the surging traffic with flash-crowd customers during the seasonal shopping festivals (e.g., November 11) using these "good" practices. We also discuss the limitations of our current solution (the "bad") and some practical engineering constraints (the "ugly") that make many prior research solutions inapplicable to our system.
When to Collect Sensitive Category Data? Public Sector Considerations For Balancing Privacy and Freedom from Discrimination in Automated Decision Systems

Automated Decision Systems (ADS) are being used to inform important decisions in government services. Concerns regarding discrimination in ADS have led to the rise of bias mitigation techniques, or data science practices that measure and adjust for disparities based on protected class data. These techniques often require demographic or sensitive category data to both measure discrimination in ADS and process data to mitigate the discriminatory bias. The collection of sensitive category data increases privacy concerns. This preliminary study includes nine semi-structured interviews with data practitioners working in government. The analysis explores what considerations data practitioners in the public sector make when determining best practices for sensitive category data collection and how they engage with the tradeoff between privacy and right to freedom from discrimination. Themes pertinent to social services emerged including the importance of accessibility, reasons for data minimization and the role of reporting structures and historical norms.
Shell and isospin effects in nuclear charge radii

The shell effect and isospin effect in nuclear charge radii are systematically investigated and a four-parameter formula is proposed for the description of the root-mean-square (rms) charge radii by combining the shell corrections and deformations of nuclei obtained from the Weizs\"acker-Skyrme mass model. The rms deviation with respect to the 885 measured charge radii falls to 0.022 fm. The proposed formula is also applied for the study of the charge radii of super-heavy nuclei and nuclear symmetry energy. The linear relationship between the slope parameter L of the nuclear symmetry energy and the rms charge radius difference of 30S - 30Si mirror pair is clearly observed. The estimated slope parameter is about $L=54 \pm 19$ MeV from the coefficient of the isospin term in the proposed charge radius formula.
"Digital Sanctums of Empowerment": Exploring Community and Everyday Resilience-Building Tactics in Online Professional Communities for Women

Work and learning are essential facets of human life and legacy, yet sociocultural barriers may limit access to them. Historically, women have faced multiple restrictions that have hindered their entry into professional ventures. These constraints are even more palpable in technical domains, due to the added sociocultural barriers in these fields which have relegated women’s voices to the margins. Through a mixed-methods comparative investigation of three professional development communities on Reddit - two focused on women, and one general professional online community serving as the baseline, we report and discuss the resilience-building, empowerment, and knowledge-development tactics that drive collective identity formation in these subreddits. The long-term goal of this work is to address the way in which such channels can be designed, maintained, and curated to offer spaces for enrichment, empowerment, and advocacy with a focus on professional development for women in technical domains.
KOLE: breaking the scalability barrier for managing far edge nodes in cloud

In edge computing, the trend is moving towards leveraging cloud native technologies and platforms such as con-tainerization and Kubernetes to manage edge applications to improve operation efficiency. Unfortunately, the supported number of nodes per cluster is only several thousand in Kubernetes which is much less than what typical far edge use cases require. In this paper, we propose KOLE, a framework that extends the upstream Kubernetes for supporting a large number of far edge nodes. It replaces the existing apiserver-to-node communication mechanism in Kubernetes with a MQTT messaging system. The MQTT broker completely offloads the overhead of keeping numerous HTTP connections for nodes in the apiserver. In KOLE, we avoid creating numerous individual objects in the apiserver by maintaining them in a cloud state cache. The cache is snapshotted periodically for disaster recovery. Overall, KOLE achieves outstanding scalability by sacrificing the manageability of having individual objects, which we believe is a reasonable trade-off for far edge use cases. The experiment results show that KOLE is scalable and can support up to one million nodes.
A Privacy Paradox? Impact of Privacy Concerns on Willingness to Disclose COVID-19 Health Status in the United States

Privacy concerns around sharing personal health information are frequently cited as hindering COVID-19 contact tracing app adoption. We conducted a nationally representative survey of 304 adults in the United States to investigate their attitudes towards sharing two types of COVID-19 health status (COVID-19 Diagnosis, Exposure to COVID-19) with three different audiences (Anyone, Frequent Contacts, Occasional Contacts). Using the Internet User’s Information Privacy Concern (IUIPC) scale, we were able to identify the effect of different types of privacy concerns on sharing this information with various audiences. We found that privacy concerns around data Collection predicted lower willingness to share either type of health status to all of these audiences. However, desire for Control and for Awareness of data practices increased willingness to share health information with certain audiences. We discuss the implications of our findings.
Spectral properties of quantum circulant graphs

We introduce a new model for investigating spectral properties of quantum graphs, a quantum circulant graph. Circulant graphs are the Cayley graphs of cyclic groups. Quantum circulant graphs with standard vertex conditions maintain important features of the prototypical quantum star graph model. In particular, we show the spectrum is encoded in a secular equation with similar features. The secular equation of a quantum circulant graph takes two forms depending on whether the edge lengths respect the cyclic symmetry of the graph. When all the edge lengths are incommensurate, the spectral statistics correspond to those of random matrices from the Gaussian orthogonal ensemble according to the conjecture of Bohigas, Giannoni and Schmit. When the edge lengths respect the cyclic symmetry the spectrum decomposes into subspectra whose corresponding eigenfunctions transform according to irreducible representations of the cyclic group. We show that the subspectra exhibit intermediate spectral statistics and analyze the small and large parameter asymptotics of the two-point correlation function, applying techniques developed from star graphs. The particular form of the intermediate statistics differs from that seen for star graphs or Dirac rose graphs. As a further application, we show how the secular equations can be used to obtain spectral zeta functions using a contour integral technique. Results for the spectral determinant and vacuum energy of circulant graphs are obtained from the zeta functions.
Modest transcriptomic response to polyploidization in allohexaploid wheat synthetics

Bread wheat is a recent allohexaploid (genomic constitution AABBDD) that emerged through a hybridization between tetraploid Triticum turgidum (AABB) and diploid Aegilops tauschii (DD) <10,000 years ago. The hexaploidization can be re-created artificially, producing synthetic wheat that has been used to study immediate genomic responses to polyploidization. Here we produced several synthetic wheats from alternative parental genotypes and reciprocal crosses, and examined transcriptomes from two different tissues and successive generations. We did not detect a massive reprogramming in gene expression, with only ∼1% of expressed genes showing significant differences compared to their lower-ploidy parents. Most of the differential expression is located on the D subgenome, without consistency in the direction of the expression change. Leaves and developing endosperm show distinct patterns of homoeologous expression bias, and almost non-overlapping sets of differentially expressed genes, implying that the polyploidization-triggered reprogramming is not effectuated through permanent (epi)genetic changes. While 0-3 families of transposable elements (TEs) became upregulated in wheat synthetics, we did not detect any significant association between TEs and the expression of nearby genes. We conclude that the modest tissue-specific and partially genotype-specific transcriptomic response to polyploidization is likely caused by rare incompatibilities of parental regulomes, and we discuss the pitfalls of transcriptomic comparisons across ploidy levels that can inflate the de-regulation signal.
Evolutionary rescue in a fluctuating environment

No environment is constant over time, and environmental fluctuations impact the outcome of evolutionary dynamics. Survival of a population not adapted to some environmental conditions is threatened unless a mutation rescues it, an eco-evolutionary process termed evolutionary rescue. We here investigate evolutionary rescue in an environment that fluctuates between a favorable state, in which the population grows, and a harsh state, in which the population declines. We develop a stochastic model that includes both population dynamics and genetics. We derive analytical predictions for the mean extinction time of a non-adapted population given that it is not rescued, the probability of rescue by a generalist mutation, and the mean appearance time of a rescue mutant, which we validate using numerical simulations. We find that evolutionary rescue is less (respectively more) likely if the environmental fluctuations are stochastic rather than deterministic and if the mean time between each environmental change is less (respectively more) than the mean survival time of the population in the harsh environment. We demonstrate that high equilibrium population sizes and per capita growth rates maximize the chances of evolutionary rescue. We show that an imperfectly harsh environment, which does not fully prevent births but makes the death rate to birth rate ratio much greater than unity, has almost the same rescue probability as a perfectly harsh environment, which fully prevents births. Finally, we put our results in the context of antimicrobial resistance and conservation biology.