Long-tail motion forecasting is a core challenge for autonomous driving, where rare yet safety-critical events-such as abrupt maneuvers and dense multi-agent interactions-dominate real-world risk. Existing approaches struggle in these scenarios because they rely on either non-interpretable clustering or model-dependent error heuristics, providing neither a differentiable notion of “tailness” nor a mechanism for rapid adaptation. We propose SAML, a Semantic-Aware Meta-Learning framework that introduces the first differentiable definition of tailness for motion forecasting. SAML quantifies motion rarity via semantically meaningful intrinsic (kinematic, geometric, temporal) and interactive (local and global risk) properties, which are fused by a Bayesian Tail Perceiver into a continuous, uncertainty-aware Tail Index. This Tail Index drives a meta-memory adaptation module that couples a dynamic prototype memory with an MAML-based cognitive set mechanism, enabling fast adaptation to rare or evolving patterns. Experiments on nuScenes, NGSIM, and HighD show that SAML achieves state-of-the-art overall accuracy and substantial gains on top 1-5% worst-case events, while maintaining high efficiency. Our findings highlight semantic meta-learning as a pathway toward robust and safety-critical motion forecasting.

Nudges are subtle interventions designed to influence user behavior without restricting choice. Responsible gambling messages (RGMs) exemplify such nudges by encouraging safer decision-making in gambling environments. Prior research has examined how pop-up messages influence gambling behavior in experimental settings and has explored the design of effective slogan messages. However, little is known about how different types of RGMs shape users’ real-world gambling behavior and safety. To address this gap, we apply a nudging perspective to examine how RGMs support gambling safety throughout gamblers’ decision-making journey. We conducted semi-structured interviews with 22 gamblers and found that participants were generally aware of RGMs, yet some misunderstood their intended purpose. Participants perceived the safety impact of RGMs as reflected in both attitudinal and behavioral dimensions. We further discuss users’ message reception practices and the effectiveness of RGMs as nudges, and conclude with design implications for promoting gambling safety.

Short-form video platforms (SVSPs) have rapidly evolved from entertainment applications to essential digital infrastructures in rural areas. As such, they are reshaping how farmers organize production and manage everyday activities. However, little is known about how this transformation impacts agricultural practices directly. To explore this, we conducted semi-structured interviews with 20 farmers engaged in crop, livestock, and aquaculture production. Our findings reveal that farmers perceive SVSPs as infrastructural supports across various farming stages—planning, establishment, protection, and sales—by providing timely access to market opportunities, practical knowledge, and peer networks. However, reliance on SVSPs also introduces challenges, including fragmented and unreliable content, issues of contextual relevance, and tensions between platform dynamics and farming practices. This study contributes to understanding how emerging media infrastructures, like SVSPs, reshape rural production. We also offer design recommendations for building more context-aware, resilient, and inclusive digital support systems for agriculture.

Recent advances in generative AI have introduced a new programming paradigm—vibe coding, a natural language–driven mode of AI collaboration. While promising for adults, little is known about how children engage with this approach, especially in block-based environments. To explore this gap, we conducted workshops with children of varying Scratch experience (n=41) and interviewed five Scratch teachers. Our study investigates how vibe coding impacts children’s programming learning and practice, and what challenges arise. Findings show that vibe coding has both positive and negative impacts across three key contexts of children’s programming experience: acquisition, application, and creation. Across the stages of vibe coding—goal articulation, information interpretation, and outcome evaluation—children encounter distinct challenges. By examining the mismatches between core assumptions of vibe coding and children’s needs, and analyzing its applicability across different contexts, we offer child-centered design implications for future vibe coding systems and GenAI tools.

As Smart Home Personal Assistants (SPAs) evolve into social agents, understanding user privacy necessitates interpersonal communication frameworks, such as Privacy Boundary Theory (PBT). To ground our investigation, our three-phase preliminary study (1) identified transmission and sharing ranges as key boundary-related risk factors, (2) categorized relevant SPA functions and data types, and (3) analyzed commercial practices, revealing widespread data sharing and non-transparent safeguards. A subsequent mixed-methods study (N=412 survey, N=40 interviews among the survey participants) assessed users’ perceived privacy risks across data types, transmission ranges and sharing ranges. Results demonstrate a significant, non-linear escalation in perceived risk when data crosses two critical boundaries: the ‘public network’ (transmission) and ‘third parties’ (sharing). This boundary effect holds robustly across data types and demographics. Furthermore, risk perception is modulated by data attributes (e.g., social relational data), and contextual privacy calculus. Conversely, anonymization safeguards show limited efficacy especially for third-party sharing, a finding attributed to user distrust. These findings empirically ground PBT in the SPA context and inform design of boundary-aware privacy protection.

As mobile devices become increasingly integrated into children's daily lives, digital safety has emerged as a pressing concern, particularly for children with intellectual disabilities (ID), who are more vulnerable due to their cognitive and behavioral challenges. Despite their heightened risk, little research has addressed the unique digital safety issues these children face. To bridge this gap, we conducted semi-structured interviews with parents and special education teachers who are key figures for overseeing the digital access and safety of children with ID. Our findings highlight four primary concerns: imitation of harmful behaviors, accidental misoperation of devices, risks from fraud, and exposure to cyberbullying. To address these, parents and teachers largely rely on proactive educational strategies supported by technical controls and device restrictions. We conclude by emphasizing the need to adapt special education practices to the evolving digital landscape and propose inclusive safety strategies applicable to other at-risk user groups.

Distributed tracing is widely employed for troubleshooting distributed systems such as microservices. Existing tracing systems typically improve one or more of data completeness, non-intrusiveness, or lightweight operation through various data generation strategies. However, no current solution optimizes all three aspects simultaneously, which can introduce significant overhead in I/O-intensive environments that demand both high completeness and minimal intrusion. In this paper, we introduce LightTrace, a novel, eBPF-enabled toolkit that optimizes the data transmission mechanism in distributed tracing. LightTrace integrates seamlessly with mainstream tracing systems and leverages eBPF to reduce end-to-end latency and lower overall system overhead. LightTrace is implemented using a combination of kernel-level eBPF and user-space Golang components. Our evaluation demonstrates that LightTrace decreases the average latency overhead by up to 22.8 % and improves the peak throughput of microservice systems by between$\mathbf{1 2. 3 \%}$and$\mathbf{1 8. 6 \%}$. Furthermore, LightTrace's adaptability across diverse tracing platforms underscores its versatility in various microservice environments.

Federated Unlearning (FU) addresses the "right to be forgotten" in federated learning by removing specific client data's contribution without retraining from scratch. Existing FUs are data-dependent, which make the assumption that systems can access original training data or stored historical parameter updates during unlearning. However, the assumption cannot always hold in practice, as users usually request the deletion of client data and historical parameter updates due to privacy concerns or storage limitations. Therefore, it is crucial to develop a zero-shot FU method without such data access. The key challenge is how to distinguish and remove the impact of target clients without data-level information. Motivated by the idea that if we can learn client-specific personalized information from the model instead of data, FU can be model-centric and data-free, we present the first zero-shot FU framework ZeroFU. By embedding client contributions into the model during learning via condition computation, ZeroFU enables the model to possess personalized features for unlearning. The unlearning is achieved using a proposed GAN-based distillation framework that obfuscates the personalized feature of the target client. Evaluations demonstrate its effectiveness in unlearning under non-IID settings.

Stablecoins, a type of cryptocurrency pegged to another asset to maintain a stable price, have become an important part of the cryptocurrency ecosystem. Prior studies have primarily focused on examining the security of stablecoins from technical and theoretical perspectives, with limited investigation into users' risk perceptions and security behaviors in stablecoin practices. To address this research gap, we conducted a mixed-method study that included constructing a stablecoin interaction framework based on the literature, which informed the design of our interview protocol, semi-structured interviews (n=21), and Reddit data analysis (9,326 posts). We found that participants see stable value and regulatory compliance as key security advantages of stablecoins over other cryptocurrencies. However, participants also raised concerns about centralization risks in fiat-backed stablecoins, perceived challenges in crypto-backed stablecoins due to limited reliance on fully automated execution, and confusion regarding the complex mechanisms of algorithmic stablecoins. We proposed improving user education and optimizing mechanisms to address these concerns and promote the safer use of stablecoins.

Non-Fungible Tokens (NFTs) are transforming the digital art by allowing artists to sell unique, one-of-a-kind digital artwork that is verified on the blockchain. Although NFTs have attracted much attention from academia and industry, little is known about this innovative art practice. In this work, we focus on how NFT artists understand their art creation process, interact with NFT marketplaces, and face challenges in their NFT practices. We conducted a mixed-method study, including interviews with 19 artists and an analysis of market transactions on OpenSea and SuperRare. We found that serialization and Web3 concepts are significant features in NFT artwork creations, and artists utilize artificial intelligence and smart contracts to create artworks incorporating these features. Artists perceived that blockchain features like transparency, openness, and authentication strongly influenced their NFT trading experiences, and they also encountered challenges such as determining artwork value, rights ownership, and addressing technical issues in the creation process.

Local Differential Privacy (LDP) is a mechanism used to protect training privacy in Federated Learning (FL) systems, typically by introducing noise to data and local models. However, in real-world distributed edge systems, the non-independent and identically distributed nature of data means that clients in FL systems experience varying sensitivities to LDP-introduced noise. This disparity leads to fairness issues, potentially discouraging marginal clients from contributing further. In this paper, we explore how to enhance client-level performance fairness under LDP conditions. We model an FL system with LDP and formulate the problem PriFair using regularization, which assigns varied noise amplitudes to clients based on federated analytics. Additionally, we develop PriFairFed, a Tikhonov regularization-based algorithm that eliminates variable dependencies and optimizes variables alternately, while also offering a theoretical privacy guarantee. We further experimented with the algorithm on a real-world system with 20 Raspberry Pi clients, showing up to a 73.2% improvement in client-level fairness compared to existing state-of-the-art approaches, while maintaining a comparable level of privacy.

Fandom crowdfunding, where fans collectively raise funds for idols, fosters dynamic interactions within fandom communities, evoking a range of emotions. Despite the prevalence of such activities, the specific emotions involved and their effects on participant behavior remain underexplored. Addressing this, our mixed-methods study—encompassing observations, interviews, and analysis of crowdfunding data—investigated emotions during fandom crowdfunding and their influence on behavior across crowdfunding stages: planning, support, encouragement, realization, and auditing. We identified 10 key emotions related to idols and the community, finding these emotions crucial in shaping participant actions. Our findings highlight the dual impact of fandom crowdfunding on the community’s internal dynamics and its relationships with idols and broader society. We propose design recommendations for enhancing fandom crowdfunding and suggest how general crowdfunding can benefit from insights gained from the fandom context, offering a novel understanding of emotions in collaborative monetary activities.

We present FedSM, a Federated Spectrum Management architecture to increase channel utilization (CU) and reduce latency, while protecting users’ data privacy. We employ hedonic coalition formation game for spectrum allocation. Within each coalition, we design a bandit learning algorithm to share spectra and adjust resource usage. Preliminary simulation results show FedSM increases CU to 93.51% and reduces latency to 248.68 ms compared to three privacy-preserving dynamic spectrum management architectures.

The advent of Web3 technologies promises unprecedented levels of user control and autonomy. However, this decentralization shifts the burden of security onto the users, making it crucial to understand their security behaviors and perceptions. To address this, our study introduces a comprehensive framework that identifies four core components of user interaction within the Web3 ecosystem: blockchain infrastructures, Web3-based Decentralized Applications (DApps), online communities, and off-chain cryptocurrency platforms. We delve into the security concerns perceived by users in each of these components and analyze the mitigation strategies they employ, ranging from risk assessment and aversion to diversification and acceptance. We further discuss the landscape of both technical and human-induced security risks in the Web3 ecosystem, identify the unique security differences between Web2 and Web3, and highlight key challenges that render users vulnerable, to provide implications for security design in Web3.

Combinatorial auctions (CAs) offer the flexibility for bidders to articulate complex preferences when competing for multiple assets. However, the behavior of bidders under different payment rules is often unclear. Our research explores the relationship between core constraints and several core-selecting payment rules. Specifically, we examine the natural and desirable property of payment rules of being non-decreasing, which ensures that bidding higher does not lead to lower payments. Earlier studies revealed that the VCG-nearest payment method – a commonly employed payment rule – fails to adhere to this principle even for single-minded CAs. We establish that when a single effective core constraint exists, the payment maintains the non-decreasing property in single-minded CAs. To identify auctions where such a constraint is present, we introduce a novel framework using conflict graphs to represent single-minded CAs and establish sufficient conditions for the existence of single effective core constraints. We proceed with an analysis of the implications on bidder behavior, demonstrating that there is no overbidding in any Nash equilibrium when considering non-decreasing core-selecting payment rules. Our study concludes by establishing the non-decreasing nature of two additional payment rules, namely the proxy and proportional payment rules, for single-minded CAs.

Resource optimization in blockchain systems is a critical aspect of their architectural design. Despite frequent network congestion in Ethereum, a notable proportion of block space is underutilized, with occurrences of completely unused blocks exacerbating resource inefficiency in the network. This study investigates the motivations behind miners’ production of empty blocks. It is found that the immediate gains from mining empty blocks often outweigh the potential benefits derived from including transactions. Furthermore, our analysis indicates a substantial decrease in the frequency of empty blocks following Ethereum’s transition at the Merge, underscoring the effectiveness of the Proof-of-Stake (PoS) consensus mechanism in improving block space utilization in blockchain environments.

In the evolving landscape of digital art, NonFungible Tokens (NFTs) have emerged as a groundbreaking platform, bridging the realms of art and technology. NFTs serve as the foundational framework that has revolutionized the market for digital art, enabling artists to showcase and monetize their creations in unprecedented ways. NFTs combine metadata stored on the blockchain with off-chain data, such as images, to create a novel form of digital ownership. It is not fully understood how these factors come together to determine NFT prices. In this study, we analyze both on-chain and off-chain data of NFT collections trading on OpenSea to understand what influences NFT pricing. Our results show that while text and image data of the NFTs can be used to explain price variations within collections, the extracted features do not generalize to new, unseen collections. Furthermore, we find that an NFT collection's trading volume often relates to its online presence, like social media followers and website traffic.

Within just four years, the blockchain-based Decentralized Finance (DeFi) ecosystem has accumulated a peak total value locked (TVL) of more than 253 billion USD. This surge in DeFi’s popularity has, unfortunately, been accompanied by many impactful incidents. According to our data, users, liquidity providers, speculators, and protocol operators suffered a total loss of at least 3.24 billion USD from Apr 30, 2018 to Apr 30, 2022. Given the blockchain’s transparency and increasing incident frequency, two questions arise: How can we systematically measure, evaluate, and compare DeFi incidents? How can we learn from past attacks to strengthen DeFi security?In this paper, we introduce a common reference frame to systematically evaluate and compare DeFi incidents, including both attacks and accidents. We investigate 77 academic papers, 30 audit reports, and 181 real-world incidents. Our data reveals several gaps between academia and the practitioners’ community. For example, few academic papers address "price oracle attacks" and "permissonless interactions", while our data suggests that they are the two most frequent incident types (15% and 10.5% correspondingly). We also investigate potential defenses, and find that: (i) 103 (56%) of the attacks are not executed atomically, granting a rescue time frame for defenders; (ii) bytecode similarity analysis can at least detect 31 vulnerable/23 adversarial contracts; and (iii) 33 (15.3%) of the adversaries leak potentially identifiable information by interacting with centralized exchanges.

Decentralized finance (DeFi) enables crypto-asset holders to conduct complex financial transactions, while maintaining control over their assets in the blockchain ecosystem. However, the transparency of blockchain networks and the open mechanism of DeFi applications also cause new security issues. In this paper, we focus on sandwich attacks, where attackers take advantage of the transaction confirmation delay and cause financial losses for victims. We evaluate the impact and investigate users’ perceptions of sandwich attacks through a mix-method study. We find that due to users’ lack of technical background and insufficient notifications from the markets, many users were not aware of the existence and the impact of sandwich attacks. They also had a limited understanding of how to resolve the security issue. Interestingly, users showed high tolerance for the impact of sandwich attacks on individuals and the ecosystem, despite potential financial losses. We discuss general implications for users, DeFi applications, and the community.

Abstract Live Streaming Commerce (LSC) is proliferating in China and gaining traction worldwide. LSC is an e-commerce service where sellers communicate with consumers through live streaming while consumers can place orders within the same system. Despite the significant involvement of consumers in LSC, it has not been systematically analyzed how consumers make shopping decisions when engaging with LSC. In this paper, we conduct a mixed-methods study, consisting of surveys ( N 1 = 240) and follow-up interviews ( N 2 = 16) with LSC consumers. We focus on two features of LSC, i.e., the communication between merchants and consumers through live streaming and the participation of streamers, and aim to understand how these changes influence consumers’ decision-making process in LSC. We find that LSC enables merchants to exchange information with consumers based on their needs and provide additional customer services. Because of the appropriate information about the products they acquire and the enjoyable shopping atmosphere, consumers are willing to purchase products in LSC. As the intermediaries between merchants and consumers, streamers utilize their independent identity from merchants to enhance consumers’ awareness of shopping and persuade their online shopping decisions. Moreover, we consider the opportunities and challenges of current LSC services and provide implications for LSC services and the research community regarding the development of LSC.

Gay dating applications, such as Grindr and SCRUFF, are considered the primary platforms for gay men to conduct online dating activities. However, on Zhihu, a Chinese question-and-answer website, tens of thousands of homosexual users have been searching for romantic partners, which suggests that Zhihu may have unique affordances in online dating activities for Chinese gay men. To better understand how Chinese gay men perceive the affordances of a non-dating platform for online dating, we conduct a mixed-methods study, including observations, interviews, and quantitative and qualitative analysis of users' self-presentations. We find that gay men users publish personal ads by answering "fishing questions" on Zhihu. Through our analysis, we examine how users perceive the affordances of Zhihu to satisfy their social and psychological gratifications at the self, community, and audience levels. Although gay users face the risk of disclosing homosexual identity on mainstream social media, they perceive such risk as acceptable for better online dating experience. We discuss how users respond to severe social stigma in China, and the gap between user needs and the design of gay dating applications. We elaborate on the implications of our findings to discuss the potential benefits for LGBTQ users if LGBTQ service providers collaborate with social media.

The consensus number concept is used to determine the power of synchronization primitives in distributed systems. Recent work in the blockchain domain motivates shifting the attention to consensus number 1 objects, as it has been shown that transaction-based blockchains just need consensus number 1. In this paper we want to get a better understanding of such consensus number 1 objects. In particular, we study the necessary and sufficient conditions for determining the consensus number 1 objects. If an object has consensus number 1, then its operations must be either commutative or associative (necessary condition). On the other hand, if the operations are consistently commutative or overwriting, i.e., independent of the current state of the object, then the consensus number of the object is 1 (sufficient condition). We give an algorithm to implement such generic consensus number 1 objects using only read/write registers. This implies that read/write registers are universal enough to solve tasks, such as asset transfer of a cryptocurrency, among many others, in wait-free distributed systems for any number of processes.

The Byzantine agreement problem is considered to be a core problem in distributed systems. For example, Byzantine agreement is often used to build a blockchain, a totally ordered log of records. Blockchains are asynchronous distributed systems, fault-tolerant against Byzantine nodes.

Combinatorial auctions are used to allocate resources in domains where bidders have complex preferences over bundles of goods. However, the behavior of bidders under different payment rules is not well understood, and there has been limited success in finding Bayes-Nash equilibria of such auctions due to the computational difficulties involved. In this paper, we introduce non-decreasing payment rules. Under such a rule, the payment of a bidder cannot decrease when he increases his bid, which is a natural and desirable property. VCG-nearest, the payment rule most commonly used in practice, violates this property and can thus be manipulated in surprising ways. In contrast, we show that many other payment rules are non-decreasing. We also show that a non-decreasing payment rule imposes a structure on the auction game that enables us to search for an approximate Bayes-Nash equilibrium much more efficiently than in the general case. Finally, we introduce the utility planes BNE algorithm, which exploits this structure and outperforms a state-of-the-art algorithm by multiple orders of magnitude.