Effective programming education requires personalized instruction adapted to diverse learner backgrounds. However, while LLM-based multi-agent systems (MAS) excel at complex planning, existing planners often lack profile-grounding and pedagogical scaffolding, thereby undermining personalized programming learning. To fill in the gap, we first introduce \textbf{MAP-PPL} (\textbf{M}ulti-\textbf{A}gent \textbf{P}lans for \textbf{P}ersonalized \textbf{P}rogramming \textbf{L}earning), a profile-conditioned multi-agent planning dataset with 3{,}043 query--profile--plan instances from 1{,}730 Stack Overflow question groups and 2{,}738 learner profiles. Each plan specifies agents, subtasks, executable steps, and prerequisite dependencies. Then, we propose \textbf{PersonalPlan}, a two-stage MAS planner that first performs hierarchical SFT with separate LoRA adapters for profile-aware task decomposition and step dependency planning, then applies a Reward-Adaptive GRPO to encourage the model to generate executable, personalized, and pedagogically scaffolded plans. Extensive experiments on MAP-PPL comparing PersonalPlan against frontier LLMs, generic MAS frameworks, and agentic planners demonstrate its superiority. With only 8B and 32B variants, PersonalPlan achieves state-of-the-art plan executability, personalization, and pedagogical quality, effectively orchestrating MAS for agent-student interactions.

As large language model (LLM) services become widely adopted, the cost of GPU resources for serving these models in cloud environments has emerged as a critical concern. Spot instances offer up to 90% cost savings over on-demand instances, but their frequent interruptions and limited availability pose significant challenges for continuous LLM serving. GPU spot instances, in particular, exhibit lower and more volatile availability than CPU-based instances, making homogeneous clusters that depend on a single GPU type vulnerable to correlated failures. Heterogeneous clusters spanning multiple GPU types can address this by leveraging complementary availability patterns across diverse spot pools, yet existing LLM serving systems are designed for homogeneous environments and suffer from load imbalance when deployed on heterogeneous GPUs. This paper presents ShuntServe, a cost-efficient LLM serving system for heterogeneous spot GPU clusters. ShuntServe employs a roofline model-based analytical serving performance estimator and a dynamic programming-based model placement optimizer that jointly determines node configuration, parallelization strategy, and layer assignment to maximize throughput across heterogeneous GPUs. To enhance fault tolerance when using spot instances, ShuntServe combines output-preserving request migration with concurrent initialization via a shared tensor store, minimizing migration downtime by overlapping replacement node preparation with ongoing serving. Evaluation on Llama-3.1-70B and Qwen3-32B with a heterogeneous AWS cluster of L4, A10G, and L40S GPUs shows that ShuntServe achieves 1.42x and 1.35x higher throughput than state-of-the-art baselines and attains 31.9% and 31.2% cost efficiency improvements over on-demand instances for offline and online serving, respectively.

The U.S. Food and Drug Administration (FDA)'s Food Traceability Rule requires agri-food supply chain stakeholders (stakeholders)--including farmers, fishers, retail workers, and others--to maintain detailed tracking records beginning in January 2026. Through this Rule, the FDA envisions a "New Era of Tech-Enabled Traceability," in which standardized, harmonized tracking data serve as a foundational public health infrastructure, enabling more rapid identification and removal of potentially contaminated food and ultimately reducing the risk of foodborne illness. Despite this promising vision, we observe that the Rule reconfigures agri-food stakeholders into data laborers by mandating stringent data collection, formatting, and reporting requirements. In this paper, we examine the tensions and burdens that arise from such reconfiguration. Leveraging Data Feminism as an orientation to attend to how data-driven policy implementation disproportionately burdens smaller, under-resourced stakeholders who lack the infrastructural and financial capacity to comply, we analyze 1,198 public comments submitted to Regulations.gov in response to the proposed Rule. Our qualitative document analysis reveals three key tensions: (1) the individual labor, financial, and educational burdens stakeholders experience as they are reconfigured into data workers; (2) moments where data tracking becomes infeasible due to infrastructural limitations, cultural contexts, and situated production practices; and (3) instances where the Rule's intended flexibility instead introduces confusion and burden due to its ambiguity.

The Erdős-Anning theorem states that any point set for which all distances are integers, in a Euclidean space of any dimension, must be either finite or collinear. We prove the same result in hyperbolic space of any dimension. A quantitative form of our result also extends for the first time to Euclidean spaces of dimension greater than two: if a set of points with integer distances in $\mathbb{E}^D$ or $\mathbb{H}^D$ has a subset of $D+1$ points in general position whose diameter is $d$, then the whole set has size $O(D(d+1)^D)$. To prove these results we formulate a lemma that, if the graph of external tangencies of a system of spheres in Euclidean or hyperbolic space contains a $K_{a,b}$ subgraph for $a,b\ge 3$, then the sets of spheres on each side of this biclique have centers that lie on a hyperplane. This lemma also implies that, in multilateration (determining a position from differences of distances to known landmarks), $D+1$ non-coplanar landmarks always suffice to limit the position to two possibilities.

Measuring the influence of a player in a simple game is a widely studied topic. Shapley-Shubik power index is perhaps the maximum exponent in terms of relevance. Furthermore, other power indexes have been proposed over time. In this paper, we propose yet another power index, defined in terms of winning coalitions. We show that this index coincides with eigenvalues obtained with the Principal Component Analysis method, a broadly used technique in data science to determine the influence of different features given a dataset. Furthermore, we provide a characterization of this proposed index in terms of four properties.

Unmanned aerial vehicle (UAV)-mounted base stations are highly susceptible to wind disturbances such as gusts and turbulence, which induce positional drift and degrade communication link quality, particularly in emergency scenarios. To address this challenge, we propose a DRL-based framework for wind-resilient trajectory adjustment and positioning based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The method models wind as a stochastic kinematic perturbation, avoiding complex aerodynamic modeling, thereby enabling the TD3 agent to learn adaptive control policies that maintain optimal coverage footprints. By prioritizing user-centric performance metrics under turbulent conditions, the proposed architecture ensures continuous service availability despite external disruptions. Simulation results demonstrate that the TD3-based approach effectively compensates for wind-induced displacements and outperforms benchmark methods, including Proximal Policy Optimization (PPO), in terms of throughput stability and robustness in windy environments.

Risk-Aware Causal Gating (RACG) defends tool-augmented LLM agents against indirect prompt injection by removing dangerous tools from the agent's visible action space, so that even a fully injection-compliant agent cannot call a tool it cannot see. We make three points. First, this structural guarantee does not eliminate the trust assumption behind safe tool use; it relocates it into the integrity of the tool contracts -- declared preconditions, effects, risk, and authorization -- that the gate reads, so an attacker who corrupts a contract can make the gate mis-decide without ever persuading the agent. Second, forging a tool's effects is strictly more dangerous than tampering with its risk label, because RACG applies a causal gate before its admissibility gate: an off-path tool is never exposed, so risk-relabeling alone fails, whereas effect forgery routes the dangerous tool onto the causal path and succeeds. Effect integrity, not the risk label, is the load-bearing assumption. Third, we introduce ContractGuard, a verifier between the registry and the gate that layers signed provenance, typed contract attestation, and runtime effect verification; on a controlled benchmark it restores injection success to zero against every modeled attack -- including an exhaustive white-box adaptive attacker -- without over-rejecting honest contracts, and the structural prediction is confirmed on six current-generation hosted models (Claude Opus 4.8, Sonnet 4.6, Haiku 4.5; Amazon Nova Premier and Nova 2 Lite; GPT-OSS-120B).

Researchers have long suspected that international research collaboration (IRC) and scientific and technological (S\&T) performance are subject to reciprocal causality, yet the endogenous co-evolution of these twin phenomena has yet to be tested by large-scale empirical analysis. This study tests IRC network effects on national research performance and vice versa simultaneously using a longitudinal co-evolution model on three decades of global network and national performance data. Stochastic actor oriented models (SAOM) are used to analyze data on 166 countries from 1993 to 2022. Yearly IRC networks are constructed from Web of Science's XML database, and performance data are gathered from Elsevier's fractional field-weighted citation index (FWCI). The models also account for geographic, economic, demographic, and political factors, as well as endogenous network processes. The results provide support for reciprocal co-evolution. However, notably, geographic distance appears to moderate the interaction between research performance and network dynamics, suggesting researchers may rely more on visible performance metrics when selecting geographically distant collaborators. This finding points to the role of citation based performance metrics as a signaling mechanism for collaborator selection.

Today's young adults are most immersed in technology, leading in feelings of powerlessness in managing online privacy across many platforms, and particularly susceptible to phishing attacks. This raises questions about their general, wide-ranging attitudes towards and management of cybersecurity. How do young, tech-savvy adults approach cybersecurity? We seek a better understanding of their cybersecurity knowledge, attitudes and experiences, in particular in addressing deceptive online communications. We surveyed a group of `lead users': computing university students (n = 236). By combining thematic analysis of open-ended responses with quantitative data, we provide insights into their experiences and perceptions. While students demonstrate reasonable cybersecurity awareness, their cybersecurity experiences vary, and inconsistencies exist around their practices, perceptions of responsibility, and support structures. Findings also reveal four key thematic tensions: 1) Computing students are knowledgeable yet have persistent incorrect beliefs, 2) They learn more about keeping safe from sources outside the classroom, 3) They have limited assistance and have fallen victim to cybercrime, and 4) Many are confident, yet others are concerned about their own safety and responsibility. Through cluster analysis of attitudes, we identify two groups, with one feeling less prepared, less confident, yet expressing a desire to learn more. Established measures of intentions and objective knowledge were correlated to preparedness. Self-efficacy correlated to confidence and predicted cluster membership.

We study the role of depth in ReLU networks with discrete (Boolean) inputs and real-valued outputs, complementing two established lines of work. For Boolean inputs, striking depth separation results were proven for $\mathsf{AC}^0$ but with threshold ($\mathsf{TC}^0$) or ReLU gates depth separation is only established for depth two vs. three. On the other hand, for {\em real-valued} functions and ReLU networks, Telgarsky's (2016) constructed a simple one variable class of functions which establishes separation at higher depths. In this paper we are interested to establish an all-depths depth separation for ReLU networks on $\{0,1\}^n$. We do so by exhibiting an explicit family of functions computable exactly by a ReLU network of depth $n+1$ and constant width, such that any ReLU network of depth $d$ and width $w$ computing the function exactly must satisfy $w^d = Ω(2^n)$; in particular, no network of depth $d = o(n/\log n)$ can compute it with width polynomial in $n$. We note that our lower bound relies on \emph{exact, infinite-accuracy} computation as an exponential precision truncation of the output is computable by a polynomial-size $\mathsf{TC}^0$ circuit.

UI/UX designers work with both paper-based and digital artifacts but lack tools that seamlessly integrate the two. Mixed Reality (MR) offers under-explored opportunities to combine the strengths of both design environments. To examine these opportunities, we first conducted interviews with 19 professional UI/UX designers to understand their current experiences using paper and digital artifacts. Motivated and informed by the interview insights, we organized nine conceptual-probe user study sessions in which designers engaged with a MR-probe that combined paper and digital prototyping processes and brainstormed MR's potential in UI/UX design. We found that participants valued MR for enabling continuous hybrid design workflows, reducing manual reconstruction, supporting spatially anchored workspaces, and facilitating real-time cross-medium collaboration. They also envisioned future MR tools with AI assistance, richer interactive and dynamic content, and the ability to manage diverse design artifacts within a unified environment. From these findings, we derive four design dimensions for future MR systems that could enable more fluid, creative, and collaborative design practices.

Retrieval-augmented generation (RAG) allows large language models to access external and private corpora for factual, domain-specific responses. Modern RAG pipelines use hierarchical navigable small world (HNSW) vector databases for efficient similarity search. When a user requests data deletion, the systems typically only mark the record as deleted, leaving the embedding on disk physically unchanged. This soft-delete operation raises compliance concerns under data-erasure and retention requirements such as GDPR Article 17 and HIPAA. Analysis on three HNSW implementations confirms that deleted vectors remain physically recoverable by accessing the raw index files at the storage layer, bypassing API access. Using the Vec2Text inversion model without domain-specific fine-tuning, we show this vulnerability on multiple real-world datasets and data modalities. On Wikipedia biographical living persons dataset (BLP), we successfully recover 25.5% of exact person names and 46.4% of geographic locations (ROUGE-L 0.185). Recovery reaches 100% for both patient age and gender markers (ROUGE-L 0.290) on highly structured, sensitive data (NIH Synthea dataset). On soft-deleted image embeddings, we show 100% tissue classification on histopathology patches (p=1.02e-07) and top-1 identity recovery reaches 99% on facial embeddings (p<0.01). This work introduces Epoch Key Rotation, which encrypts vectors and discards the key upon deletion. Epoch key rotation reduces observed PII recovery to 0% and completes in 2.5 ms for 500 deleted vectors (approximately 0.005 ms/record). Additionally, it generates an ECDSA-signed cryptographic proof as an auditable record of the deletion event.

State estimation from uncertain, distributed observations is central in many cyber-physical applications. While Distributed Particle Filtering (DPF) algorithms address nonlinear and non-Gaussian estimations in distributed settings, most solutions remain tied to specific architectures and communication assumptions, limiting adaptability in open, heterogeneous deployments-most notably, the Internet of Things (IoT). In this paper, we propose a field-based formulation of Distributed Particle Filtering grounded in Aggregate Computing (AC). By expressing estimation and information dissemination as computational fields, our approach decouples the core filtering logic from coordination and data-flow strategies. This enables systematic customisation of key design dimensions, including fusion-center placement and resilience, aggregated measurement functions, as well as the type and scope of information propagation. Through a set of in-silico experiments, we show how diverse DPF configurations can be derived within a unified framework, highlighting trade-offs among accuracy, communication cost, and robustness. Overall, the proposed approach positions AC as an effective abstraction layer for engineering adaptable DPF solutions in open IoT environments.

Creative coding libraries provide high-level tools that make computational and algorithmic art accessible to artists and learners. Processing/p5 is one such family of libraries, known for its beginner-friendly approach and wide reach across artistic and technical communities. L5 is a new member of this family, implemented in Lua using the LOVE framework. It applies permacomputing principles, a movement addressing sustainability in computing inspired by permaculture, bringing these values to a community of practice not historically centered on them. This paper explores L5's design decisions and tensions between sustainability and usability through five case studies: 1. balancing perceived simplicity versus exposing the seams, 2. designing for lower resource consumption, 3. ensuring long-term stability, 4. constraining functionality, and 5. designing documentation for resource-constrained access. Rather than optimizing for a single metric, sustainable creative tools require navigating competing values transparently.

In China, a burgeoning underground market sells citizens subscription-based censorship circumvention proxies known as ''airports''. We present the first systematic study of this ecosystem, combining user surveys, social media analysis, and active network measurements. We find that airports are by far the most popular off-the-shelf censorship circumvention tool in China, used by over half of our 1,667~survey respondents, who cite their ease of use, performance, and access to geo-restricted services like ChatGPT and Netflix. By scanning the Internet and scraping Telegram announcement channels, we identify 3,431 active airports built on a handful of open-source toolkits. We subscribe to 35 airports and characterize their performance, which often surpasses direct connections through the Great Firewall due to a distinctive multi-hop architecture. However, airports also pose new challenges and security risks: they accept payment through commercial services like Alipay, suffer frequent government takedowns, and are difficult for clients to configure optimally. Many airports also deploy their own distinct censorship policies. Airports are far more widely used than other circumvention tools from the academic literature, but introduce new forms of fragility and control, offering both lessons and opportunities for future circumvention research.

Educational resources may frame students' perceptions of who belongs in software engineering, which is relevant given the field's ongoing gender gap. However, we know little about the hidden curriculum regarding gender in online learning spaces. This study presents a critical discourse analysis of 200 manually analysed English and German software engineering tutorials on YouTube, examining gender representation through contextual domains and linguistic identity markers. Our results show that male characters and masculine linguistic defaults dominate the tutorials. We identified an agency gap, in which technical and decision-making roles are almost exclusively assigned to male actors, while female actors are either absent or tend to passive, low-agency roles. The findings indicate that software engineering education on YouTube may reproduce gendered norms, in which linguistic and representational gatekeeping may serve as a symbolic barrier to software engineering.

The growing deployment of artificial intelligence (AI) necessitates robust deep learning (DL) compilers, such as TVM and ONNX-MLIR. These compilers take as input high-level AI models, lower them through multi-layer transformations, and specialize them to diverse hardware. Testing such compilers is uniquely challenging as correctness depends on implicit constraints embedded throughout the compilation stack. Existing testing approaches largely take type constraints to restrict input model generation and therefore emphasize type validation and monitor compilation crashes or coverage gains. This focus overlooks compiler-platform interaction bugs that arise from interleaved effects across compilation and execution environments. In this work, we propose a scalable, automated DL compiler testing framework for, in tandem, (1) finding compiler-platform interaction bugs and (2) enabling behavior equivalence partitioning. Our key insight is that these bugs are caused by violated assumptions arising from interactions across compilation passes and hardware platforms. Therefore, we move beyond constraining input generation and derive full-stack constraints. Our approach is three-fold. First, we design an automated approach to extract full-stack constraints that jointly guide model generation and characterize compilation behaviors. Second, we prioritize constraints that expose interaction-sensitive behaviors, so our generated models are capable of exercising deep compilation logic. Third, we enable behavior equivalence partitioning by automatically inserting assertions to monitor distinct compilation symptoms that coverage or pass/fail signals miss. We evaluated our tool, XCheck, on three widely-used DL compilers and found 2,034 bug-revealing cases, including memory overflows, integer overflows, and silent unexpected compilations that were rooted in compiler-platform interactions.

Time-dependent partial differential equations (PDEs) arise in many engineering systems, including thermo-fluid applications. Classical numerical simulations of such systems can become computationally expensive for long-time dynamics because they typically require sequential time integration with time steps constrained by stability, accuracy, or nonlinear solvers. Although scientific machine learning provides an alternative for approximating PDE solutions, standard neural network approximations often degrade when extrapolated beyond the training time interval. In this work, we propose a steady-state-informed neural network representation for dissipative PDE systems whose solutions relax toward a stationary equilibrium. The proposed ansatz decomposes the solution into a steady-state component and a transient correction modulated by a time-dependent decay profile. When the decay profile vanishes at long time and the transient correction remains bounded, the representation embeds convergence to the prescribed steady state directly into the architecture, rather than enforcing it through an additional penalty term. This allows the network to learn the transient dynamics while preserving the correct asymptotic behavior. We implement the approach within a physics-informed neural network (PINN) framework and train the resulting model using the SOAP optimizer. The method is evaluated on a sequence of problems of increasing physical and geometric complexity, ranging from the one-dimensional heat equation to incompressible Navier-Stokes flow in a lid-driven cavity, natural convection in a square cavity, and a full three-dimensional conjugate heat transfer problem. The numerical results show that the steady-state-informed architecture substantially improves temporal extrapolation beyond the training interval compared with architectures that do not explicitly enforce the asymptotic condition.

Space-based solar power (SBSP) has recently gained renewed attention as an appealing technological advancement for providing continuous clean energy using space-based infrastructure. However, the potential of low-Earth orbit (LEO) satellite constellations for SBSP remains largely unexplored and lacks detailed simulation-based studies. In this paper, we introduce a novel LEO SBSP system model and conduct a 24-hour system-level simulation of a Walker $4\times 5$ LEO SBSP constellation at an altitude of 450\,km, beaming 2.45\,GHz microwave power to eight ground stations (GSs) under a greedy allocation policy. The model includes orbital propagation, eclipse cycles, the satellite power chain, Goubau--Brown beam coupling, ITU-R P.618 atmospheric attenuation, and onboard battery dynamics. The results confirm that the peak DC power delivered reaches 1.986\,MW, while the mean per-site delivery at the served GS ranged from 40 to 75\,kW. Two of the eight GSs received no service during the run, as their passes were consistently ranked lower under the greedy policy than competing links at the same step. The incident peak power density (PD) at the rectenna remained within the 3.35--5.72\,W/m\textsuperscript{2} range, below the International Commission on Non-Ionizing Radiation Protection (ICNIRP) general-public exposure limit. For a 20-satellite Walker LEO at this altitude, realistic per-site delivery is 50--100 kW, and the rectenna should be sized to the operational incident PD of order 5,W/m\textsuperscript{2} rather than to a Geostationary Earth Orbit (GEO)-era 100,W/m\textsuperscript{2} rating.

Testing implementations of binary protocols for specification compliance requires inputs that satisfy both structural and semantic constraints. Purely random generation and primitive mutations are often insufficient for exploring semantically meaningful behaviors in protocols that rely on Type-Length-Value (TLV) encoding, yet domain-specific compliance testing tools require deep protocol expertise and significant manual effort to construct. This work investigates whether grammar-level mutation combined with LLM-based code synthesis can serve as a viable, more generalizable approach to specification compliance testing. We evaluate the approach on PKCS#1 v1.5 signature verification -- a widely deployed TLV-encoded standard with a formally verified testing oracle (Morpheus) -- across 48 cryptographic library implementations. We reproduced 10 of 13 non-trivial specification violation categories previously identified by Morpheus, including all 5 signature forgery categories, and discovered 1 previously unreported discrepancy. We found that LLM hallucination -- occurring in 82.5% of generated scripts -- is the primary factor limiting effectiveness, not the mutation strategies. We identify five distinct hallucination types and show that their distribution varies systematically across mutation categories: structural mutations are implemented with 13.3% fidelity while constraint mutations achieve 30.3% correctness but suffer the highest rate of mutations being fully ignored (8.1%). These findings reveal a striking gap between operational reliability (99.8%) and semantic fidelity (17.5%), providing actionable guidance on when LLM-based code synthesis can be trusted in specification-driven testing pipelines.

Large foundation models deployed on third-party and shared accelerator infrastructure face a practical risk of model exfiltration that existing defenses do not fully address. In common serving deployments, model providers control the VM or bare-metal serving stack but not the surrounding hardware substrate. The host to GPU interconnect, accelerator fabric, and neighboring infrastructure components remain outside the tenant's trust boundary and have been shown to be exploitable. Hermes demonstrates lossless DNN reconstruction from passive PCIe observation, while TunnelS exfiltrates HBM contents at high throughput via driver-level access without disrupting inference. Co-tenant VMs can further access memory-mapped interfaces or misconfigured RDMA regions without physical co-location. These attacks exploit a common property of ML systems: model weights are stored in large, contiguous, and repeatedly accessed memory regions, making intercepted PCIe transfers and HBM dumps rich enough to reveal model structure and parameters. We present CloakLM, a software-only memory-obfuscation framework that removes this structural regularity without changing the inference stack's logical view of memory. CloakLM combines three mechanisms: PCIe traffic shaping, inter- and intra-layer weight shuffling, and physical HBM page remapping. Authorized execution retains a valid virtual memory layout with negligible overhead, while unauthorized observers see fragmented and semantically incoherent state. CloakLM integrates with vLLM and PyTorch, requires no hardware changes, and complements confidential computing. Evaluation on distributed inference workloads using LLaMA and Qwen models shows near-native performance while significantly increasing resistance to PCIe snooping and HBM dump attacks, making inference-time model exfiltration substantially less practical.

In service systems, customers now rely on congestion information before deciding which queue or server to join, from restaurants and theme-park attractions to road networks. We study this setting as human-in-the-loop learning (HILL), where customers both consume and generate time-sensitive congestion information through crowdsourcing platforms. Because congestion reports become stale, efficient system operation requires continued exploration of servers whose current states are uncertain. Yet selfish customers avoid such exploration when it reduces their immediate service utility, even though their observations would benefit future customers. We analyze this tension between individual incentives and system-wide learning in queueing systems with endogenous congestion. We first show that myopic server choices can induce an infinite price of anarchy (PoA): decentralized customers may cause arbitrarily large efficiency losses by overexploring servers that are likely congested. In the single-server case, we prove that the lower bound on PoA decreases as buffer size grows, while in the multi-server case the upper bound decreases as the number of servers increases. We further show that existing informational, non-monetary mechanisms for exploration-exploitation with exogenous information fail in our setting, as customers' choices directly reshape the queue states and still lead to infinite PoA. To address this challenge, we design a dynamic side-payment mechanism that periodically charges some customers and rewards others, discouraging excessive exploration while maintaining ex-post budget balance. The mechanism coordinates congestion management and information acquisition across heterogeneous servers, and guarantees PoA below 2. Beyond worst-case analysis, experiments using real datasets demonstrate that the proposed mechanism also achieves strong average-case performance.

Software performance regressions can have significant business consequences, making automated detection a critical component of modern continuous integration pipelines. At Mozilla, performance anomaly detection is handled by Perfherder, Mozilla's performance engineering management system that relies on a Student's T-test-based approach to flag regressions across hundreds of daily code changes. However, our preliminary analysis of one year of Mozilla performance data reveals that 12.5% of generated alert groups are false positives, while approximately 6.8% of them contain regressions missed by the automated system. This paper presents an empirical study evaluating 25 change-point detection (CPD) methods and 15 ensemble approaches as alternatives to Mozilla's current method. We construct a ground-truth dataset of 174 performance time series manually annotated by eleven Mozilla performance engineers, representing one of the first practitioner-annotated CPD benchmarks for performance engineering. Our results show that while offline and hybrid CPD methods improve recall over Mozilla's method, they do so at a high cost to precision. Ensemble voting strategies alleviate this trade-off and offer more consistent performance, resulting in 11% improvement in the F1-score. We validate the experimental results through a practitioner survey and report on lessons learned from integrating the best methods into Mozilla's performance engineering system.

Cybersecurity literature reviews require a reproducible denominator: the set of papers that a protocol includes before screening and synthesis begin. Today, that denominator is often reconstructed from publisher portals, bibliographic indices, and scholarly application programming interfaces (APIs) whose coverage, formats, and query semantics change over time. This paper presents TopVenues, an open-source system that materializes corpus construction as a versioned research artifact. TopVenues declares a venue and year scope, uses DBLP Computer Science Bibliography (DBLP) as the metadata spine, enriches records with abstracts and BibTeX entries via open scholarly APIs and publisher-specific extractors, and stores the results in a monotonic SQLite snapshot, accessible via a command-line interface (CLI), a web interface, and export paths for review workflows. The May 2026 snapshot contains 9,925 papers from 11 cybersecurity sources over 2017 to 2026, with 99.86% abstract coverage and 99.99% BibTeX coverage; keyword search over the full corpus completes in under 31 ms, and a 250-test suite validates the data-integrity invariants. The fixed denominator also enables repeatable measurement: 29.2% of 2024 to 2025 papers from the four top-ranked security conferences in our scope appear as arXiv preprints, with a median of five months before publication, and a prior-author-track-record filter yields a 16.5x precision gain at 90% recall for triaging preprints that later appear in the same venue set. TopVenues links corpus construction to auditable cybersecurity measurement by making the corpus itself executable, inspectable, and citable. The artifact is available at https://github.com/sidneibarbieri/topVenues.

We study dynamic geometric data structures for exact nearest-neighbour maintenance under small motions. For each point we store a certificate consisting of its nearest neighbour and the two smallest neighbour distances, with clearance $c_i=d^i_2-d^i_1$. A triangle-inequality argument gives a sharp validity radius: after a step of maximum displacement $\varepsilon$, every certificate with $c_i>4\varepsilon$ remains valid, so all possible failures are confined to a repair frontier $F_t$. We introduce repair-frontier entropy $H(F_t)$, the normalized Shannon entropy of failed certificates over index cells, as a workload descriptor for choosing between event-driven repair, batched repair, and full rebuild. The resulting maintenance rule repairs only the frontier in $O(|F_t|\log N)$ time under bounded cell occupancy, while a full rebuild costs $Θ(N)$; moreover, entropy lower-bounds the number of frontier cells touched by event-driven repair and shifts the empirical repair-rebuild crossover. We evaluate ten motion families in $d\in{2,3}$, with $N$ up to $16,000$, using an exact tiled GPU oracle and a GPU grid rebuild as ground truth and competitor. Across $2400$ labelled transitions, the validity rule misses no invalid certificate, low-pressure frontiers are usually cheaper to repair incrementally, and diffuse frontiers of the same size are more expensive for event-driven repair but not for batched repair. The released dataset records frontier geometry, certificate audits, per-strategy times, and best-strategy labels.

Property-graph schemas often contain descriptive properties that recur across heterogeneous nodes and edges, yet schema designers lack a clear method for deciding whether such properties should remain embedded or be treated as reusable metadata structures. This paper addresses this design-stage problem within a 5GNF-oriented modeling perspective by proposing a method for identifying metadata candidates based on five criteria: cross-element occurrence, conceptual independence, lossless externalization, reuse potential, and governance relevance. The method classifies properties into trait candidates, embedded properties, and borderline cases using a rule-based decision workflow. The approach is illustrated using a running example from a library domain and examined through an illustrative validation involving participant-based classification tasks in two schema contexts. The results show that recurrence alone is not a sufficient basis for externalization and that metadata-candidate identification requires semantic interpretation beyond frequency. The main contribution of the paper is methodological: it provides a more explicit and systematic basis for deciding when descriptive properties should be modeled as reusable metadata in property-graph schemas.

The purpose of this qualitative multi-case study was to examine how social bias emerges, is perceived, and can be mitigated within artificial intelligence and machine learning systems by practitioners directly involved in their design, development, and governance. Although examples from healthcare, criminal justice, employment, and education were used to illustrate domains where automated systems shape everyday life, the study focused on the lived experiences and professional insights of AI practitioners rather than sector-specific populations. Guided by Intersectionality Theory and Cognitive Science, the study employed an interpretivist approach, utilizing semi-structured interviews with nine practitioners, supplemented by document analysis and triangulated case material to enrich contextual understanding. Findings showed that algorithmic bias arises from historical inequities, exclusionary design assumptions, and organizational pressures that prioritize speed and efficiency over ethical reflection. Participants emphasized that technical corrections alone cannot ensure fairness; instead, equitable AI requires structural accountability, diverse participation, and sustained cognitive awareness during the development lifecycle. Many described limited enforcement of ethical standards and organizational cultures that inconsistently support responsible practice. The study concludes that human-centered and socially grounded AI development depends on embedding ethics early in the design process, strengthening governance frameworks, and cultivating institutional environments that encourage reflective decision-making. These insights contribute to ongoing conversations on responsible AI and offer practical guidance for organizations seeking to design systems that are transparent, accountable, and aligned with the communities they affect.

Social media platforms like TikTok have become a key source of health information, with studies reporting inaccuracies in posts. As Large Language Model (LLM) providers increasingly integrate LLMs into digital platforms to fact-check content (e.g., Grok and Perplexity on X and WhatsApp, respectively) and are being used by people to fact-check information, deploying these systems in critical areas such as reproductive health without rigorous evaluation can cause serious harm. We introduce RELIANCE, an expert-annotated dataset of health information on TikTok surrounding pregnancy and postpartum queries, serving as both an analysis of the reproductive health information landscape and an evaluation of LLMs' capabilities in fact-checking this content. Our dataset comprises 409 annotated sentences from 336 videos across 56 clinician-reviewed queries, annotated by three expert clinicians in Obstetrics, Gynecology, and Internal Medicine. Our findings reveal that nearly 60\% of the health information in the videos we sampled is accurate. Furthermore, LLM evaluations reveal a gap between evaluating specific claims and evaluating the entire content (15\%). We believe that our methodology, dataset, and tool will support the machine learning community in improving LLMs for important domains with real-world data, extending to other platforms and languages, and helping the health community further understand the information landscape on social media. Our dataset and code are made available at https://realize-lab.github.io/RELIANCE/.

We study the joint identification of network structure and governing dynamics in stochastic interacting particle systems, which consist of an unknown directed weighted interaction graph with unknown local and non-local interaction components. We formulate the problem as a coupled inverse problem for the graph and the associated basis coefficients, and develop two alternating least-squares-type estimators: a three-block scheme (TALS) and an integrated diagonal-augmented scheme (IALS). The IALS formulation combines the updates of the local and interaction coefficients into a single least-squares subproblem, and is particularly well suited to settings in which the nodewise local dynamics share a common functional template up to node-dependent scaling. We further establish an identifiability result under a rank-2 joint coercivity condition together with an appropriate normalization convention. Synthetic experiments show that the proposed estimators accurately recover both the interaction graph and the dynamical components, and remain robust under stochastic forcing, observation noise, and basis mismatch. We also provide an illustrative real-data application on ictal SEEG recordings, where the learned models produce stable and interpretable dynamical summaries across multiple basis configurations. This work advances a theoretically guaranteed scalable framework for learning stochastic interacting particle systems, with broad potential for data-driven identification in computational biology, neuroscience, and beyond.

Hypergraphs are a powerful representation for higher-order interactions but their scale and complexity pose significant data management and analysis challenges. While summarization techniques are widely used to distill simple graphs, lossless summarization for hypergraphs remains unexplored. We introduce HyDRA, the first formal framework for lossless summarization of weighted hypergraphs. In our framework, a summary is a new weighted hypergraph composed of supernodes (groups of nodes) and superhyperedges (groups of hyperedges), paired with a correction table for exact reconstruction. By establishing a conceptual link to co-clustering, we design an efficient, parameter-free greedy algorithm that iteratively merges node and hyperedge clusters to minimize a novel storage-aware cost function. HyDRA employs an incremental update strategy to prevent the costly recomputation of the correction table at each step. Extensive experiments demonstrate that \our achieves a substantial reduction in storage cost (80-93% in some settings, depending on the hypergraph characteristics). Because the resulting summaries are themselves hypergraphs, they can be queried directly, providing fast and accurate approximate answers for various connectivity and centrality queries, and accelerating downstream tasks such as influence maximization.