2489 papers
This survey provides a comprehensive review of over 200 papers on Wi-Fi sensing generalizability, offering a structured taxonomy of techniques to address domain shifts, summarizing key datasets, and outlining future research directions and community resources.
This study demonstrates that gender biases in leadership perceptions, where male managers are favored and female managers face greater skepticism upon making unfavorable decisions, extend from human leaders to AI managers, highlighting the need to address these prejudices in the design of AI-driven organizational systems.
This paper introduces iMarkers, a novel class of invisible fiducial markers detectable only by robots and AR devices, which overcome the visual aesthetic limitations of traditional markers while offering customizable production, robust detection algorithms, and proven effectiveness across diverse robotics scenarios.
This paper introduces Kite, a protocol that enables private delegation of voting power in Decentralized Autonomous Organizations (DAOs) by allowing voters to delegate, revoke, or re-delegate their votes without revealing delegate identities to anyone, including the delegates themselves, while maintaining public verifiability and demonstrating practical implementation on Ethereum.
This study employs text-mining techniques to analyze 160 guidelines and policy statements across fourteen industrial sectors, offering critical insights and recommendations for balancing innovation with ethical accountability in the governance of Generative AI and Large Language Models.
This paper introduces ARSGaussian, a novel view synthesis method for aerial remote sensing that integrates LiDAR constraints, distortion-aware coordinate transformations, and geometric consistency losses to mitigate floaters and overgrowth while achieving high-precision geo-alignment, supported by the newly released AIR-LONGYAN dataset.
This study involving 72 participants across three languages demonstrates that while nominal groups serve as a crucial benchmark for evaluating collaborative virtual environments, 3D graph representations in mixed reality do not inherently yield better collaborative problem-solving outcomes than individual performance.
This paper presents a taxonomy of nine log smells derived from a survey of 51 studies to help developers write better logging code, while also mapping these issues to existing repair tools and highlighting critical gaps in current research and tooling.
This paper proposes a budget-efficient active prompt learning framework for vision-language models that utilizes class-guided clustering and adaptive class-wise thresholding to achieve higher accuracy with fewer labeled samples compared to existing baselines.
This study investigates the pitfalls of integrating large language models into software engineering workflows, revealing that persistent inaccuracies and cognitive overload often lead developers to abandon AI tools, though strategic prompt refinement can significantly mitigate the risk of such abandonment.
The paper introduces OWL-TAMP, a novel framework that integrates Vision-Language Models into Task and Motion Planning systems to generate language-parameterized discrete and continuous constraints, enabling robots to solve complex, long-horizon manipulation tasks specified in natural language within open-world environments.
This paper introduces VisPoison, a backdoor attack framework that exploits text-to-visualization models for tabular data by using stealthy triggers to cause data exposure, misleading visualizations, or denial-of-service failures with over 90% success rates, thereby highlighting critical security vulnerabilities in current systems and the inadequacy of existing defenses.
TIMotion is an efficient framework for human-human motion generation that improves upon existing methods by introducing Causal Interactive Injection, Role-Evolving Scanning, and Localized Pattern Amplification to better model temporal dynamics and interactive roles, thereby achieving superior performance on benchmark datasets.
This paper presents a longitudinal study of GPT, Llama, and Qwen models, revealing that continuous updates and increased model sizes do not consistently enhance adversarial robustness against misclassification, jailbreaks, and hallucinations, and can sometimes exacerbate existing vulnerabilities.
This paper introduces BOPIM, a Bayesian Optimization framework for influence maximization on temporal networks that utilizes novel kernel functions and a greedy acquisition strategy to achieve superior performance and significant speed improvements over existing methods while uniquely quantifying uncertainty in optimal seed sets.
This paper presents a comprehensive overview of 5G network slicing as a key architectural enabler for efficient resource utilization, focusing specifically on its business aspects through profit modeling for both own-slice implementation and resource leasing, while also addressing existing challenges and future research directions.
This study analyzes NBA play-by-play and Last Two Minutes data to find that while referees exhibit a home-court advantage bias (which has decreased since the pandemic) and show statistical favoritism toward specific players, there is no evidence of negative bias against particular players, teams, or racial groups.
This paper proposes a framework using Poisson regression with censored data filtering to accurately estimate excess demand in public transportation systems, addressing the common issue of underestimation caused by unrecorded passengers left behind on full buses, and validates the approach using simulated data and real-world data from Pittsburgh's Port Authority.
By combining data from a representative sample of 5,000 American adults with breach records from Have I Been Pwned, the study estimates that at least 82.84% of Americans have experienced an account breach, with an average of at least three breaches per person, a risk that is higher among women, White individuals, the middle-aged, and those with higher education levels.
This paper introduces a general framework that extends Knowledge Compilation to Satisfiability Modulo Theories (SMT) by combining input formulas with pre-computed theory lemmas to enable polytime propositional-style querying of compiled SMT d-DNNFs.