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Thesis Tide

Thesis Tide ranks papers based on their relevance to the fields, with the goal of making it easier to find the most relevant papers. It uses AI to analyze the content of papers and rank them!

Combining Threat Intelligence with IoT Scanning to Predict Cyber Attack

While the Web has become a worldwide platform for communication, hackers and hacktivists share their ideology and communicate with members on the "Dark Web" - the reverse of the Web. Current...

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The article presents a novel methodology for leveraging Dark Web intelligence in the context of IoT security, addressing current gaps in predicting cyber threats. The integration of IoT with threat intelligence is innovative, and the focus on a growing sector, such as IoT, adds to its relevance. However, the effectiveness and practical applicability of the proposed method require further exploration and validation.

Examples of effectivity for integral points on certain curves of genus 2

We consider families of smooth projective curves of genus 2 with a single point removed and study their integral points. We show that in many such families there is a dense set of fibres for which the...

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The paper addresses a highly specialized topic within algebraic geometry with significant implications for understanding integral points on curves of higher genus. The novelty lies in the application of degree-3 étale covers and torsion values, which suggests methodological rigor and the potential for advancing research in related areas. The results may inspire future studies on curves of higher genus and their geometric properties.

Non-uniqueness of Oberbeck-Boussinesq System with Gravitational Field

We consider the Oberbeck-Boussinesq system with gravitational force on the whole space. We prove the non-uniqueness of the system applying the unstable profile of Navier-Stokes equation.

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The article addresses the Oberbeck-Boussinesq system, an important topic in fluid dynamics, particularly in the study of buoyancy-driven flows. The demonstration of non-uniqueness is a significant contribution to the theoretical understanding of the behavior of such systems. The use of methods relating to the unstable profiles of the Navier-Stokes equation showcases methodological rigor and provides a solid basis for the findings. However, the practical implications and applicability of the results might be limited, which prevents a higher score.

MapEval: Towards Unified, Robust and Efficient SLAM Map Evaluation Framework

Evaluating massive-scale point cloud maps in Simultaneous Localization and Mapping (SLAM) remains challenging, primarily due to the absence of unified, robust and efficient evaluation frameworks. We p...

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This article presents a novel framework for evaluating point cloud maps in SLAM, addressing a significant gap in current methodologies. The introduction of new metrics, particularly the Gaussian-approximated Wasserstein distance, enhances the robustness and efficiency of map evaluations, contributing meaningfully to both theoretical understanding and practical applications in SLAM.

Measuring Emergent Capabilities of LLMs for Software Engineering: How Far Are We?

The adoption of Large Language Models (LLMs) across multiple contexts has sparked interest in understanding how scaling model size might lead to behavioral changes, as LLMs can exhibit behaviors not o...

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The study introduces a novel approach to evaluating emergent capabilities of LLMs in the Software Engineering domain, which has been largely overlooked. It employs a rigorous method to analyze behaviors across various scales and tasks, adding significant value to the existing literature. The implications for improving model interpretability and application in software tasks make it particularly impactful, although the lack of emergent behaviors found may limit immediate application.

A Practical Approach to Formal Methods: An Eclipse Integrated Development Environment (IDE) for Security Protocols

To develop trustworthy distributed systems, verification techniques and formal methods, including lightweight and practical approaches, have been employed to certify the design or implementation of se...

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This paper presents a novel and practical tool that helps bridge the gap between theoretical formal methods and their application in real-world scenarios, especially in the area of cybersecurity. Its user-centric design and evaluation provide strong evidence of its applicability and utility, particularly in educational settings. The integration of multiple formal verification tools in a user-friendly Eclipse IDE is an innovative approach that could significantly enhance the use of formal methods in practice, promoting methodological rigor in the field of security protocols.

Stability and Synchronization of Kuramoto Oscillators

Imagine a group of oscillators, each endowed with their own rhythm or frequency, be it the ticking of a biological clock, the swing of a pendulum, or the glowing of fireflies. While these individual o...

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This article presents a solid exploration of Kuramoto oscillators, emphasizing both theoretical analysis and practical applications, which makes it impactful for fields interested in synchronization phenomena. Its focus on graph theory and stability broadens its applicability and could inspire further interdisciplinary research.

AI2T: Building Trustable AI Tutors by Interactively Teaching a Self-Aware Learning Agent

AI2T is an interactively teachable AI for authoring intelligent tutoring systems (ITSs). Authors tutor AI2T by providing a few step-by-step solutions and then grading AI2T's own problem-solving at...

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This article introduces a novel approach to building intelligent tutoring systems through an interactively teachable AI, which shows significant potential for enhancing educational technology. The methodological rigor is evident in the evaluation through user studies and comparison with state-of-the-art algorithms. The self-aware learning aspect and emphasis on trustworthiness are particularly relevant in current discussions about AI applications in education. Overall, its implications for reducing authoring time while increasing reliability make it a highly impactful contribution.

A Measurement of the Water Abundance in the Atmosphere of the Hot Jupiter WASP-43b with High-resolution Cross-correlation Spectroscopy

Measuring the abundances of carbon- and oxygen-bearing molecules has been a primary focus in studying the atmospheres of hot Jupiters, as doing so can help constrain the carbon-to-oxygen (C/O) ratio. ...

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This article presents a significant advancement in the measurement of atmospheric composition of hot Jupiters, specifically WASP-43b, using high-resolution spectroscopy. The ability to detect water and impose limits on other carbon-bearing molecules contributes valuable data to the understanding of exoplanet atmospheres. The methodology employed, high-resolution cross-correlation spectroscopy, is notably rigorous and adds to the robustness of the findings. This research holds importance for theories concerning the formation and evolution of hot Jupiters, indicating its potential for high impact in the astrophysics community.

Exploring Superpixel Segmentation Methods in the Context of Citizen Science and Deforestation Detection

Tropical forests play an essential role in the planet's ecosystem, making the conservation of these biomes a worldwide priority. However, ongoing deforestation and degradation pose a significant t...

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The article addresses both a critical environmental issue (deforestation) and leverages innovative methodologies (superpixel segmentation) that prove useful in citizen science, thus combining practical application with methodological development. Its focus on enhancing a specific tool currently used in citizen science campaigns demonstrates clear applicability and potential for real-world impact. The empirical evaluation of 22 methods strengthens its contribution, although broader implications might be limited to specific tasks within remote sensing. Overall, the relevance to both environmental science and citizen science adds to its significance.

Kochen-Specker for many qubits and the classical limit

Several arguments demonstrate the incompatibility between Quantum Mechanics and classical Physics. Bell's inequalities and Greenberger-Horne-Zeilinger (GHZ) arguments apply to specific non-classic...

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The paper explores the fundamental relationship between quantum mechanics and classical physics, extending the Kochen-Specker argument to arbitrary numbers of qubits. This represents a significant advancement towards understanding the classical limit of quantum predictions, with a robust methodological foundation. Its applicability to various quantum states enhances its relevance in both theoretical physics and practical quantum computing scenarios.

Visual Complexity of Point Set Mappings

We study the visual complexity of animated transitions between point sets. Although there exist many metrics for point set similarity, these metrics are not adequate for this purpose, as they typicall...

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The article presents a novel approach to measuring visual complexity in point set transitions, addressing a significant gap in existing metrics that typically assess point sets individually. The introduction of group translations as a single operation is innovative and could lead to new methodologies in related fields. The thorough analysis, including NP-hardness discussions and polynomial-time solutions, adds rigor to the research, enhancing its applicability.

Compositional and Interface Engineering of Hybrid Metal Halide Perovskite Thin Films for Solar Cells

Perovskite solar cells (PSCs) are the fastest-growing photovoltaic (PV) technology in the solar cell community and have reached an efficiency close to that of commercial silicon (Si) solar cells. The ...

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The paper provides significant insights into the compositional and interface engineering of hybrid metal halide perovskite solar cells (PSCs), which is highly relevant in advancing current photovoltaic technologies. The novelty of integrating self-assembled monolayers with conjugated polyelectrolytes introduces a new strategy that could greatly influence the efficiency and stability of PSCs. Additionally, the focus on device physics and interfacial defects showcases methodological rigor that could inspire future research in enhancing PSC performance.

Generalized torsion elements in infinite groups

A group element is called generalized torsion if a finite product of its conjugates is equal to the identity. We show that in a finitely generated abelian-by-finite group, an element is generalized to...

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The paper presents a novel exploration of generalized torsion elements within the context of infinite groups, specifically focusing on finitely generated abelian-by-finite groups. The findings clarify the relationship between elements of the group and their images in the abelianization, addressing a potentially significant gap in the understanding of group theory. The inclusion of sharp bounds on the generalized exponent and numerous examples adds methodological rigor and applicability to the topic, enhancing its relevance for future research in group theory, particularly concerning torsion properties and identities. The rigorous approach and identifiable classes of groups reflect a strong potential for influencing subsequent studies and applications within the mathematical discipline.

DECODE: Domain-aware Continual Domain Expansion for Motion Prediction

Motion prediction is critical for autonomous vehicles to effectively navigate complex environments and accurately anticipate the behaviors of other traffic participants. As autonomous driving continue...

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This article presents a highly innovative approach to motion prediction in autonomous vehicles, showcasing strong methodological rigor through the integration of continual learning with specialization and generalization dynamics. The use of hypernetworks and Bayesian uncertainty estimation adds significant novelty, potentially influencing future research in machine learning and robotics.

Tidally Delayed Spin-Down of Very Low Mass Stars

Very low-mass main-sequence stars reveal some curious trends in observed rotation period distributions that require abating the spin-down that standard rotational evolution models would otherwise impl...

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This article presents a novel approach to understanding the rotation period distribution of very low-mass stars through the interaction of tidal forces and magnetic spin-down, filling a significant gap in current astrophysical models. The rigorous methodology and implications for gyrochronology and stellar evolution suggest high potential impact on the field. Its interdisciplinary implications also enhance its relevance.

Stochastic SketchRefine: Scaling In-Database Decision-Making under Uncertainty to Millions of Tuples

Decision making under uncertainty often requires choosing packages, or bags of tuples, that collectively optimize expected outcomes while limiting risks. Processing Stochastic Package Queries (SPQs) i...

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The article presents novel methodologies, namely risk-constraint linearization and the Stochastic SketchRefine framework, which significantly improve the scalability of decision-making processes under uncertainty. This addresses a key challenge in the field of data management and optimization, offering a substantial advancement over existing methods. The demonstrated efficiency in handling large datasets with high variance adds to its robustness. The methodological rigor is strengthened through experimental validation, which establishes the practical applicability of the proposed solutions.

Enhancing Project Performance Forecasting using Machine Learning Techniques

Accurate forecasting of project performance metrics is crucial for successfully managing and delivering urban road reconstruction projects. Traditional methods often rely on static baseline plans and ...

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This article addresses a significant gap in traditional project management by applying machine learning techniques to enhance performance forecasting, which is novel and critically relevant in an era increasingly reliant on data analytics. The methodological rigor is evident in the use of both ARIMA and LSTM networks, providing robust comparative insights into forecasting accuracy. Furthermore, the inclusion of external factors in the modeling process enhances applicability, making this research timely and impactful for practitioners in the field.

CrypQ: A Database Benchmark Based on Dynamic, Ever-Evolving Ethereum Data

Modern database systems are expected to handle dynamic data whose characteristics may evolve over time. Many popular database benchmarks are limited in their ability to evaluate this dynamic aspect of...

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The article introduces a novel database benchmark, CrypQ, specifically designed to address the limitations of existing benchmarks in handling dynamic data. Its focus on real-world, evolving Ethereum data enhances its relevance, applicability, and methodological rigor. The paper's contribution can significantly improve the performance evaluations of database systems in contexts involving dynamic and unpredictable datasets, making it a substantial advancement in the field.

Can LLMs plan paths in the real world?

As large language models (LLMs) increasingly integrate into vehicle navigation systems, understanding their path-planning capability is crucial. We tested three LLMs through six real-world path-planni...

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The study addresses a timely issue regarding the integration of LLMs in real-world applications, particularly navigation. The empirical testing provides valuable insights into their limitations, encouraging further investigation in model improvement and application strategies. Its explicit suggestions for future work (reality checks, transparency, smaller models) add to its impact potential.