arXiv subjects

Longfei Chen

Publications and source records attributed to Longfei Chen.

At least 19 recordsLinked to original sources

arXiv2026

ConSearcher: Supporting Conversational Information Seeking in Online Communities with Member Personas

Many people browse online communities to learn from others' experiences and opinions, e.g., for constructing travel plans. Conversational search powered by large language models (LLMs) could ease this information-seeking task, but it remains under-investigated within the online community. In this paper, we first conducted an exploratory study (N=10) that indicated the helpfulness of a classic conversational search tool and identified room for improvement. Then, we proposed ConSearcher, an LLM-powered tool with dynamically generated member personas based on user queries to facilitate conversational search in the community. In ConSearcher, users can clarify their interests by checking what a simulated member similar to them may ask and get responses from diverse members' perspectives. A within-subjects study (N=27) showed that compared to two conversational search baselines, ConSearcher led to significantly higher information-seeking outcome and user engagement but raised concerns about over-personalization. We discuss implications for supporting conversational information seeking in online communities.

cs.HC↗

arXiv2026

SecCodeBench-V2 Technical Report

We introduce SecCodeBench-V2, a publicly released benchmark for evaluating Large Language Model (LLM) copilots' capabilities of generating secure code. SecCodeBench-V2 comprises 98 generation and fix scenarios derived from Alibaba Group's industrial productions, where the underlying security issues span 22 common CWE (Common Weakness Enumeration) categories across five programming languages: Java, C, Python, Go, and JavaScript. SecCodeBench-V2 adopts a function-level task formulation: each scenario provides a complete project scaffold and requires the model to implement or patch a designated target function under fixed interfaces and dependencies. For each scenario, SecCodeBench-V2 provides executable proof-of-concept (PoC) test cases for both functional validation and security verification. All test cases are authored and double-reviewed by security experts, ensuring high fidelity, broad coverage, and reliable ground truth. Beyond the benchmark itself, we build a unified evaluation pipeline that assesses models primarily via dynamic execution. For most scenarios, we compile and run model-generated artifacts in isolated environments and execute PoC test cases to validate both functional correctness and security properties. For scenarios where security issues cannot be adjudicated with deterministic test cases, we additionally employ an LLM-as-a-judge oracle. To summarize performance across heterogeneous scenarios and difficulty levels, we design a Pass@K-based scoring protocol with principled aggregation over scenarios and severity, enabling holistic and comparable evaluation across models. Overall, SecCodeBench-V2 provides a rigorous and reproducible foundation for assessing the security posture of AI coding assistants, with results and artifacts released at https://alibaba.github.io/sec-code-bench. The benchmark is publicly available at https://github.com/alibaba/sec-code-bench.

cs.CR↗

arXiv2026

Echo: Towards Advanced Audio Comprehension via Audio-Interleaved Reasoning

The maturation of Large Audio Language Models (LALMs) has raised growing expectations for them to comprehend complex audio much like humans. Current efforts primarily replicate text-based reasoning by contextualizing audio content through a one-time encoding, which introduces a critical information bottleneck. Drawing inspiration from human cognition, we propose audio-interleaved reasoning to break through this bottleneck. It treats audio as an active reasoning component, enabling sustained audio engagement and perception-grounded analysis. To instantiate it, we introduce a two-stage training framework, first teaching LALMs to localize salient audio segments through supervised fine-tuning, and then incentivizing proficient re-listening via reinforcement learning. In parallel, a structured data generation pipeline is developed to produce high-quality training data. Consequently, we present Echo, a LALM capable of dynamically re-listening to audio in demand during reasoning. On audio comprehension benchmarks, Echo achieves overall superiority in both challenging expert-level and general-purpose tasks. Comprehensive analysis further confirms the efficiency and generalizability of audio-interleaved reasoning, establishing it as a promising direction for advancing audio comprehension. Project page: https://github.com/wdqqdw/Echo.

cs.SD↗

arXiv2025

SmartPoC: Generating Executable and Validated PoCs for Smart Contract Bug Reports

Smart contracts are commonly audited through static analysis to explore vulnerabilities. However, static approaches typically produce heterogeneous findings rather than reproducible, executable proof-of-concept (PoC) test cases, leading to costly and ad hoc manual validation. Large language models (LLMs) offer a promising way to translate audit reports into PoC test cases, but face three major challenges: noisy inputs, lack of execution grounding, and missing runtime oracles. We present SmartPoC, an end-to-end approach for validating reported vulnerabilities in audit reports by generating and executing PoC test cases with automated exploitability verification. SmartPoC first extracts a focused function-level slice from each report to reduce noise, centering on the key functions referenced in a finding and augmenting them with execution-relevant neighbors. To improve executability, we wrap LLM-based PoC synthesis in a generate-repair-execute loop, combining deterministic pre-execution sanitization with feedback-driven post-execution debugging. We further use differential verification as an oracle to confirm the exploitability of generated test cases. On the SmartBugs-Vul and FORGE-Vul benchmarks, SmartPoC achieves confirmation precision of 98.32% and 98.65%, with recall of 84.17% and 85.28%, respectively. On a recent Etherscan verified-source corpus, SmartPoC confirms 64 bugs from 545 audit findings at an average cost of $0.03.

cs.SE↗

arXiv2025

QueryGenie: Making LLM-Based Database Querying Transparent and Controllable

Conversational user interfaces powered by large language models (LLMs) have significantly lowered the technical barriers to database querying. However, existing tools still encounter several challenges, such as misinterpretation of user intent, generation of hallucinated content, and the absence of effective mechanisms for human feedback-all of which undermine their reliability and practical utility. To address these issues and promote a more transparent and controllable querying experience, we proposed QueryGenie, an interactive system that enables users to monitor, understand, and guide the LLM-driven query generation process. Through incremental reasoning, real-time validation, and responsive interaction mechanisms, users can iteratively refine query logic and ensure alignment with their intent.

cs.HC↗

arXiv2025

Evaluating Personalized Beneficial Interventions in the Daily Lives of Older Adults Using a Camera

Beneficial daily activity interventions have been shown to improve both the physical and mental health of older adults. However, there is a lack of robust objective metrics and personalized strategies to measure their impact. In this study, two older adults aged over 65, living in Edinburgh, UK, selected their preferred daily interventions (mindful meals and art crafts), which are then assessed for effectiveness. The total monitoring period across both participants was 8 weeks. Their physical behaviours were continuously monitored using a non-contact, privacy-preserving camera-based system. Postural and mobility statistics were extracted using computer vision algorithms and compared across periods with and without the interventions. The results demonstrate significant behavioural changes for both participants, highlighting the effectiveness of both these activities and the monitoring system.

cs.HC↗

arXiv2025

DancingBoard: Streamlining the Creation of Motion Comics to Enhance Narratives

Motion comics, a digital animation format that enhances comic book narratives, have wide applications in storytelling, education, and advertising. However, their creation poses significant challenges for amateur creators, primarily due to the need for specialized skills and complex workflows. To address these issues, we conducted an exploratory survey (N=58) to understand the challenges associated with creating motion comics, and an expert interview (N=4) to identify a typical workflow for creation. We further analyzed $95$ online motion comics to gain insights into the design space of character and object actions. Based on our findings, we proposed DancingBoard, an integrated authoring tool designed to simplify the creation process. This tool features a user-friendly interface and a guided workflow, providing comprehensive support throughout each step of the creation process. A user study involving 23 creators showed that, compared to professional tools, DancingBoard is easily comprehensible and provides improved guidance and support, requiring less effort from users. Additionally, a separate study with $18$ audience members confirmed the tool's effectiveness in conveying the story to its viewers.

cs.HC↗

arXiv2023

Towards Better Modeling with Missing Data: A Contrastive Learning-based Visual Analytics Perspective

Missing data can pose a challenge for machine learning (ML) modeling. To address this, current approaches are categorized into feature imputation and label prediction and are primarily focused on handling missing data to enhance ML performance. These approaches rely on the observed data to estimate the missing values and therefore encounter three main shortcomings in imputation, including the need for different imputation methods for various missing data mechanisms, heavy dependence on the assumption of data distribution, and potential introduction of bias. This study proposes a Contrastive Learning (CL) framework to model observed data with missing values, where the ML model learns the similarity between an incomplete sample and its complete counterpart and the dissimilarity between other samples. Our proposed approach demonstrates the advantages of CL without requiring any imputation. To enhance interpretability, we introduce CIVis, a visual analytics system that incorporates interpretable techniques to visualize the learning process and diagnose the model status. Users can leverage their domain knowledge through interactive sampling to identify negative and positive pairs in CL. The output of CIVis is an optimized model that takes specified features and predicts downstream tasks. We provide two usage scenarios in regression and classification tasks and conduct quantitative experiments, expert interviews, and a qualitative user study to demonstrate the effectiveness of our approach. In short, this study offers a valuable contribution to addressing the challenges associated with ML modeling in the presence of missing data by providing a practical solution that achieves high predictive accuracy and model interpretability.

cs.LG↗

arXiv2023

Amplifying the Music Listening Experience through Song Comments on Music Streaming Platforms

Music streaming services are increasingly popular among younger generations who seek social experiences through personal expression and sharing of subjective feelings in comments. However, such emotional aspects are often ignored by current platforms, which affects the listeners' ability to find music that triggers specific personal feelings. To address this gap, this study proposes a novel approach that leverages deep learning methods to capture contextual keywords, sentiments, and induced mechanisms from song comments. The study augments a current music app with two features, including the presentation of tags that best represent song comments and a novel map metaphor that reorganizes song comments based on chronological order, content, and sentiment. The effectiveness of the proposed approach is validated through a usage scenario and a user study that demonstrate its capability to improve the user experience of exploring songs and browsing comments of interest. This study contributes to the advancement of music streaming services by providing a more personalized and emotionally rich music experience for younger generations.

cs.HC↗

arXiv2023

FSLens: A Visual Analytics Approach to Evaluating and Optimizing the Spatial Layout of Fire Stations

The provision of fire services plays a vital role in ensuring the safety of residents' lives and property. The spatial layout of fire stations is closely linked to the efficiency of fire rescue operations. Traditional approaches have primarily relied on mathematical planning models to generate appropriate layouts by summarizing relevant evaluation criteria. However, this optimization process presents significant challenges due to the extensive decision space, inherent conflicts among criteria, and decision-makers' preferences. To address these challenges, we propose FSLens, an interactive visual analytics system that enables in-depth evaluation and rational optimization of fire station layout. Our approach integrates fire records and correlation features to reveal fire occurrence patterns and influencing factors using spatiotemporal sequence forecasting. We design an interactive visualization method to explore areas within the city that are potentially under-resourced for fire service based on the fire distribution and existing fire station layout. Moreover, we develop a collaborative human-computer multi-criteria decision model that generates multiple candidate solutions for optimizing firefighting resources within these areas. We simulate and compare the impact of different solutions on the original layout through well-designed visualizations, providing decision-makers with the most satisfactory solution. We demonstrate the effectiveness of our approach through one case study with real-world datasets. The feedback from domain experts indicates that our system helps them to better identify and improve potential gaps in the current fire station layout.

cs.HC↗

arXiv2022

Corrected Navier-Stokes equations for compressible flows

For gas flows, the Navier-Stokes (NS) equations are established by mathematically expressing conservations of mass, momentum and energy. The advantage of the NS equations over the Euler equations is that the NS equations have taken into account the viscous stress caused by the thermal motion of molecules. The viscous stress arises from applying Isaac Newton's second law to fluid motion, together with the assumption that the stress is proportional to the gradient of velocity1. Thus, the assumption is the only empirical element in the NS equations, and this is actually the reason why the NS equations perform poorly under special circumstances. For example, the NS equations cannot describe rarefied gas flows and shock structure. This work proposed a correction to the NS equations with an argument that the viscous stress is proportional to the gradient of momentum when the flow is under compression, with zero additional empirical parameters. For the first time, the NS equations have been capable of accurately solving shock structure and rarefied gas flows. In addition, even for perfect gas, the accuracy of the prediction of heat flux rate is greatly improved. The corrected NS equations can readily be used to improve the accuracy in the computation of flows with density variations which is common in nature.

physics.flu-dyn↗

arXiv2022

RISeer: Inspecting the Status and Dynamics of Regional Industrial Structure via Visual Analytics

Restructuring the regional industrial structure (RIS) has the potential to halt economic recession and achieve revitalization. Understanding the current status and dynamics of RIS will greatly assist in studying and evaluating the current industrial structure. Previous studies have focused on qualitative and quantitative research to rationalize RIS from a macroscopic perspective. Although recent studies have traced information at the industrial enterprise level to complement existing research from a micro perspective, the ambiguity of the underlying variables contributing to the industrial sector and its composition, the dynamic nature, and the large number of multivariant features of RIS records have obscured a deep and fine-grained understanding of RIS. To this end, we propose an interactive visualization system, RISeer, which is based on interpretable machine learning models and enhanced visualizations designed to identify the evolutionary patterns of the RIS and facilitate inter-regional inspection and comparison. Two case studies confirm the effectiveness of our approach, and feedback from experts indicates that RISeer helps them to gain a fine-grained understanding of the dynamics and evolution of the RIS.

cs.HC↗

arXiv2022

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -- VIII. A search for hot cores by using C$_2$H$_5$CN, CH$_3$OCHO and CH$_3$OH lines

Hot cores characterized by rich lines of complex organic molecules are considered as ideal sites for investigating the physical and chemical environments of massive star formation. We present a search for hot cores by using typical nitrogen- and oxygen-bearing complex organic molecules (C$_2$H$_5$CN, CH$_3$OCHO and CH$_3$OH), based on ALMA Three-millimeter Observations of Massive Star-forming regions (ATOMS). The angular resolutions and line sensitivities of the ALMA observations are better than 2 arcsec and 10 mJy/beam, respectively. A total of 60 hot cores are identified with 45 being newly detected, in which the complex organic molecules have high gas temperatures ($>$ 100 K) and small source sizes ($<$ 0.1 pc). So far this is the largest sample of hot cores observed with similar angular resolution and spectral coverage. The observations have also shown nitrogen and oxygen differentiation in both line emission and gas distribution in 29 hot cores. Column densities of CH$_3$OH and CH$_3$OCHO increase as rotation temperatures rise. The column density of CH$_3$OCHO correlates tightly with that of CH$_3$OH. The pathways for production of different species are discussed. Based on the spatial position difference between hot cores and UC~H{\sc ii} regions, we conclude that 24 hot cores are externally heated while the other hot cores are internally heated. The observations presented here will potentially help establish a hot core template for studying massive star formation and astrochemistry.

astro-ph.GA↗

arXiv2021

Precise Measurements of CH Maser Emission and Its Abundance in Translucent Clouds

We present high-sensitivity CH 9 cm ON/OFF observations toward 18 extra-galactic continuum sources that have been detected with OH 18 cm absorption in the Millennium survey with the Arecibo telescope. CH emission was detected toward six of eighteen sources. The excitation temperature of CH has been derived directly through analyzing all detected ON and OFF velocity components. The excitation temperature of CH 3335 MHz transition ranges from $-54.5$ to $-0.4$ K and roughly follows a log-normal distribution peaking within [$-$5, 0] K, which implies overestimation by 20% to more than ten times during calculating CH column density by assuming the conventional value of $-60$ or $-10$ K. Furthermore, the column density of CH would be underestimated by a factor of $1.32\pm 0.03$ when adopting local thermal equilibrium (LTE) assumption instead of using the CH three hyperfine transitions. We found a correlation between the column density of CH and OH following log$N$(CH) = (1.80$\pm$ 0.49) log$N$(OH) $-11.59 \pm 6.87$. The linear correlation between the column density of CH and H$_2$ is consistent with that derived from visible wavelengths studies, confirming that CH is one of the best tracers of H$_2$ component in diffuse molecular gas.

astro-ph.GA↗

arXiv2021

Studying Infall in Infrared Dark Clouds with Multiple HCO+ Transitions

We investigate the infall properties in a sample of 11 infrared dark clouds (IRDCs) showing blue-asymmetry signatures in HCO$^{+}$ J=1--0 line profiles. We used JCMT to conduct mapping observations in HCO$^{+}$ J=4--3 as well as single-pointing observations in HCO$^{+}$ J =3--2, towards 23 clumps in these IRDCs. We applied the HILL model to fit these observations and derived infall velocities in the range of 0.5-2.7 km s$^{-1}$, with a median value of 1.0 km s$^{-1}$, and obtained mass accretion rates of 0.5-14$\times$10$^{-3}$ Msun yr$^{-1}$. These values are comparable to those found in massive star forming clumps in later evolutionary stages. These IRDC clumps are more likely to form star clusters. HCO$^{+}$ J =3--2 and HCO$^{+}$ J =1--0 were shown to trace infall signatures well in these IRDCs with comparable inferred properties. HCO$^{+}$ J=4--3, on the other hand, exhibits infall signatures only in a few very massive clumps, due to smaller opacties. No obvious correlation for these clumps was found between infall velocity and the NH3/CCS ratio.

astro-ph.GA↗

arXiv2021

The TMRT K Band Observations towards 26 Infrared Dark Clouds: NH$_{3}$, CCS, and HC$_{3}$N

We present one of the first Shanghai Tian Ma Radio Telescope (TMRT) K Band observations towards a sample of 26 infrared dark clouds (IRDCs). We observed the (1,1), (2,2), (3,3), and (4,4) transitions of NH$_{3}$ together with CCS (2$_{1}$-1$_{0}$) and HC$_{3}$N $J\,$=2-1, simultaneously. The survey dramatically increases the existing CCS-detected IRDC sample from 8 to 23, enabling a better statistical study of the ratios of carbon-chain molecules (CCM) to N-bearing molecules in IRDCs. With the newly developed hyperfine group ratio (HFGR) method of fitting NH$_{3}$ inversion lines, we found the gas temperature to be between 10 and 18 K. The column density ratios of CCS to NH$_{3}$ for most of the IRDCs are less than 10$^{-2}$, distinguishing IRDCs from low-mass star-forming regions. We carried out chemical evolution simulations based on a three-phase chemical model NAUTILUS. Our measurements of the column density ratios between CCM and NH$_{3}$ are consistent with chemical evolutionary ages of $\lesssim$10$^{5}$ yr in the models. Comparisons of the data and chemical models suggest that CCS, HC$_{3}$N, and NH$_{3}$ are sensitive to the chemical evolutionary stages of the sources.

astro-ph.GA↗

arXiv2020

LES and finite-volume CMC modelling of a turbulent lifted H2/N2 flame: effects of CMC mesh resolution and numerical scheme

Large eddy simulations with three-dimensional finite-volume Conditional Moment Closure (CMC) model are performed for a hydrogen / nitrogen lifted flame with detailed chemical meachanism. The emphasis is laid on the influences of mesh resolution and convection scheme of finite-volume CMC model on predictions of reactive scalar distribution and unsteady flame dynamics. The results show that the lift-off height is underestimated and the reactive scalars (e.g. temperature, H2 and OH) are over-predicted with coarser CMC mesh. It is also found that further refinement of the CMC mesh would not considerably improve the results. The time sequences of the most reactive and stoichiometric OH mass fractions indicate that finer CMC mesh can capture more unsteady details than coarser CMC mesh. Moreover, the coarse CMC mesh has lower conditional scalar dissipation rate, which would promote the ealier auto-ignition of the flame base. Besides, the effects of the convection schemes in the CMC equations on the lifted flame characteristics are also investigated. It is shown that different convection schemes lead to limited differences on the time-averaged temperature, mixture fraction and species mass fractions. Moreover, the RMS values of H2 and OH mass fractions show larger deviation from the measurements with hybrid upwind and central differencing scheme, especially around the flame base. Furthermore, the distributions of the numerical flux on the CMC faces also show obvious distinction between the upwind scheme and the blending scheme. The budget analysis of the individual CMC terms shows that a sequence of CMC faces has comparable contributions with upwind scheme. However, with the hybrid schemes, the instantaneous flux is dominantly from limited CMC faces. The reactivity of a CMC cell is more easily to be affected by its neighbors when the upwind scheme is used.

physics.flu-dyn↗

arXiv2020

OH Evolution in Molecular Clouds

We have conducted OH 18 cm survey toward 141 molecular clouds in various environments, including 33 optical dark clouds, 98 Planck Galactic cold clumps (PGCCs) and 10 Spitzer dark clouds with the Arecibo telescope. The deviations from local thermal equilibrium are common for intensity ratios of both OH main lines and satellite lines. Line intensity of OH 1667 MHz is found to correlate linearly with visual extinction $A\rm_V$ when $A\rm_V$ is less than 3 mag. It was converted into OH column density by adopting excitation temperature derived from Monte Carlo simulations with one sigma uncertainty. The relationship between OH abundance $X$(OH) relative to H$_2$ and $A\rm_V$ is found to follow an empirical formula, \begin{equation} \nonumber \frac{X(\textrm{OH})}{10^{-7}} = 1.3^{+0.4}_{-0.4} + 6.3^{+0.5}_{-0.5}\times \textrm{exp}(-\frac{A_\textrm{V}}{2.9^{+0.6}_{-0.6}}). \end{equation} Linear correlation is found between OH and $^{13}$CO intensity. Besides, nonthermal velocity dispersions of OH and $^{13}$CO are closely correlated. These results imply tight chemical evolution and spatial occupation between OH and $^{13}$CO. No obvious correlation is found between column density and nonthermal velocity dispersion of OH and HI Narrow Self-Absorption (HINSA), indicating different chemical evolution and spatial volume occupation between OH and HINSA. Using the age information of HINSA analysis, OH abundance $X$(OH) is found to increase linearly with cloud age, which is consistent with previous simulations. Fourteen OH components without corresponding CO emission were detected, implying the effectiveness of OH in tracing the `CO-dark' molecular gas.

astro-ph.GA↗