Security practitioners maintain vulnerability reports (e.g., GitHub Advisory) to help developers mitigate security risks. An important task for these databases is automatically extracting structured information mentioned in the report, e.g., the affected software packages, to accelerate the defense of the vulnerability ecosystem. However, it is challenging for existing work on affected package identification to achieve a high accuracy. One reason is that all existing work focuses on relatively smaller models, thus they cannot harness the knowledge and semantic capabilities of large language models. To address this limitation, we propose VulLibGen, the first method to use LLM for affected package identification. In contrast to existing work, VulLibGen proposes the novel idea to directly generate the affected package. To improve the accuracy, VulLibGen employs supervised fine-tuning (SFT), retrieval augmented generation (RAG) and a local search algorithm. The local search algorithm is a novel postprocessing algorithm we introduce for reducing the hallucination of the generated packages. Our evaluation results show that VulLibGen has an average accuracy of 0.806 for identifying vulner
The aim of this research review is to propose the logic and search mechanism for the development of an artificially intelligent automaton (AIA) that can find affected cells in a 3-dimensional biological system. Research on the possible application of such automatons to detect and control cancer cells in the human body are greatly focused MRI and PET scans finds the affected regions at the tissue level even as we can find the affected regions at the cellular level using the framework. The AIA may be designed to ensure optimum utilization as they record and might control the presence of affected cells in a human body. The proposed models and techniques can be generalized and used in any application where cells are injured or affected by some disease or accident. The best method to import AIA into the body without surgery or injection is to insert small pill like automata, carrying material viz drugs or leukocytes that is needed to correct the infection. In this process, the AIA can be compared to nano pills to deliver or support therapy. NanoHive simulation software was used to validate the framework of this paper. The existing nanomedicine models such as obstacle avoidance algorithm
This paper investigates how the speed of code review is affected by the code quality, activity and usage in the context of MediaWiki extensions. The median time to merge is compared against several other variables which are collected using a variety of manual methods and APIs. The results are graphed where possible and statistical analysis is used to determine the significance of the results. The paper finds that the number of reviewers voting on code and whether the extension has a steward affects the median time to merge. Finally, conclusions are drawn and further research topics are recommended.
We study the collective motion of self-propelled particles affected by the spatial-dependent noise based on the Vicsek rules. Only the particles inside the special region will affected by noise. The consideration of the spatial-dependent noise is closer to reality because of the complexity of the environment. Interestingly, we find that there exists an optimal amplitude of noise to adjust the average motional direction of the system. Particular orientation of the noisy region makes the motional direction of the system parallel to the orientation of the noisy region. The adjustment of the motional direction of the system also depends on the shape, the proportion and the spatial distribution of the noisy region. Our findings may inspire the capture of the key features of collective motion underlying various phenomena.
The fundamental problem of causal inference -- that we never observe counterfactuals -- prevents us from identifying how many might be negatively affected by a proposed intervention. If, in an A/B test, half of users click (or buy, or watch, or renew, etc.), whether exposed to the standard experience A or a new one B, hypothetically it could be because the change affects no one, because the change positively affects half the user population to go from no-click to click while negatively affecting the other half, or something in between. While unknowable, this impact is clearly of material importance to the decision to implement a change or not, whether due to fairness, long-term, systemic, or operational considerations. We therefore derive the tightest-possible (i.e., sharp) bounds on the fraction negatively affected (and other related estimands) given data with only factual observations, whether experimental or observational. Naturally, the more we can stratify individuals by observable covariates, the tighter the sharp bounds. Since these bounds involve unknown functions that must be learned from data, we develop a robust inference algorithm that is efficient almost regardless of
Two recent studies explicitly recommend labeling defective classes in releases using the affected versions (AV) available in issue trackers. The aim our study is threefold: 1) to measure the proportion of defects for which the realistic method is usable, 2) to propose a method for retrieving the AVs of a defect, thus making the realistic approach usable when AVs are unavailable, 3) to compare the accuracy of the proposed method versus three SZZ implementations. The assumption of our proposed method is that defects have a stable life cycle in terms of the proportion of the number of versions affected by the defects before discovering and fixing these defects. Results related to 212 open-source projects from the Apache ecosystem, featuring a total of about 125,000 defects, reveal that the realistic method cannot be used in the majority (51%) of defects. Therefore, it is important to develop automated methods to retrieve AVs. Results related to 76 open-source projects from the Apache ecosystem, featuring a total of about 6,250,000 classes, affected by 60,000 defects, and spread over 4,000 versions and 760,000 commits, reveal that the proportion of the number of versions between defect
We consider the problem of designing control protocols for nonlinear network systems affected by heterogeneous, time-varying delays and disturbances. For these networks, the goal is to reject polynomial disturbances affecting the agents and to guarantee the fulfilment of some desired network behaviour. To satisfy these requirements, we propose an integral control design implemented via a multiplex architecture. We give sufficient conditions for the desired disturbance rejection and stability properties by leveraging tools from contraction theory. We illustrate the effectiveness of the results via a numerical example that involves the control of a multi-terminal high-voltage DC grid.
In the last few years there has been a growing interest in the use of symbolic models for the formal verification and control design of purely continuous or hybrid systems. Symbolic models are abstract descriptions of continuous systems where one symbol corresponds to an "aggregate" of continuous states. In this paper we face the problem of deriving symbolic models for nonlinear control systems affected by disturbances. The main contribution of this paper is in proposing symbolic models that can be effectively constructed and that approximate nonlinear control systems affected by disturbances in the sense of alternating approximate bisimulation.
Automatic neonatal brain tissue segmentation in preterm born infants is a prerequisite for evaluation of brain development. However, automatic segmentation is often hampered by motion artifacts caused by infant head movements during image acquisition. Methods have been developed to remove or minimize these artifacts during image reconstruction using frequency domain data. However, frequency domain data might not always be available. Hence, in this study we propose a method for removing motion artifacts from the already reconstructed MR scans. The method employs a generative adversarial network trained with a cycle consistency loss to transform slices affected by motion into slices without motion artifacts, and vice versa. In the experiments 40 T2-weighted coronal MR scans of preterm born infants imaged at 30 weeks postmenstrual age were used. All images contained slices affected by motion artifacts hampering automatic tissue segmentation. To evaluate whether correction allows more accurate image segmentation, the images were segmented into 8 tissue classes: cerebellum, myelinated white matter, basal ganglia and thalami, ventricular cerebrospinal fluid, white matter, brain stem, cor
Most of the information we have about the internal rotation of stars comes from modes that are weakly affected by rotation, for example by using rotational splittings. In contrast, we present here a method, based on the asymptotic theory of Prat et al. (2016), which allows us to analyse the signature of rotation where its effect is the most important, that is in low-frequency gravity modes that are strongly affected by rotation. For such modes, we predict two spectral patterns that could be confronted to observed spectra and those computed using fully two-dimensional oscillation codes.
COVID-19 is a new type of coronavirus disease which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It originated in China in the month of December 2019 and quickly started to spread within the country. On 31st December 2019, it was first reported to country office of World Health Organization (WHO) in China. Since then, it has spread to most of the countries around the globe. However, there has been a recent rise in trend in believing that it would go away during summer days, which has not yet been properly investigated. In this paper, relationship of daily number of confirmed cases of COVID-19 with three environmental factors, viz. maximum relative humidity (RH_max), maximum temperature (T_max) and highest wind speed (WS_max), considering the incubation period, have been investigated statistically, for four of the most affected places of China, viz. Beijing, Chongqing, Shanghai, Wuhan and five of the most affected places of Italy, viz. Bergamo, Cremona, Lodi, Milano. It has been found that the relationship with maximum relative humidity and highest wind is mostly negligible, whereas relationship with maximum temperature is ranging between negligible to
An operatorial theoretical model based on raising and lowering fermionic operators for the description of the dynamics of a political system consisting of macro--groups affected by turncoat--like behaviors is presented. The analysis of the party system dynamics is carried on by combining the action of a suitable quadratic Hamiltonian operator with specific rules (depending on the variations of the mean values of the observables) able to adjust periodically the conservative model to the political environment.
This paper is concerned with the study of scalability in nonlinear heterogeneous networks affected by communication delays and disturbances. After formalizing the notion of scalability, we give two sufficient conditions to assess this property. Our results can be used to study leader-follower and leaderless networks and also allow to consider the case when the desired configuration of the system changes over time. We show how our conditions can be turned into design guidelines to guarantee scalability and illustrate their effectiveness via numerical examples.
We investigate a problem of the necessary and sufficient conditions for appearance of the 1/f fluctuations in the simple systems affected by the external random perturbations, i.e. the power spectral density of the flux of particles moving in some contours and perturbed by the external forces. In some cases we observe the 1/f behavior but only in some range of frequencies and parameters of the systems.
We provide a general framework for handling the effects of a unitary disturbance on the estimation of the amplitude $λ$ associated to a unitary dynamics. By computing an analytical and general expression for the quantum Fisher information, we prove that the optimal estimation precision for $λ$ cannot be outperformed through the addition of such a unitary disturbance. However, if the dynamics of the system is already affected by an external field, increasing its strength does not necessary imply a loss in the optimal estimation precision.
Affective priming exemplifies the challenge of ambiguity in affective computing. While the community has largely addressed this issue from a label-based perspective, identifying data points in the sequence affected by the priming effect, the impact of priming on data itself, particularly in physiological signals, remains underexplored. Data affected by priming can lead to misclassifications when used in learning models. This study proposes the Affective Priming Score (APS), a data-driven method to detect data points influenced by the priming effect. The APS assigns a score to each data point, quantifying the extent to which it is affected by priming. To validate this method, we apply it to the SEED and SEED-VII datasets, which contain sufficient transitions between emotional events to exhibit priming effects. We train models with the same configuration using both the original data and priming-free sequences. The misclassification rate is significantly reduced when using priming-free sequences compared to the original data. This work contributes to the broader challenge of ambiguity by identifying and mitigating priming effects at the data level, enhancing model robustness, and offe
Modeling dimensional affect in longitudinal text requires distinguishing current affect estimation from future affective change forecasting. Existing approaches often treat each text as an independent observation and apply similar assumptions to both tasks, without testing whether they rely on different information sources. This paper investigates that distinction using longitudinal self-reported ecological essays and feeling-word entries. We propose the Trait--State Affective Prediction (TSAP) framework and its temporal extension E-TSAP for per-text valence and arousal prediction, evaluated on a held-out prediction test set of 1,737 entries from 91 users. We further propose the Affective Change Forecaster Hybrid (ACF-Hybrid) for next-step affective change forecasting, evaluated on a held-out forecasting test set of 46 users. For prediction, E-TSAP achieves composite Pearson correlations of 0.670 for valence and 0.449 for arousal. For forecasting, textual representations perform worse than compact numeric trajectory baselines: the text-inclusive model achieves only r=0.316 for valence and r=0.284 for arousal, whereas a simple prior-state baseline reaches r=0.615 and r=0.670, respec
We propose a visuo-tactile feedback method that combines virtual hand visualization and fingertip vibrations to modulate affective roughness perception in VR. While prior work has focused on object-based textures and vibrotactile feedback, the role of visual feedback on virtual hands remains underexplored. Our approach introduces affective visual cues including line shape, motion, and color applied to hand outlines, and examines their influence on both affective responses (arousal, valence) and perceived roughness. Results show that sharp contours enhanced perceived roughness, increased arousal, and reduced valence, intensifying the emotional impact of haptic feedback. In contrast, color affected valence only, with red consistently lowering emotional positivity. These effects were especially noticeable at lower haptic intensities, where visual cues extended affective modulation into mid-level perceptual ranges. Overall, the findings highlight how integrating expressive visual cues with tactile feedback can enrich affective rendering and offer flexible emotional tuning in immersive VR interactions.
Major Depressive Disorder (MDD) is a highly prevalent mental health condition, and a deeper understanding of its neurocognitive foundations is essential for identifying how core functions such as emotional and self-referential processing are affected. We investigate how depression alters the temporal dynamics of emotional processing by measuring neural responses to self-referential affective sentences using surface electroencephalography (EEG) in healthy and depressed individuals. Our results reveal significant group-level differences in neural activity during sentence viewing, suggesting disrupted integration of emotional and self-referential information in depression. Deep learning model trained on these responses achieves an area under the receiver operating curve (AUC) of 0.707 in distinguishing healthy from depressed participants, and 0.624 in differentiating depressed subgroups with and without suicidal ideation. Spatial ablations highlight anterior electrodes associated with semantic and affective processing as key contributors. These findings suggest stable, stimulus-driven neural signatures of depression that may inform future diagnostic tools.
Work in Computational Affective Science and Computational Social Science explores a wide variety of research questions about people, emotions, behavior, and health. Such work often relies on language data that is first labeled with relevant information, such as the use of emotion words or the age of the speaker. Although many resources and algorithms exist to enable this type of labeling, discovering, accessing, and using them remains a substantial impediment, particularly for practitioners outside of computer science. Here, we present the ABCDE dataset (Affect, Body, Cognition, Demographics, and Emotion), a large-scale collection of over 400 million text utterances drawn from social media, blogs, books, and AI-generated sources. The dataset is annotated with a wide range of features relevant to computational affective and social science. ABCDE facilitates interdisciplinary research across numerous fields, including affective science, cognitive science, the digital humanities, sociology, political science, and computational linguistics.