Multimodal deepfake technologies have emerged rapidly in recent years, with wide application prospects in various fields. The conventional single-training paradigm with inherent limited generalization illustrates inadequate for addressing the continuous evolution of multimodal deepfakes. However, fine-tuning a model with new deepfake data faces past forgery patterns loss and the significant domain shift in diverse novel multimodal deepfake technologies. To address these issues, we propose a novel Quantum State Analytic Incremental Adaptability Learning method (Qsaint) for multimodal deepfake detection. To stabilize prior deepfake memory, Qsaint recursively learns detection-label mapping relations for the new deepfakes artifact with a closed-form solution, preserving the distribution memory from the historical deepfake domains without accessing previous videos. During incremental learning stages, we propose a deepfake quantum state adaptability module inspired by quantum information science. It adapts to the new forgery states and aligns them with the historical deepfake knowledge through cooling and evolution operations, eliminating deepfake domain shift issues. Comprehensive experiments demonstrate that Qsaint significantly mitigates the memory interference of historical deepfakes, effectively balancing the adaptability for new forgery tasks with the memorization of known deepfake patterns.

Self-improving alignment leveraging large language models (LLMs) to automatically generate synthetic preference data has garnered significant attention as a means of reducing reliance on human labelers. These methods typically employ the LLM-as-a-judge mechanism, where the LLM generates responses and then employs itself to judge which response best aligns with the given prompt for curating the binary self-preferred dataset. However, these methods encounter two major challenges: (1) LLM-as-a-judge often produces error-prone evaluations, resulting in low-quality preference annotation, and (2) their optimization strategies often overlook the strength of preferences within binary pairs, leading to overfitting. This paper proposes a novel method, Preference-Strength-aware Optimization (PSO), to address these issues. Specifically, PSO frames the preference annotation process as a judgment token prediction task using the generative preference model to produce reliable judgments. The predicted judgment token indicates the preferred response and its corresponding probability reflects the disparity between responses, referred to as preference strength. Based on this strength, we introduce a new preference-strength-aware loss to adaptively reweight the impact of different response pairs on optimization, concentrating the model's learning on high-quality response pairs. Our experiments demonstrate that PSO significantly improves performance in preference benchmarks, achieving stronger alignment with human preferences, reducing verbose responses, and mitigating overfitting. Furthermore, PSO exhibits robust generalization and sample efficiency, offering a scalable and promising solution for LLM alignment without relying on human-annotated preferences.

Large Language Model (LLM) agents frequently produce sub-optimal actions when tackling complex, multi-step decision-making tasks. Employing self-critique to identify flaws and suggest enhancements is an effective strategy for refining actions. Although trajectory-level critique is commonly employed, it often fails to identify flawed steps accurately. In this paper, we introduce SLSC-MCTS, a method that integrates Monte Carlo Tree Search with Step-Level Self-Critique to enhance LLM agents during both testing and self-training phases. During decision tree expansion with SLSC-MCTS, the LLM agent initially generates an action, receives environmental feedback, and subsequently generates further actions via self-critique and refinement. Through multiple episodes of SLSC-MCTS, LLM agents can effectively utilize step-level critiques while disregarding ineffective ones based on node values, thereby incorporating the critiques more robustly. Additionally, our method further empowers LLM agents in a self-training manner, collecting training data from the constructed decision tree to iteratively fine-tune the LLM agents. The self-training data gathered via SLSC-MCTS is diverse and high-quality, which further enhances the reasoning, critiquing, and refining abilities of LLM agents. Experimental results demonstrate that SLSC-MCTS significantly improves LLM agents during testing, surpassing state-of-the-art baselines and achieving shorter task completion trajectories across information retrieval benchmarks such as WebShop and HotPotQA. After three iterations of self-training, LLM agents established by Llama-3.1-8B-Instruct show substantial improvement, even surpassing human experts in WebShop.

Docker containers have become the de-facto industry standard. Docker builds often break, and a large amount of efforts are put into troubleshooting broken builds. Prior studies have evaluated the rate at which builds in large organizations fail. However, little is known about the frequency and fix effort of failures that occur in Docker builds of open-source projects. This paper provides a first attempt to present a preliminary study on 857,086 Docker builds from 3,828 open-source projects hosted on GitHub. Using the Docker build data, we measure the frequency of broken builds and report their fix time. Furthermore, we explore the evolution of Docker build failures across time. Our findings help to characterize and understand Docker build failures and motivate the need for collecting more empirical evidence.

In GitHub, the pull-based development model enables community contributors to collaborate in a more efficient way. However, the distributed and parallel characteristics of this model pose a potential risk for developers to submit duplicate pull-requests (PRs), which increase the extra cost of project maintenance. To facilitate the further studies to better understand and solve the issues introduced by duplicate PRs, we construct a large dataset of historical duplicate PRs extracted from 26 popular open source projects in GitHub by using a semi-automatic approach. Furthermore, we present some preliminary applications to illustrate how further researches can be conducted based on this dataset.

Large and sparse datasets with a lot of missing values are common in the big data era, such as user behaviors over a large number of items. Classification in such datasets is an important topic for machine learning and data mining. Practically, naive Bayes is still a popular classification algorithm for large sparse datasets, as its time and space complexity scales linearly with the size of non-missing values. However, several important questions about the behavior of naive Bayes are yet to be answered. For example, how different mechanisms of data missing, data sparsity, and the number of attributes systematically affect the learning curves and convergence? In this paper, we address several common data missing mechanisms and propose novel data generation methods based on these mechanisms. We generate large and sparse data systematically, and study the entire AUC (Area Under ROC Curve) learning curve and convergence behavior of naive Bayes. We not only have several important experiment observations, but also provide detailed theoretic studies. Finally, we summarize our empirical and theoretic results as an intuitive decision flowchart and a useful guideline for classifying large sparse datasets in practice.

Large and sparse datasets with a lot of missing values are common in the big data era. Naive Bayes is a good classification algorithm for such datasets, as its time and space complexity scales well with the size of non-missing values. However, several important questions about the behavior of naive Bayes are yet to be answered. For example, how different mechanisms of missing, data sparseness and the number of attributes systematically affect the learning curves and convergence? Recent work in classifying large and sparse real-world datasets still could not address these questions mainly because the data missing mechanisms of these datasets are not taken into account. In this paper, we propose two novel data missing and expansion mechanisms to answer these questions. We use the data missing mechanisms to generate large and sparse data with various properties, and study the entire learning curve and convergence behavior of naive Bayes. We made several observations, which are verified through detailed theoretical study. Our results are useful for learning large sparse data in practice.

The pull-based development model, enabled by git and popularised by collaborative coding platforms like Bit Bucket, Gitorius, and GitHub, is widely used in distributed software teams. While this model lowers the barrier to entry for potential contributors (since anyone can submit pull requests to any repository), it also increases the burden on integrators (i.e., Members of a project's core team, responsible for evaluating the proposed changes and integrating them into the main development line), who struggle to keep up with the volume of incoming pull requests. In this paper we report on a quantitative study that tries to resolve which factors affect pull request evaluation latency in GitHub. Using regression modeling on data extracted from a sample of GitHub projects using the Travis-CI continuous integration service, we find that latency is a complex issue, requiring many independent variables to explain adequately.

Context awareness is one of the most fundamental issues in pervasive computing. In this paper, component based context model based on middleware architecture is proposed. Moreover, OWL-based context ontology for modelling context information to easily share and reuse context knowledge is presented. By giving fire alarm scenario for our prototype, the proposed component based context architecture can be deployed in different context-aware application and can provide a middleware support for context representation and knowledge sharing.