Text-to-Visualization (Text-to-Vis) systems span three paradigms–rule-based, neural, and Large Language Model (LLM)-based–each emitting incompatible output artifacts, from declarative grammars to imperative plotting code, so existing benchmarks compare systems only within a single output family, and unified Abstract Syntax Tree (AST) extraction introduces systematic measurement artifacts mistaken for capability gaps. We propose the Multi-Level Format-Agnostic (MLFA) evaluation framework, which assesses systems at three deliberately disaggregated levels: executability through rendering (L1), semantic accuracy via LLM-based component extraction calibrated against ground-truth parsers (L2), and visual fidelity via multimodal-LLM comparison of rendered images (L3). Validated on seven systems spanning all three paradigms across two benchmarks, L2 recovers capability that format-bound axis metrics had hidden in code-emitting LLM systems, L3 yields a uniform visual-fidelity ranking across paradigms, and an efficiency analysis exposes token and latency gaps favoring specialised neural architectures. To our knowledge, MLFA is the first evaluation framework to apply multi-level measurement uniformly across heterogeneous output formats, establishing disaggregated measurement as a prerequisite for fair cross-paradigm comparison.
As a by-product of the rapid advancement of big data and artificial intelligence, high-dimensional data has been expeditiously evolving in both quantity and complexity, resulting in the critical need for suitable, effective retrieval and management systems. With this motivation, numerous classical solutions to the problem of high-dimensional data search were proposed. One of the most well-known instances of these is locality-sensitive hashing (LSH) and its prominent ability to run in sub-linear time. In situations with large databases, candidate validation, the last step of LSH’s procedure, can present itself as a bottleneck, which, in most cases, induces a tradeoff between accuracy and efficiency. Therefore, in this paper, we intend to utilize the renowned ability to speed up specific tasks like search problems of quantum computing to alleviate this issue and develop a quantum locality-sensitive hashing (QLSH) system. Specifically, by exploiting a Hamming-distance-calculating quantum circuit to perform candidate validation, we reduce the complexity of this step while preserving the retrieval performance compared to the classical counterpart in approximate search among high-dimensional data, as demonstrated by quantum simulation results.
An autoencoder (AE) is a neural network that reconstructs its input. AE consists of two sub-networks: the encoder and the decoder. AE has been commonly applied in feature extraction and anomaly detection. However, AE was not applied to AE models themselves. Accordingly, this paper develops AE for AEs, namely AEAE. For this purpose, we apply AE to the parameters (weights and biases) of AEs. The experiment includes 1) computing reconstruction error of the AEAE model, 2) applying feature extraction and classification to AE models, and 3) one-class classification of AE models using the reconstruction error of the AEAE model. The most interesting outcome is that biases of output layers are distinguishable with high accuracy, whereas other parameters are hard to classify. Moreover, AEAE could be extended to wider directions, applying deep learning to deep learning models. Finally, we got another question: how to apply AE to AEAE models.
The rapid growth of short-video platforms has created a content environment where information spreads quickly and user engagement changes greatly over time. As a result, early trend detection has become an important task for intelligent software analytics systems. In this study, we propose an analytical framework for detecting trends in short videos on social media using multi-source features and machine learning models. We construct a dataset of 1,922 TikTok videos, of which 172 are labeled as trending based on engagement growth criteria. For each video, we extract features related to metadata, content creators, posting time, captions, background music metadata, contextual information, and semantic representations of captions. Several machine learning models are evaluated, including Logistic Regression, Random Forest, SVM, LightGBM, and XGBoost. The results show that XGBoost achieves the best performance, with an F1-score of 0.8367 for the Trending class and a Macro-F1 score of 0.9108. The results also show that F1-score and PR-AUC are more informative than accuracy alone for this imbalanced trend detection task. Overall, this study shows that combining multi-source features with boosting models is a feasible approach for short-video trend detection. The dataset and source code are available upon reasonable request for research purposes.
Effective coordination between interacting actors relies on messages whose reliability varies with operational conditions, strategic incentives, and previous interactions. Assessing a message from its current content alone may therefore be insufficient, particularly when actors adapt their communication over time. This paper proposes a credibility-aware framework that formulates strategic information sharing as a sequential prediction problem, combining current messages, operational variables, observed actions, and interaction histories to estimate the reliability of communicated information and support coordination decisions. A neural sequential learning model learns a dynamic representation of actor credibility, capturing temporal dependencies and changes in communication strategies. Compared with Bayesian updating and memoryless prediction across simulated scenarios, the approach is evaluated on both predictive performance and the consequences of predictions for decision quality and coordination outcomes. The study investigates whether data-driven sequential prediction can provide adaptive, robust credibility estimates when communication patterns are uncertain, context-dependent, or time-varying.
