Improve Model Testing by Integrating Bounded Model Checking and Coverage Guided Fuzzing

The control logic models built by Simulink or Ptolemy have been widely used in industry scenes. It is an urgent need to ensure the safety and security of the control logic models. Test case generation technologies are widely used to ensure the safety and security. State-of-the-art model testing tools employ model checking techniques or search-based methods to generate test cases. Traditional search based techniques based on Simulink simulation are plagued by problems such as low speed and high overhead. Traditional model checking techniques such as symbolic execution have limited performance when dealing with nonlinear elements and complex loops. Recently, coverage guided fuzzing technologies are known to be effective for test case generation, due to their high efficiency and impressive effects over complex branches of loops. In this paper, we apply fuzzing methods to improve model testing and demonstrate the effectiveness. The fuzzing methods aim to cover more program branches by mutating valuable seeds. Inspired by this feature, we propose a novel integration technology SPsCGF, which leverages bounded model checking for symbolic execution to generate test cases as initial seeds and then conduct fuzzing based upon these worthy seeds. In this manner, our work combines the advantages of the model checking methods and fuzzing techniques in a novel way. Since the control logic models always receive signal inputs, we specifically design novel mutation operators for signals to improve the existing fuzzing method in model testing. Over the evaluated benchmarks which consist of industrial cases, SPsCGF could achieve 8% to 38% higher model coverage and 3x-10x time efficiency compared with the state-of-the-art works.Comment: 10 page

Improving the Robustness to Data Inconsistency between Training and Testing for Code Completion by Hierarchical Language Model

In the field of software engineering, applying language models to the token sequence of source code is the state-of-art approach to build a code recommendation system. The syntax tree of source code has hierarchical structures. Ignoring the characteristics of tree structures decreases the model performance. Current LSTM model handles sequential data. The performance of LSTM model will decrease sharply if the noise unseen data is distributed everywhere in the test suite. As code has free naming conventions, it is common for a model trained on one project to encounter many unknown words on another project. If we set many unseen words as UNK just like the solution in natural language processing, the number of UNK will be much greater than the sum of the most frequently appeared words. In an extreme case, just predicting UNK at everywhere may achieve very high prediction accuracy. Thus, such solution cannot reflect the true performance of a model when encountering noise unseen data. In this paper, we only mark a small number of rare words as UNK and show the prediction performance of models under in-project and cross-project evaluation. We propose a novel Hierarchical Language Model (HLM) to improve the robustness of LSTM model to gain the capacity about dealing with the inconsistency of data distribution between training and testing. The newly proposed HLM takes the hierarchical structure of code tree into consideration to predict code. HLM uses BiLSTM to generate embedding for sub-trees according to hierarchies and collects the embedding of sub-trees in context to predict next code. The experiments on inner-project and cross-project data sets indicate that the newly proposed Hierarchical Language Model (HLM) performs better than the state-of-art LSTM model in dealing with the data inconsistency between training and testing and achieves averagely 11.2\% improvement in prediction accuracy

BoxSnake: Polygonal Instance Segmentation with Box Supervision

Box-supervised instance segmentation has gained much attention as it requires only simple box annotations instead of costly mask or polygon annotations. However, existing box-supervised instance segmentation models mainly focus on mask-based frameworks. We propose a new end-to-end training technique, termed BoxSnake, to achieve effective polygonal instance segmentation using only box annotations for the first time. Our method consists of two loss functions: (1) a point-based unary loss that constrains the bounding box of predicted polygons to achieve coarse-grained segmentation; and (2) a distance-aware pairwise loss that encourages the predicted polygons to fit the object boundaries. Compared with the mask-based weakly-supervised methods, BoxSnake further reduces the performance gap between the predicted segmentation and the bounding box, and shows significant superiority on the Cityscapes dataset. The code has been available publicly.Comment: ICCV 202

The Application of Data Envelopment Analysis for Chinese Bank Performance

This study uses data envelopment analysis (DEA) model to measure the efficiency of the 21 Chinese listed banks over 2012-2016. The results indicate that during the data period, there is a drop in the tendency of bank efficiency. In addition, the author applies the Tobit regression to examine the influence on ownership structure, bank size, profitability, risk and environmental factors on bank efficiency. The study supports the evidences of existing literature that state-owned banks are less efficient, and IPO can improve bank efficiency

TSST: A Benchmark and Evaluation Models for Text Speech-Style Transfer

Text style is highly abstract, as it encompasses various aspects of a speaker's characteristics, habits, logical thinking, and the content they express. However, previous text-style transfer tasks have primarily focused on data-driven approaches, lacking in-depth analysis and research from the perspectives of linguistics and cognitive science. In this paper, we introduce a novel task called Text Speech-Style Transfer (TSST). The main objective is to further explore topics related to human cognition, such as personality and emotion, based on the capabilities of existing LLMs. Considering the objective of our task and the distinctive characteristics of oral speech in real-life scenarios, we trained multi-dimension (i.e. filler words, vividness, interactivity, emotionality) evaluation models for the TSST and validated their correlation with human assessments. We thoroughly analyze the performance of several large language models (LLMs) and identify areas where further improvement is needed. Moreover, driven by our evaluation models, we have released a new corpus that improves the capabilities of LLMs in generating text with speech-style characteristics. In summary, we present the TSST task, a new benchmark for style transfer and emphasizing human-oriented evaluation, exploring and advancing the performance of current LLMs.Comment: Working in progres