Improving Quality of Software with Foreign Function Interfaces using Static Analysis

A Foreign Function Interface (FFI) is a mechanism that allows software written in one host programming language to directly use another foreign programming language by invoking function calls across language boundaries. Today\u27s software development often utilizes FFIs to reuse software components. Examples of such systems are the Java Development Kit (JDK), Android mobile OS, and Python packages in the Fedora LINUX operating systems. The use of FFIs, however, requires extreme care and can introduce undesired side effects that degrade software quality. In this thesis, we aim to improve several quality aspects of software composed of FFIs by applying static analysis. The thesis investigates several particular characteristics of FFIs and studies software bugs caused by the misuse of FFIs. We choose two FFIs, the Java Native Interface (JNI) and the Python/C interface, as the main subjects of this dissertation. To reduce software security vulnerabilities introduced by the JNI, we first propose definitions of new patterns of bugs caused by the improper exception handlings between Java and C. We then present the design and implement a bug finding system to uncover these bugs. To ensure software safety and reliability in multithreaded environment, we present a novel and efficient system that ensures atomicity in the JNI. Finally, to improve software performance and reliability, we design and develop a framework for finding errors in memory management in programs written with the Python/C interface. The framework is built by applying affine abstraction and affine analysis of reference-counts of Python objects. This dissertation offers a comprehensive study of FFIs and software composed of FFIs. The research findings make several contributions to the studies of static analysis and to the improvement of software quality

Prediction of underwater acoustic signals based on ESMD and ELM

357-362The local predictability of underwater acoustic signals plays an important role in underwater acoustic signal processing, as it is the basis for solving non-stationary signal detection. A prediction model of underwater acoustic signals based on extreme-point symmetric mode decomposition (ESMD) and extreme learning machine (ELM) is proposed. First, underwater acoustic signals are decomposed by ESMD to obtain a set of intrinsic model functions (IMFs). After IMFs are grouped, the training samples and forecast samples are obtained. Then, prediction model for training samples is established by using ELM to obtain the input layer, output layer weight vector and offset matrix. The trained ELM is used to predict the forecast sample to obtain component. Finally, the reconstructed IMFs and residuals are the final prediction results. The experimental results show that the proposed model is a good predictive model having better prediction accuracy and smaller error

The Test of torrential rains—— Analysis of factors influencing the credibility of government microblogs in major natural disasters

Social media has become an important platform for the government to release information, publicize policies, and communicate with the public due to its instantaneous, synchronized, interactive advantages. And it plays an irreplaceable role in the rescue and relief process of major natural disasters. It is because of the special attributes and unique effectiveness of government social media that it has also become a visible window to reflect and evaluate the credibility of the Government. This research focuses on the rare and extremely heavy rainstorm that oc-curred in Zhengzhou City, Henan Province, China in July 2021, and uses it as a scenario. By collecting and analyzing the information release data and public interaction information of governments’ microblog accounts in Zhengzhou City, the research is carried out from three dimensions: government information supply, public information demand, and the deviations between the them. After that we will examine the credibility and effectiveness of government social media during the "720" Zhengzhou heavy rainstorm, and try to create a model of the factors which influence the credibility of government social media in major natural disasters, and then propose strategies to improve it

Prediction of underwater acoustic signals based on ESMD and ELM

357-362The local predictability of underwater acoustic signals plays an important role in underwater acoustic signal processing, as it is the basis for solving non-stationary signal detection. A prediction model of underwater acoustic signals based on extreme-point symmetric mode decomposition (ESMD) and extreme learning machine (ELM) is proposed. First, underwater acoustic signals are decomposed by ESMD to obtain a set of intrinsic model functions (IMFs). After IMFs are grouped, the training samples and forecast samples are obtained. Then, prediction model for training samples is established by using ELM to obtain the input layer, output layer weight vector and offset matrix. The trained ELM is used to predict the forecast sample to obtain component. Finally, the reconstructed IMFs and residuals are the final prediction results. The experimental results show that the proposed model is a good predictive model having better prediction accuracy and smaller error

Understanding Expertise through Demonstrations: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning

Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving. However, the structure of an expert's preferences implicit in observed actions is closely linked to the expert's model of the environment dynamics (i.e. the ``world''). Thus, inaccurate models of the world obtained from finite data with limited coverage could compound inaccuracy in estimated rewards. To address this issue, we propose a bi-level optimization formulation of the estimation task wherein the upper level is likelihood maximization based upon a conservative model of the expert's policy (lower level). The policy model is conservative in that it maximizes reward subject to a penalty that is increasing in the uncertainty of the estimated model of the world. We propose a new algorithmic framework to solve the bi-level optimization problem formulation and provide statistical and computational guarantees of performance for the associated reward estimator. Finally, we demonstrate that the proposed algorithm outperforms the state-of-the-art offline IRL and imitation learning benchmarks by a large margin, over the continuous control tasks in MuJoCo and different datasets in the D4RL benchmark