249 research outputs found
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
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
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
Sunspots Time-Series Prediction Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Neural Network
CTP-Net: Character Texture Perception Network for Document Image Forgery Localization
Due to the progression of information technology in recent years, document
images have been widely disseminated in social networks. With the help of
powerful image editing tools, document images are easily forged without leaving
visible manipulation traces, which leads to severe issues if significant
information is falsified for malicious use. Therefore, the research of document
image forensics is worth further exploring. In a document image, the character
with specific semantic information is most vulnerable to tampering, for which
capturing the forgery traces of the character is the key to localizing the
forged region in document images. Considering both character and image
textures, in this paper, we propose a Character Texture Perception Network
(CTP-Net) to localize the forgery of document images. Based on optical
character recognition, a Character Texture Stream (CTS) is designed to capture
features of text areas that are essential components of a document image.
Meanwhile, texture features of the whole document image are exploited by an
Image Texture Stream (ITS). Combining the features extracted from the CTS and
the ITS, the CTP-Net can reveal more subtle forgery traces from document
images. To overcome the challenge caused by the lack of fake document images,
we design a data generation strategy that is utilized to construct a Fake
Chinese Trademark dataset (FCTM). Through a series of experiments, we show that
the proposed CTP-Net is able to capture tampering traces in document images,
especially in text regions. Experimental results demonstrate that CTP-Net can
localize multi-scale forged areas in document images and outperform the
state-of-the-art forgery localization methods
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