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arXiv 2022年

作者： Yang, Ze Wang, Liran Tian, Zhoujin Wu, Wei Li, Zhoujun State Key Lab of Software Development Environment Beihang University Beijing China Meituan Beijing China

Although pre-trained language models (PLMs) have achieved great success and become a milestone in NLP, abstractive conversational summarization remains a challenging but less studied task. The difficulty lies in two aspects. One is the lack of large-scale conversational summary data. Another is that applying the existing pre-trained models to this task is tricky because of the structural dependence within the conversation and its informal expression, etc. In this work, we first build a large-scale (11M) pretraining dataset called RCS, based on the multi-person discussions in the Reddit community. We then present TANET, a thread-aware Transformer-based network. Unlike the existing pre-trained models that treat a conversation as a sequence of sentences, we argue that the inherent contextual dependency among the utterances plays an essential role in understanding the entire conversation and thus propose two new techniques to incorporate the structural information into our model. The first is thread-aware attention which is computed by taking into account the contextual dependency within utterances. Second, we apply thread prediction loss to predict the relations between utterances. We evaluate our model on four datasets of real conversations, covering types of meeting transcripts, customer-service records, and forum threads. Experimental results demonstrate that TANET achieves a new state-of-the-art in terms of both automatic evaluation and human judgment. © 2022, CC BY-NC-SA.

关键词： Large dataset

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Ensuring the Correctness of Regular Expressions: A Review

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International Journal of Automation and Computing 2021年

作者： Li-Xiao Zheng Shuai Ma Zu-Xi Chen Xiang-Yu Luo College of Computer Science and Technology Huaqiao University State Key Laboratory of Software Development Environment Beihang University

Regular expressions are widely used within and even outside of computer science due to their expressiveness and ***, regular expressions have a quite compact and rather tolerant syntax that makes them hard to understand, hard to compose,and error-prone. Faulty regular expressions may cause failures of the applications that use them. Therefore, ensuring the correctness of regular expressions is a vital prerequisite for their use in practical applications. The importance and necessity of ensuring correct definitionsofregularexpressionshaveattractedextensiveattentionfromresearchersandpractitioners,*** study, we provide a review of the recent works for ensuring the correct usage of regular expressions. We classify those works into different categories, including the empirical study, test string generation, automatic synthesis and learning, static checking and verification,visual representation and explanation, and repairing. For each category, we review the main results, compare different approaches, and discuss their advantages and disadvantages. We also discuss some potential future research directions.

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SCPS:A Secure and Copyright-Preserving System for IIoT Based on Hyperledger Fabric 9

SCPS:A Secure and Copyright-Preserving System for IIoT Based...

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9th International Conference on Advanced Cloud and Big Data, CBD 2021

作者： Peng, Jiahui Li, Chunpei Yuan, Bo Meng, Hongwei Yu, Feng Li, Xianxian Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining Security GuiLin China Beihang University Development Environment School of Computer Science and Engineering State Key Laboratory of Software Beijing100191 China Xiong'An Intelligent City Innovation Federation Blockchain Lab Beijing China

ISBN: (纸本)9781665407458

Data, a key asset in Industrial Internet of Things(IIoT), has fueled the emergence of a new data trading in-dustry, which can be collected and traded for better development of IIoT. However, copyright protection is not considered in traditional transactions, resulting in the loss of copyright control of the data after the data was sold. A large number of piracy activities will reduce the enthusiasm of data owners to collect data. The solution we proposed was to use blockchain and smart contract to achieve a decentralized and secure copyright protection scheme. The Trusted Execution environment(TEE) helped our system realized the piracy tracking. To be specific, our solution effectively achieves a secure and copyright protection system of IIoT data, we realize the real simulation on Hyperledger Fabric shows the good performance of our system. © 2022 IEEE.

关键词： Blockchain

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A Progressive Real-Time System Performance Defect Diagnosis Framework Based on Java Virtual Machine Run-time Data 5

A Progressive Real-Time System Performance Defect Diagnosis ...

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Meng, Fan Chen, Bing Zhang, Xiu Zhu, Shaohong Nanjing Betterway Information Technology Limited Company Research and Development Department Nanjing China Nanjing University State Key Lab for Novel Software Technology Nanjing China Wuhan Zhongtian Railway Rolling Stock Limited Company Research and Development Department Wuhan China

ISBN: (纸本)9798350309034

Effective diagnosis of real-time system performance defects is of great significance in practical scenarios. Traditional performance defect detection methods are limited in localization and time-cost, which restrict the use of real applications. To address the above problem, this paper proposes a progressive real-time system performance defect diagnosis framework based on Java Virtual Machine (JVM) run-time data. This framework first conducts the fast diagnosis to derive the preliminary performance evaluation results based on the status data from the operating system during the running period of a real-time system. secondly, conducts the deep diagnosis to analyze the cause of defects in depth based on the stack data from the JVM run-time area. Finally, utilizes a deep-learning method to capture the potential relationships of stack data in the JVM run-time area and learns the disposal policies for the detection task. In addition, we instantiate the framework to a real-time scenario in the field of civil aviation e-commerce tickets and design the core data structure and functions list of the prototype diagnostic system for the sales of civil aviation e-commerce tickets. © 2023 IEEE.

关键词： Deep Learning JVM Run-time Data Performance Defect Diagnosis Real-time System Representation Learning.

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Detecting Political Opinions in Tweets through Bipartite Graph Analysis: A Skip Aggregation Graph Convolution Approach

arXiv

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arXiv 2023年

作者： Peng, Xingyu Zhou, Zhenkun Zhang, Chong Xu, Ke State Key Lab of Software Development Environment Beihang University Beijing100191 China School of Statistics Capital University of Economics and Business Beijing100070 China

Public opinion is a crucial factor in shaping political decision-making. Nowadays, social media has become an essential platform for individuals to engage in political discussions and express their political views, presenting researchers with an invaluable resource for analyzing public opinion. In this paper, we focus on the 2020 US presidential election and create a large-scale dataset from Twitter. To detect political opinions in tweets, we build a user-tweet bipartite graph based on users' posting and retweeting behaviors and convert the task into a Graph Neural Network (GNN)-based node classification problem. Then, we introduce a novel skip aggregation mechanism that makes tweet nodes aggregate information from second-order neighbors, which are also tweet nodes due to the graph's bipartite nature, effectively leveraging user behavioral information. The experimental results show that our proposed model significantly outperforms several competitive baselines. Further analyses demonstrate the significance of user behavioral information and the effectiveness of skip aggregation. © 2023, CC BY.

关键词： Sentiment analysis

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Pbaeg: Combine-Vulnerabilities Aeg to Defeat Protection Mechanisms

SSRN

引用

SSRN 2023年

作者： Wang, Yu Zhai, You Li, Zhoujun Zhang, Yipeng State Key Lab of Software Development Environment Beihang University Beijing China School of Information Science and Technology North China University of Technology Beijing China

Automatic exploit generation (AEG) refers to the process of automatically finding the path in the program that can trigger vulnerabilities and generate exploits. The existing AEG usually sets the preset environment ideally, which does not enable all protection mechanisms. In response to this situation, we propose an exploit generation system Protection Bypass Automatic Exploit Generator (PBAEG) which automatically detects stack overflow vulnerabilities and format string vulnerabilities. Then PBAEG combines the above two vulnerabilities to generate exploits. PBAEG uses symbolic execution and dynamic binary analysis to find the above two vulnerabilities, adopts different exploit generation strategies for different protection mechanisms, and defeats Non-Executable (NX), Position-Independent Executable (PIE), Canary, and Address Space Layout Randomization (ASLR) protection mechanisms. At the same time, for some difficult-to-exploit situations, advanced stack overflow exploitation methods are applied to generate exploits. Finally, we also use docker to simulate the remote environment to test the ability of PBAEG to attack the remote environment. Experiments show that PBAEG can complete the vulnerability detection and exploitation generation of 124 binary files, 22 capture-the-flag (CTF) binary files, and 10 public softwares, which takes a shorter time than the existing AEG and covers more types of vulnerabilities. © 2023, The Authors. All rights reserved.

关键词： Model checking

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IoTAEG: Automatic Exploit Generation of IoT Devices

IoTAEG: Automatic Exploit Generation of IoT Devices

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Computer Engineering and Intelligent Control (ICCEIC) International Conference on

作者： Yu Wang Zhoujun Li Yipeng Zhang You Zhai State Key Lab of Software Development Environment Beihang University Beijing China School of Information Science and Technology North China University of Technology Beijing China

Automatic exploit generation (AEG) refers to the process of automatically finding the path in the program that can trigger vulnerabilities and generate exploits. Generally speaking, the process of finding vulnerabilities needs to apply fuzzing and symbolic execution techniques. Existing AEG generally targets executables for regular Linux and Windows platforms, but no AEG for vulnerable Internet of Things (IoT) devices. In response to this situation, we propose the exploit generation system IoTAEG, which automatically detects stack overflow vulnerabilities in the firmware of IoT devices, and automatically generates and exploits the generation system based on the stack overflow vulnerabilities. IoTAEG uses the mature fuzzing software AFL++ to detect vulnerabilities in IoT devices, uses the crashed input found by AFL++ to construct a symbolic state through symbolic execution, dynamically analyzes the constructed symbolic state, and detects whether there are exploitable vulnerabilities. If the above vulnerabilities exist, different exploit generation strategies will be adopted for different protection mechanisms, and some protection mechanisms such as Address space layout randomization (ASLR) and Non-eXecute (NX) will be bypassed. For some difficult-to-exploit cases, IoTAEG uses advanced stack overflow exploitation methods to generate exploits. Experiments show that IoTAEG can complete 20 MIPS/ARM binary files and 8 IoT devices’ firmware vulnerability detection and exploit generation. IoTAEG is the first publicly available vulnerability mining and exploit generation system for IoT devices.

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TRANSFORMER-PATCHER: ONE MISTAKE WORTH ONE NEURON 11

TRANSFORMER-PATCHER: ONE MISTAKE WORTH ONE NEURON

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11th International Conference on Learning Representations, ICLR 2023

作者： Huang, Zeyu Shen, Yikang Zhang, Xiaofeng Zhou, Jie Rong, Wenge Xiong, Zhang State Key Laboratory of Software Development Environment Beihang University China Sino-French Engineer School Beihang University China School of Computer Science and Engineering Beihang University China Mila University of Montreal Canada WeChat AI Tencent Inc. China

Large Transformer-based Pretrained Language Models (PLMs) dominate almost all Natural Language Processing (NLP) tasks. Nevertheless, they still make mistakes from time to time. For a model deployed in an industrial environment, fixing these mistakes quickly and robustly is vital to improve user experiences. Previous works formalize such problems as Model Editing (ME) and mostly focus on fixing one mistake. However, the one-mistake-fixing scenario is not an accurate abstraction of the real-world challenge. In the deployment of AI services, there are ever-emerging mistakes, and the same mistake may recur if not corrected in time. Thus a preferable solution is to rectify the mistakes as soon as they appear nonstop. Therefore, we extend the existing ME into Sequential Model Editing (SME) to help develop more practical editing methods. Our study shows that most current ME methods could yield unsatisfying results in this scenario. We then introduce Transformer-Patcher, a novel model editor that can shift the behavior of transformer-based models by simply adding and training a few neurons in the last Feed-Forward Network layer. Experimental results on both classification and generation tasks show that Transformer-Patcher can successively correct up to thousands of errors (Reliability) and generalize to their equivalent inputs (Generality) while retaining the model's accuracy on irrelevant inputs (Locality). Our method outperforms previous fine-tuning and HyperNetwork-based methods and achieves state-of-the-art performance for Sequential Model Editing (SME). The code is available at https://***/ZeroYuHuang/Transform er-Patcher. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Network layers

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Predicting accepted pull requests in GitHub

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Science China(Information Sciences) 2021年第7期64卷 241-243页

作者： Jing JIANG Jiateng ZHENG Yun YANG Li ZHANG Jie LUO State Key Laboratory of Software Development Environment Beihang University

Dear editor,Various open source software hosting sites, such as Github,provide support for pull-based development and allow developers to flexibly and efficiently make contributions [1].

关键词： Information Systems and Communication Service

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Goal2UCM: Automatic Generation of Use Case Model from iStar Model 14

Goal2UCM: Automatic Generation of Use Case Model from iStar ...

引用

14th International iStar Workshop, iStar 2021

作者： Yang, Yilong Bok, Younggi Yang, Zhuoxi Sheriff, Eric Li, Tong State Key Laboratory of Software Development Environment School of Software Beihang University China Faculty of Information Technology Beijing University of Technology China

Goal-oriented modeling is an effective way for modeling and analyzing the requirements of users. It takes stakeholder s intentions as the main clue and analyzes their goals and tasks to construct hierarchical requirements model. Unified Modeling Language (UML) is a de facto standard for system requirements modeling and design. In practice, it is very desirable to have an approach to automatically transform user requirements into system requirements, then automatically generate system prototypes for requirements validation. In ICSE 19 and RE 19, we propose an approach and CASE tool RM2PT, which can automatically generate prototypes from system requirements in UML. In this paper, we focus on filling the gap between user and system requirements. Specifically, we propose an approach Goal2UCM to automatically generate the use case diagram, the system operations and interfaces of use cases from the goal-oriented model iStar based on the model-driven approach. We evaluate the proposed approach with the case study of CoCoME system. Overall, the result is satisfactory. The 93.6% of the iStar model elements can be transformed successfully, and the remaining parts of sub-goals and sub-Tasks can be refined and mapped into UML models manually. The proposed approach with the developed CASE tool can be applied to the software industry for requirements engineering. © 2021 CEUR-WS. All rights reserved.

关键词： Unified Modeling Language

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