arXiv subjects

Chenyang Lyu

Publications and source records attributed to Chenyang Lyu.

65 records · Page 4Linked to original sources

arXiv2023

Dialogue-to-Video Retrieval

Recent years have witnessed an increasing amount of dialogue/conversation on the web especially on social media. That inspires the development of dialogue-based retrieval, in which retrieving videos based on dialogue is of increasing interest for recommendation systems. Different from other video retrieval tasks, dialogue-to-video retrieval uses structured queries in the form of user-generated dialogue as the search descriptor. We present a novel dialogue-to-video retrieval system, incorporating structured conversational information. Experiments conducted on the AVSD dataset show that our proposed approach using plain-text queries improves over the previous counterpart model by 15.8% on R@1. Furthermore, our approach using dialogue as a query, improves retrieval performance by 4.2%, 6.2%, 8.6% on R@1, R@5 and R@10 and outperforms the state-of-the-art model by 0.7%, 3.6% and 6.0% on R@1, R@5 and R@10 respectively.

cs.IR↗

arXiv2023

MINER: A Hybrid Data-Driven Approach for REST API Fuzzing

In recent years, REST API fuzzing has emerged to explore errors on a cloud service. Its performance highly depends on the sequence construction and request generation. However, existing REST API fuzzers have trouble generating long sequences with well-constructed requests to trigger hard-to-reach states in a cloud service, which limits their performance of finding deep errors and security bugs. Further, they cannot find the specific errors caused by using undefined parameters during request generation. Therefore, in this paper, we propose a novel hybrid data-driven solution, named MINER, with three new designs working together to address the above limitations. First, MINER collects the valid sequences whose requests pass the cloud service's checking as the templates, and assigns more executions to long sequence templates. Second, to improve the generation quality of requests in a sequence template, MINER creatively leverages the state-of-the-art neural network model to predict key request parameters and provide them with appropriate parameter values. Third, MINER implements a new data-driven security rule checker to capture the new kind of errors caused by undefined parameters. We evaluate MINER against the state-of-the-art fuzzer RESTler on GitLab, Bugzilla, and WordPress via 11 REST APIs. The results demonstrate that the average pass rate of MINER is 23.42% higher than RESTler. MINER finds 97.54% more unique errors than RESTler on average and 142.86% more reproducible errors after manual analysis. We have reported all the newly found errors, and 7 of them have been confirmed as logic bugs by the corresponding vendors.

cs.CR↗

arXiv2022

One Bad Apple Spoils the Barrel: Understanding the Security Risks Introduced by Third-Party Components in IoT Firmware

Currently, the development of IoT firmware heavily depends on third-party components (TPCs) to improve development efficiency. Nevertheless, TPCs are not secure, and the vulnerabilities in TPCs will influence the security of IoT firmware. Existing works pay less attention to the vulnerabilities caused by TPCs, and we still lack a comprehensive understanding of the security impact of TPC vulnerability against firmware. To fill in the knowledge gap, we design and implement FirmSec, which leverages syntactical features and control-flow graph features to detect the TPCs in firmware, and then recognizes the corresponding vulnerabilities. Based on FirmSec, we present the first large-scale analysis of the security risks raised by TPCs on $34,136$ firmware images. We successfully detect 584 TPCs and identify 128,757 vulnerabilities caused by 429 CVEs. Our in-depth analysis reveals the diversity of security risks in firmware and discovers some well-known vulnerabilities are still rooted in firmware. Besides, we explore the geographical distribution of vulnerable devices and confirm that the security situation of devices in different regions varies. Our analysis also indicates that vulnerabilities caused by TPCs in firmware keep growing with the boom of the IoT ecosystem. Further analysis shows 2,478 commercial firmware images have potentially violated GPL/AGPL licensing terms.

cs.CR↗

arXiv2022

Exploiting Rich Textual User-Product Context for Improving Sentiment Analysis

User and product information associated with a review is useful for sentiment polarity prediction. Typical approaches incorporating such information focus on modeling users and products as implicitly learned representation vectors. Most do not exploit the potential of historical reviews, or those that currently do require unnecessary modifications to model architecture or do not make full use of user/product associations. The contribution of this work is twofold: i) a method to explicitly employ historical reviews belonging to the same user/product to initialize representations, and ii) efficient incorporation of textual associations between users and products via a user-product cross-context module. Experiments on IMDb, Yelp-2013 and Yelp-2014 benchmarks show that our approach substantially outperforms previous state-of-the-art. Since we employ BERT-base as the encoder, we additionally provide experiments in which our approach performs well with Span-BERT and Longformer. Furthermore, experiments where the reviews of each user/product in the training data are downsampled demonstrate the effectiveness of our approach under a low-resource setting.

cs.CL↗

arXiv2022

QAScore -- An Unsupervised Unreferenced Metric for the Question Generation Evaluation

Question Generation (QG) aims to automate the task of composing questions for a passage with a set of chosen answers found within the passage. In recent years, the introduction of neural generation models has resulted in substantial improvements of automatically generated questions in terms of quality, especially compared to traditional approaches that employ manually crafted heuristics. However, the metrics commonly applied in QG evaluations have been criticized for their low agreement with human judgement. We therefore propose a new reference-free evaluation metric that has the potential to provide a better mechanism for evaluating QG systems, called QAScore. Instead of fine-tuning a language model to maximize its correlation with human judgements, QAScore evaluates a question by computing the cross entropy according to the probability that the language model can correctly generate the masked words in the answer to that question. Furthermore, we conduct a new crowd-sourcing human evaluation experiment for the QG evaluation to investigate how QAScore and other metrics can correlate with human judgements. Experiments show that QAScore obtains a stronger correlation with the results of our proposed human evaluation method compared to existing traditional word-overlap-based metrics such as BLEU and ROUGE, as well as the existing pretrained-model-based metric BERTScore.

cs.CL↗

arXiv2022

Extending the Scope of Out-of-Domain: Examining QA models in multiple subdomains

Past works that investigate out-of-domain performance of QA systems have mainly focused on general domains (e.g. news domain, wikipedia domain), underestimating the importance of subdomains defined by the internal characteristics of QA datasets. In this paper, we extend the scope of "out-of-domain" by splitting QA examples into different subdomains according to their several internal characteristics including question type, text length, answer position. We then examine the performance of QA systems trained on the data from different subdomains. Experimental results show that the performance of QA systems can be significantly reduced when the train data and test data come from different subdomains. These results question the generalizability of current QA systems in multiple subdomains, suggesting the need to combat the bias introduced by the internal characteristics of QA datasets.

cs.CL↗

arXiv2022

Achieving Reliable Human Assessment of Open-Domain Dialogue Systems

Evaluation of open-domain dialogue systems is highly challenging and development of better techniques is highlighted time and again as desperately needed. Despite substantial efforts to carry out reliable live evaluation of systems in recent competitions, annotations have been abandoned and reported as too unreliable to yield sensible results. This is a serious problem since automatic metrics are not known to provide a good indication of what may or may not be a high-quality conversation. Answering the distress call of competitions that have emphasized the urgent need for better evaluation techniques in dialogue, we present the successful development of human evaluation that is highly reliable while still remaining feasible and low cost. Self-replication experiments reveal almost perfectly repeatable results with a correlation of $r=0.969$. Furthermore, due to the lack of appropriate methods of statistical significance testing, the likelihood of potential improvements to systems occurring due to chance is rarely taken into account in dialogue evaluation, and the evaluation we propose facilitates application of standard tests. Since we have developed a highly reliable evaluation method, new insights into system performance can be revealed. We therefore include a comparison of state-of-the-art models (i) with and without personas, to measure the contribution of personas to conversation quality, as well as (ii) prescribed versus freely chosen topics. Interestingly with respect to personas, results indicate that personas do not positively contribute to conversation quality as expected.

cs.CL↗

arXiv2021

Improving Unsupervised Question Answering via Summarization-Informed Question Generation

Question Generation (QG) is the task of generating a plausible question for a given pair. Template-based QG uses linguistically-informed heuristics to transform declarative sentences into interrogatives, whereas supervised QG uses existing Question Answering (QA) datasets to train a system to generate a question given a passage and an answer. A disadvantage of the heuristic approach is that the generated questions are heavily tied to their declarative counterparts. A disadvantage of the supervised approach is that they are heavily tied to the domain/language of the QA dataset used as training data. In order to overcome these shortcomings, we propose an unsupervised QG method which uses questions generated heuristically from summaries as a source of training data for a QG system. We make use of freely available news summary data, transforming declarative summary sentences into appropriate questions using heuristics informed by dependency parsing, named entity recognition and semantic role labeling. The resulting questions are then combined with the original news articles to train an end-to-end neural QG model. We extrinsically evaluate our approach using unsupervised QA: our QG model is used to generate synthetic QA pairs for training a QA model. Experimental results show that, trained with only 20k English Wikipedia-based synthetic QA pairs, the QA model substantially outperforms previous unsupervised models on three in-domain datasets (SQuAD1.1, Natural Questions, TriviaQA) and three out-of-domain datasets (NewsQA, BioASQ, DuoRC), demonstrating the transferability of the approach.

cs.CL↗

arXiv2020

Improving Document-Level Sentiment Analysis with User and Product Context

Past work that improves document-level sentiment analysis by encoding user and product information has been limited to considering only the text of the current review. We investigate incorporating additional review text available at the time of sentiment prediction that may prove meaningful for guiding prediction. Firstly, we incorporate all available historical review text belonging to the author of the review in question. Secondly, we investigate the inclusion of historical reviews associated with the current product (written by other users). We achieve this by explicitly storing representations of reviews written by the same user and about the same product and force the model to memorize all reviews for one particular user and product. Additionally, we drop the hierarchical architecture used in previous work to enable words in the text to directly attend to each other. Experiment results on IMDB, Yelp 2013 and Yelp 2014 datasets show improvement to state-of-the-art of more than 2 percentage points in the best case.

cs.CL↗

arXiv2020

UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating Fuzzers

A flurry of fuzzing tools (fuzzers) have been proposed in the literature, aiming at detecting software vulnerabilities effectively and efficiently. To date, it is however still challenging to compare fuzzers due to the inconsistency of the benchmarks, performance metrics, and/or environments for evaluation, which buries the useful insights and thus impedes the discovery of promising fuzzing primitives. In this paper, we design and develop UNIFUZZ, an open-source and metrics-driven platform for assessing fuzzers in a comprehensive and quantitative manner. Specifically, UNIFUZZ to date has incorporated 35 usable fuzzers, a benchmark of 20 real-world programs, and six categories of performance metrics. We first systematically study the usability of existing fuzzers, find and fix a number of flaws, and integrate them into UNIFUZZ. Based on the study, we propose a collection of pragmatic performance metrics to evaluate fuzzers from six complementary perspectives. Using UNIFUZZ, we conduct in-depth evaluations of several prominent fuzzers including AFL [1], AFLFast [2], Angora [3], Honggfuzz [4], MOPT [5], QSYM [6], T-Fuzz [7] and VUzzer64 [8]. We find that none of them outperforms the others across all the target programs, and that using a single metric to assess the performance of a fuzzer may lead to unilateral conclusions, which demonstrates the significance of comprehensive metrics. Moreover, we identify and investigate previously overlooked factors that may significantly affect a fuzzer's performance, including instrumentation methods and crash analysis tools. Our empirical results show that they are critical to the evaluation of a fuzzer. We hope that our findings can shed light on reliable fuzzing evaluation, so that we can discover promising fuzzing primitives to effectively facilitate fuzzer designs in the future.

cs.CR↗

arXiv2019

SmartSeed: Smart Seed Generation for Efficient Fuzzing

Fuzzing is an automated application vulnerability detection method. For genetic algorithm-based fuzzing, it can mutate the seed files provided by users to obtain a number of inputs, which are then used to test the objective application in order to trigger potential crashes. As shown in existing literature, the seed file selection is crucial for the efficiency of fuzzing. However, current seed selection strategies do not seem to be better than randomly picking seed files. Therefore, in this paper, we propose a novel and generic system, named SmartSeed, to generate seed files towards efficient fuzzing. Specifically, SmartSeed is designed based on a machine learning model to learn and generate high-value binary seeds. We evaluate SmartSeed along with American Fuzzy Lop (AFL) on 12 open-source applications with the input formats of mp3, bmp or flv. We also combine SmartSeed with different fuzzing tools to examine its compatibility. From extensive experiments, we find that SmartSeed has the following advantages: First, it only requires tens of seconds to generate sufficient high-value seeds. Second, it can generate seeds with multiple kinds of input formats and significantly improves the fuzzing performance for most applications with the same input format. Third, SmartSeed is compatible to different fuzzing tools. In total, our system discovers more than twice unique crashes and 5,040 extra unique paths than the existing best seed selection strategy for the evaluated 12 applications. From the crashes found by SmartSeed, we discover 16 new vulnerabilities and have received their CVE IDs.

cs.CR↗