Cross-Inlining Binary Function Similarity Detection

Binary function similarity detection plays an important role in a wide range of security applications. Existing works usually assume that the query function and target function share equal semantics and compare their full semantics to obtain the similarity. However, we find that the function mapping is more complex, especially when function inlining happens. In this paper, we will systematically investigate cross-inlining binary function similarity detection. We first construct a cross-inlining dataset by compiling 51 projects using 9 compilers, with 4 optimizations, to 6 architectures, with 2 inlining flags, which results in two datasets both with 216 combinations. Then we construct the cross-inlining function mappings by linking the common source functions in these two datasets. Through analysis of this dataset, we find that three cross-inlining patterns widely exist while existing work suffers when detecting cross-inlining binary function similarity. Next, we propose a pattern-based model named CI-Detector for cross-inlining matching. CI-Detector uses the attributed CFG to represent the semantics of binary functions and GNN to embed binary functions into vectors. CI-Detector respectively trains a model for these three cross-inlining patterns. Finally, the testing pairs are input to these three models and all the produced similarities are aggregated to produce the final similarity. We conduct several experiments to evaluate CI-Detector. Results show that CI-Detector can detect cross-inlining pairs with a precision of 81% and a recall of 97%, which exceeds all state-of-the-art works.Comment: Accepted at ICSE 2024 (Second Cycle). Camera-ready versio

Scutellarin regulates microglia-mediated TNC1 astrocytic reaction and astrogliosis in cerebral ischemia in the adult rats

Additional file 1: (A). Scutellarin at 0.54 mM did not elicit a noticeable reaction of GFAP/iNOS in TNC1. (B). iNOS mRNA expression in TNC1 astrocytes remained relatively unchanged at all time-points following treatment with BM, BM + L and CM; however, when incubated with CM + L for various time points, TNC1 showed a remarkable increase in iNOS peaking at 24 h. (C). Confocal images showing iNOS (C1-3) expression in TNC1 astrocytes incubated with different medium for 24 h. Compared with cells incubated in BM (C1) and BM + L (C2), TNC1 astrocytes incubated with CM + L (C3) were hypertrophic and showed a marked increase in iNOS immunofluorescence. Scale bars: 20 μm. DAPI—blue

Comparing One with Many -- Solving Binary2source Function Matching Under Function Inlining

Binary2source function matching is a fundamental task for many security applications, including Software Component Analysis (SCA). The "1-to-1" mechanism has been applied in existing binary2source matching works, in which one binary function is matched against one source function. However, we discovered that such mapping could be "1-to-n" (one query binary function maps multiple source functions), due to the existence of function inlining. To help conduct binary2source function matching under function inlining, we propose a method named O2NMatcher to generate Source Function Sets (SFSs) as the matching target for binary functions with inlining. We first propose a model named ECOCCJ48 for inlined call site prediction. To train this model, we leverage the compilable OSS to generate a dataset with labeled call sites (inlined or not), extract several features from the call sites, and design a compiler-opt-based multi-label classifier by inspecting the inlining correlations between different compilations. Then, we use this model to predict the labels of call sites in the uncompilable OSS projects without compilation and obtain the labeled function call graphs of these projects. Next, we regard the construction of SFSs as a sub-tree generation problem and design root node selection and edge extension rules to construct SFSs automatically. Finally, these SFSs will be added to the corpus of source functions and compared with binary functions with inlining. We conduct several experiments to evaluate the effectiveness of O2NMatcher and results show our method increases the performance of existing works by 6% and exceeds all the state-of-the-art works

Metabolomic Analysis Uncovers Energy Supply Disturbance as an Underlying Mechanism of the Development of Alcohol‐Associated Liver Cirrhosis

Alcohol-associated liver disease (ALD) is caused by alcohol metabolism's effects on the liver. The underlying mechanisms from a metabolic view in the development of alcohol-associated liver cirrhosis (ALC) are still elusive. We performed an untargeted serum metabolomic analysis in 14 controls, 16 patients with ALD without cirrhosis (NC), 27 patients with compensated cirrhosis, and 79 patients with decompensated ALC. We identified two metabolic fingerprints associated with ALC development (38 metabolites) and those associated with hepatic decompensation (64 metabolites) in ALC. The cirrhosis-associated fingerprint (eigenmetabolite) showed a better capability to differentiate ALC from NC than the aspartate aminotransferase-to-platelet ratio index score. The eigenmetabolite associated with hepatic decompensation showed an increasing trend during the disease progression and was positively correlated with the Model for End-Stage Liver Disease score. These metabolic fingerprints belong to the metabolites in lipid metabolism, amino acid pathway, and intermediary metabolites in the tricarboxylic acid cycle. Conclusion: The metabolomic fingerprints suggest the disturbance of the metabolites associated with cellular energy supply as an underlying mechanism in the development and progression of alcoholic cirrhosis

An outbreak of Streptococcus pyogenes in a mental health facility : advantage of well-timed whole-genome sequencing over emm typing

Financial support: The outbreak investigation was supported by Institute of Mental Health.OBJECTIVE:  We report the utility of whole-genome sequencing (WGS) conducted in a clinically relevant time frame (ie, sufficient for guiding management decision), in managing a Streptococcus pyogenes outbreak, and present a comparison of its performance with emm typing. SETTING:  A 2,000-bed tertiary-care psychiatric hospital. METHODS:  Active surveillance was conducted to identify new cases of S. pyogenes. WGS guided targeted epidemiological investigations, and infection control measures were implemented. Single-nucleotide polymorphism (SNP)-based genome phylogeny, emm typing, and multilocus sequence typing (MLST) were performed. We compared the ability of WGS and emm typing to correctly identify person-to-person transmission and to guide the management of the outbreak. RESULTS:  The study included 204 patients and 152 staff. We identified 35 patients and 2 staff members with S. pyogenes. WGS revealed polyclonal S. pyogenes infections with 3 genetically distinct phylogenetic clusters (C1-C3). Cluster C1 isolates were all emm type 4, sequence type 915 and had pairwise SNP differences of 0-5, which suggested recent person-to-person transmissions. Epidemiological investigation revealed that cluster C1 was mediated by dermal colonization and transmission of S. pyogenes in a male residential ward. Clusters C2 and C3 were genomically diverse, with pairwise SNP differences of 21-45 and 26-58, and emm 11 and mostly emm120, respectively. Clusters C2 and C3, which may have been considered person-to-person transmissions by emm typing, were shown by WGS to be unlikely by integrating pairwise SNP differences with epidemiology. CONCLUSIONS:  WGS had higher resolution than emm typing in identifying clusters with recent and ongoing person-to-person transmissions, which allowed implementation of targeted intervention to control the outbreak.PostprintPeer reviewe

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Performance of the CMS muon trigger system in proton-proton collisions at √s = 13 TeV

The muon trigger system of the CMS experiment uses a combination of hardware and software to identify events containing a muon. During Run 2 (covering 2015-2018) the LHC achieved instantaneous luminosities as high as 2 × 10 cm s while delivering proton-proton collisions at √s = 13 TeV. The challenge for the trigger system of the CMS experiment is to reduce the registered event rate from about 40 MHz to about 1 kHz. Significant improvements important for the success of the CMS physics program have been made to the muon trigger system via improved muon reconstruction and identification algorithms since the end of Run 1 and throughout the Run 2 data-taking period. The new algorithms maintain the acceptance of the muon triggers at the same or even lower rate throughout the data-taking period despite the increasing number of additional proton-proton interactions in each LHC bunch crossing. In this paper, the algorithms used in 2015 and 2016 and their improvements throughout 2017 and 2018 are described. Measurements of the CMS muon trigger performance for this data-taking period are presented, including efficiencies, transverse momentum resolution, trigger rates, and the purity of the selected muon sample. This paper focuses on the single- and double-muon triggers with the lowest sustainable transverse momentum thresholds used by CMS. The efficiency is measured in a transverse momentum range from 8 to several hundred GeV