Static Semantics Reconstruction for Enhancing JavaScript-WebAssembly Multilingual Malware Detection

The emergence of WebAssembly allows attackers to hide the malicious functionalities of JavaScript malware in cross-language interoperations, termed JavaScript-WebAssembly multilingual malware (JWMM). However, existing anti-virus solutions based on static program analysis are still limited to monolingual code. As a result, their detection effectiveness decreases significantly against JWMM. The detection of JWMM is challenging due to the complex interoperations and semantic diversity between JavaScript and WebAssembly. To bridge this gap, we present JWBinder, the first technique aimed at enhancing the static detection of JWMM. JWBinder performs a language-specific data-flow analysis to capture the cross-language interoperations and then characterizes the functionalities of JWMM through a unified high-level structure called Inter-language Program Dependency Graph. The extensive evaluation on one of the most representative real-world anti-virus platforms, VirusTotal, shows that \system effectively enhances anti-virus systems from various vendors and increases the overall successful detection rate against JWMM from 49.1\% to 86.2\%. Additionally, we assess the side effects and runtime overhead of JWBinder, corroborating its practical viability in real-world applications.Comment: Accepted to ESORICS 202

Differences in how interventions coupled with effective reproduction numbers account for marked variations in COVID-19 epidemic outcomes

The COVID-19 outbreak, designated a “pandemic” by the World Health Organization (WHO) on 11 March 2020, has spread worldwide rapidly. Each country implemented prevention and control strategies, mainly classified as SARS LCS (SARS-like containment strategy) or PAIN LMS (pandemic influenza-like mitigation strategy). The reasons for variation in each strategy’s efficacy in controlling COVID-19 epidemics were unclear and are investigated in this paper. On the basis of the daily number of confirmed local (imported) cases and onset-to-confirmation distributions for local cases, we initially estimated the daily number of local (imported) illness onsets by a deconvolution method for mainland China, South Korea, Japan and Spain, and then estimated the effective reproduction numbers Rt by using a Bayesian method for each of the four countries. China and South Korea adopted a strict SARS LCS, to completely block the spread via lockdown, strict travel restrictions and by detection and isolation of patients, which led to persistent declines in effective reproduction numbers. In contrast, Japan and Spain adopted a typical PAIN LMS to mitigate the spread via maintaining social distance, self-quarantine and isolation etc., which reduced the Rt values but with oscillations around 1. The finding suggests that governments may need to consider multiple factors such as quantities of medical resources, the likely extent of the public’s compliance to different intensities of intervention measures, and the economic situation to design the most appropriate policies to fight COVID-19 epidemics

How ChatGPT is Solving Vulnerability Management Problem

Recently, ChatGPT has attracted great attention from the code analysis domain. Prior works show that ChatGPT has the capabilities of processing foundational code analysis tasks, such as abstract syntax tree generation, which indicates the potential of using ChatGPT to comprehend code syntax and static behaviors. However, it is unclear whether ChatGPT can complete more complicated real-world vulnerability management tasks, such as the prediction of security relevance and patch correctness, which require an all-encompassing understanding of various aspects, including code syntax, program semantics, and related manual comments. In this paper, we explore ChatGPT's capabilities on 6 tasks involving the complete vulnerability management process with a large-scale dataset containing 78,445 samples. For each task, we compare ChatGPT against SOTA approaches, investigate the impact of different prompts, and explore the difficulties. The results suggest promising potential in leveraging ChatGPT to assist vulnerability management. One notable example is ChatGPT's proficiency in tasks like generating titles for software bug reports. Furthermore, our findings reveal the difficulties encountered by ChatGPT and shed light on promising future directions. For instance, directly providing random demonstration examples in the prompt cannot consistently guarantee good performance in vulnerability management. By contrast, leveraging ChatGPT in a self-heuristic way -- extracting expertise from demonstration examples itself and integrating the extracted expertise in the prompt is a promising research direction. Besides, ChatGPT may misunderstand and misuse the information in the prompt. Consequently, effectively guiding ChatGPT to focus on helpful information rather than the irrelevant content is still an open problem

TikTalk: A Video-Based Dialogue Dataset for Multi-Modal Chitchat in Real World

To facilitate the research on intelligent and human-like chatbots with multi-modal context, we introduce a new video-based multi-modal dialogue dataset, called TikTalk. We collect 38K videos from a popular video-sharing platform, along with 367K conversations posted by users beneath them. Users engage in spontaneous conversations based on their multi-modal experiences from watching videos, which helps recreate real-world chitchat context. Compared to previous multi-modal dialogue datasets, the richer context types in TikTalk lead to more diverse conversations, but also increase the difficulty in capturing human interests from intricate multi-modal information to generate personalized responses. Moreover, external knowledge is more frequently evoked in our dataset. These facts reveal new challenges for multi-modal dialogue models. We quantitatively demonstrate the characteristics of TikTalk, propose a video-based multi-modal chitchat task, and evaluate several dialogue baselines. Experimental results indicate that the models incorporating large language models (LLM) can generate more diverse responses, while the model utilizing knowledge graphs to introduce external knowledge performs the best overall. Furthermore, no existing model can solve all the above challenges well. There is still a large room for future improvements, even for LLM with visual extensions. Our dataset is available at \url{https://ruc-aimind.github.io/projects/TikTalk/}.Comment: Accepted to ACM Multimedia 202

The Antihistamine Drugs Carbinoxamine Maleate and Chlorpheniramine Maleate Exhibit Potent Antiviral Activity Against a Broad Spectrum of Influenza Viruses

Influenza A viruses (IAV) comprise some of the most common infectious pathogens in humans, and they cause significant mortality and morbidity in immunocompromised people as well as children and the elderly. After screening an FDA-approved drug library containing 1280 compounds by cytopathic effect (CPE) reduction assay using the Cell Counting Kit-8, we found two antihistamines, carbinoxamine maleate (CAM) and S-(+)-chlorpheniramine maleate (SCM) with potent antiviral activity against A/Shanghai/4664T/2013(H7N9) infection with IC50 (half-maximal inhibitory concentration) of 3.56 and 11.84 μM, respectively. Further studies showed that CAM and SCM could also inhibit infection by other influenza A viruses, including A/Shanghai/37T/2009(H1N1), A/Puerto Rico/8/1934(H1N1), A/Guizhou/54/1989(H3N2), and one influenza B virus, B/Shanghai/2017(BY). Mice were challenged intranasally with A/H7N9/4664T/2013 (H7N9) virus and intraperitoneally injected with CAM (10 mg/kg per day) or SCM (1 mg/kg per day) for 5 days. CAM or SCM (10 mg/kg per day) were fully protected against challenge with A/Shanghai/4664T/2013(H7N9). The results from mechanistic studies indicate that both could inhibit influenza virus infection by blocking viral entry into the target cell, the early stage of virus life cycle. However, CAM and SCM neither blocked virus attachment, characteristic of HA activity, nor virus release, characteristic of NA activity. Such data suggest that these two compounds may interfere with the endocytosis process. Thus, we have identified two FDA-approved antihistamine drugs, CAM and SCM, which can be repurposed for inhibiting infection by divergent influenza A strains and one influenza B strain with potential to be used for treatment and prevention of influenza virus infection

Evaluation of a new fluorescence quantitative PCR test for diagnosing Helicobacter pylori infection in children

Abstract Background Numerous diagnostic tests are available to detect Helicobactor pylori (H. pylori). There has been no single test available to detect H. pylori infection reliably. We evaluated the accuracy of a new fluorescence quantitative PCR (fqPCR) for H. pylori detection in children. Methods Gastric biopsy specimens from 138 children with gastritis were sent for routine histology exam, rapid urease test (RUT) and fqPCR. 13C-urea breath test (13C-UBT) was carried out prior to endoscopic procedure. Gastric fluids and dental plaques were also collected for fqPCR analysis. Results 38 children (27.5%) were considered positive for H. pylori infection by gold standard (concordant positive results on 2 or more tests). The remaining 100 children (72.5%) were considered negative for H. pylori. Gastric mucosa fqPCR not only detected all 38 H. pylori positive patients but also detected 8 (8%) of the 100 gold standard-negative children or 11 (10.7%) of the 103 routine histology-negative samples. Therefore, gastric mucosa fqPCR identified 46 children (33.3%) with H. pylori infection, significantly higher than gold standard or routine histology (P<0.01). Both gastric fluid and dental plaque fqPCR only detected 32 (23.2%) and 30 (21.7%) children with H. pylori infection respectively and was significantly less sensitive than mucosa fqPCR (P<0.05) but was as sensitive as non-invasive UBT. Conclusions Gastric mucosa fqPCR was more sensitive than routine histology, RUT, 13C-UBT alone or in combination to detect H. pylori infection in children with chronic gastritis. Either gastric fluid or dental plaque PCR is as reliable as 13C-UBT for H. pylori detection.Peer Reviewe

The Immungenicity and Cross-Neutralizing Activity of Enterovirus 71 Vaccine Candidate Strains

This study aimed to evaluate enterovirus 71 (EV-A71) vaccine candidate strains, including their genotypes, immunogenicity and cross-neutralization capacity. From clinical samples, EV-A71 strains were separated by using Vero cells. Six strains were chosen for vaccine candidates, and the sequences were analyzed. To detect the immunogenicity of the strains, we used them to immunize NIH mice at 0 and 14 days. Cytopathic effects (CPE) were examined to determine the EV-A71 neutralizing antibody (NTAb) titer 14 d after the first and second inoculations. To evaluate the cross-neutralizing capacity of the EV-A71 vaccine candidate strains, we tested serum immunized mice with ten EV-A71 genotype strains. Six EV-A71 vaccine candidate strains were identified, all belonging to sub-genotype C4, the prevalent genotype in China. The sequence similarity of the VP1 regions of the six candidate vaccine strains and three approved inactivated vaccines was 97.58%–97.77%, and the VP1 amino acid similarity was 98.65%–99.33%. Experiments were performed to evaluate the immunogenicity and cross-neutralizing activity of the EV-A71 vaccine candidate strains. The strains had good immunogenicity 14 d after two immunizations, inducing an NTAb titer ranging from 1:94 to 1:346. The NTAb seroconversion rates 14 d after one immunization were above 80% (except HB0007), and significantly increased immunogenicity of EV-A71 strains was observed post-inoculation. Furthermore, our candidate vaccine strains had broad cross-neutralizing activity after challenge with ten sub-genotypes of EV-A71. The highest NTAb titer/lowest NTAb titer ratios of sera against EV-A71 sub-genotypes were 8.0 (JS0002), 8.0 (JS0005), 21.3 (HB0005), 21.3 (HB0007), 10.7 (HB0040) and 8.0 (GD0002), respectively. Our EV-A71 strains had good immunogenicity and cross-neutralization activity, and have the potential to serve as vaccine strains for multivalent hand, foot and mouth disease vaccines

Clinical and radiological characteristics of pediatric COVID-19 before and after the Omicron outbreak: a multi-center study

IntroductionThe emergence of the Omicron variant has seen changes in the clinical and radiological presentations of COVID-19 in pediatric patients. We sought to compare these features between patients infected in the early phase of the pandemic and those during the Omicron outbreak.MethodsA retrospective study was conducted on 68 pediatric COVID-19 patients, of which 31 were infected with the original SARS-CoV-2 strain (original group) and 37 with the Omicron variant (Omicron group). Clinical symptoms and chest CT scans were examined to assess clinical characteristics, and the extent and severity of lung involvement.ResultsPediatric COVID-19 patients predominantly had normal or mild chest CT findings. The Omicron group demonstrated a significantly reduced CT severity score than the original group. Ground-glass opacities were the prevalent radiological findings in both sets. The Omicron group presented with fewer symptoms, had milder clinical manifestations, and recovered faster than the original group.DiscussionThe clinical and radiological characteristics of pediatric COVID-19 patients have evolved with the advent of the Omicron variant. For children displaying severe symptoms warranting CT examinations, it is crucial to weigh the implications of ionizing radiation and employ customized scanning protocols and protective measures. This research offers insights into the shifting disease spectrum, aiding in the effective diagnosis and treatment of pediatric COVID-19 patients