IoT devices have become deeply integrated into our daily lives, making comprehensive security research on critical infrastructure devices increasingly important. Static analysis techniques, particularly those leveraging taint propagation, have demonstrated promise in identifying security vulnerabilities within these devices, effectively detecting critical vulnerabilities. However, current solutions often struggle with limitations in both detection efficiency and accuracy. To address these challenges, this paper introduces Liva, a novel static taint analysis tool designed for detecting web vulnerabilities in IoT devices. Liva employs a large language model (LLM) multi-agent approach for static binary taint analysis, primarily leveraging fine-tuned open-source models and commercial LLMs to improve source/sink identification and taint data analysis—areas where traditional methods often fall short—thereby enhancing overall analysis efficiency. LIVA's core analysis engine leverages a Qwen3-32B open-source model that has been fine-tuned using a dataset of 3,000 real-world device samples. This fine-tuned model achieves a 3 percentage point improvement in accuracy for identifying taint data propagation relationships compared to commercial LLMs, while also increasing average analysis efficiency by 5.5%. A comprehensive evaluation of Liva, conducted on a dataset of 64 devices from 11 vendors, revealed that it detected 309 and 349 more known vulnerabilities than the state-of-the-art solutions SaTC and Karonte, respectively, while simultaneously reducing false positive rates by 59.4% and 67.6%. Liva achieves a recall of 98.1% and a precision of 74.6%, with a 6.7× reduction in analysis time compared to the best-performing baseline. Furthermore, in the realm of zero-day vulnerability detection, Liva discovered 64 previously unknown vulnerabilities, 39 of which have since been assigned official CVE/CNVD identifiers.

Heritability, the proportion of phenotypic variance explained by genome-wide single nucleotide polymorphisms (SNPs) in unrelated individuals, is an important measure of the genetic contribution to human diseases and plays a critical role in studying the genetic architecture of human diseases. Linear mixed model (LMM) has been widely used for SNP heritability estimation, where variance component parameters are commonly estimated by using a restricted maximum likelihood (REML) method. REML is an iterative optimization algorithm, which is computationally intensive when applied to large-scale datasets (e.g. UK Biobank). To facilitate the heritability analysis of large-scale genetic datasets, we develop a fast approach, minimum norm quadratic unbiased estimator (MINQUE) with batch training, to estimate variance components from LMM (LMM.MNQ.BCH). In LMM.MNQ.BCH, the parameters are estimated by MINQUE, which has a closed-form solution for fast computation and has no convergence issue. Batch training has also been adopted in LMM.MNQ.BCH to accelerate the computation for large-scale genetic datasets. Through simulations and real data analysis, we demonstrate that LMM.MNQ.BCH is much faster than two existing approaches, GCTA and BOLT-REML.

Over the past decade, statistical methods have been developed to estimate single nucleotide polymorphism (SNP) heritability, which measures the proportion of phenotypic variance explained by all measured SNPs in the data. Estimates of SNP heritability measure the degree to which the available genetic variants influence phenotypes and improve our understanding of the genetic architecture of complex phenotypes. In this article, we review the recently developed and commonly used SNP heritability estimation methods for continuous and binary phenotypes from the perspective of model assumptions and parameter optimization. We primarily focus on their capacity to handle multiple phenotypes and longitudinal measurements, their ability for SNP heritability partition and their use of individual-level data versus summary statistics. State-of-the-art statistical methods that are scalable to the UK Biobank dataset are also elucidated in detail.

In order to select effective policies for emergency management in a timely manner, this paper proposes an agile and lightweight social computing approach to facilitating policy selection, evaluation, and adjustment relative to emergency management in both quantitative and qualitative ways. The approach consists of three components represented as PZE: 1) (P) emergency management policy selecting; 2) (Z) modeling artificial societies with the zombie-city model (a general and formal artificial society model); and 3) (E) policy evaluation. The formal specification of the zombie-city model and rigorous expressions of scenarios enable rigorous description and formal reasoning of an artificial society. A feedback loop of this approach supports the iterative adjustment of emergency management policies and the creation of more effective policies. This approach is verified by applying it to a case of an infectious disease transmission with quantitative evaluations, qualitative reasoning and analysis, and iterative adjustments. Results indicate effective emergency management policies can be established with the approach in an iterative way. In contrast with existing research, our proposed approach offers the benefits of being simple, general, rapidly adaptive to changes, and low cost.

Social propagation issue that appears in both the real world and the cyberspace recently gains great attentions in several communities. It typically is involved with a great number of autonomous participated individuals and their local interactions, and may arise global emergent behaviors or properties. It is therefore necessary to investigate what emergence may occur and how to influence the emergence in the social propagation. This paper proposes an agent-based artificial society approach to analyzing social propagation issue, which includes AVI (Agent-Virus-Interaction) model for modeling social propagation and the systematic method to investigate the emergence. The AVI model that borrows ideas from multiagent and organization metaphors has three core concepts: agent, virus and interaction, representing the propagation carrier, content, and channel, respectively. Based on the AVI model, a systematic method for analyzing social propagation that consists of five analysis activities including modeling artificial society, programming simulation codes, designing experiments, performing simulations, and analyzing results, is proposed. We study a case of an infectious virus propagation to illustrate the approach and show its usability and effectiveness. Besides, we design and conduct a number of experiments to analyze what/how factors influence the infectious virus propagation. Finally, we discuss some potential extensions to the AVI model for satisfying further requirements of social propagation applications.

Choosing whom to tweet information to promote information spread on social media is an interesting and significant topic for both academic and industrial areas. Effective policies to choose whom to tweet information on social media should make more users to be aware of the information and to be willing to retweet it. Aiming at investigating how information spreads on social media, this paper proposes an interest-based dissemination model to depict the information spread process. The interest is introduced as the foundation of users' rationality to follow other users and retweet information. Meanwhile, this paper adopts artificial society and a lightweight social computing method to evaluate and analyze the effectiveness of policies, in which users on social media are modelled as agents with various social relationships and interests. Based on PZE approach, several policies for information promotion on social media have been made, and simulations are undertaken quantitatively to evaluate their effectiveness in various scenarios. The experimental results reveal that the effectiveness of a policy is tightly related to two kinds of users' rational behaviors: retweeting information and following other users.

The growing design-productivity gap has made designers shift toward using high-level synthesis (HLS) techniques to generate register transfer level design from high-level languages. Unfortunately, this translation process is very complex and may introduce bugs into the generated design, which can create a mismatch between what a designer intends and what is actually implemented in the circuit. In this paper, we present an equivalence checking method to validate the result of HLS scheduling against the initial high-level program. Finite state machine with data path (FSMD) models were used to represent designs before and after scheduling. The proposed method uses a bisimulation relation approach to prove equivalence. The automatically established bisimulation relation guarantees that for each execution sequence in the design before scheduling, a related and equivalent execution sequence exists in the design after scheduling and vice versa. Our method provides a unified way to deal with various scheduling optimizations. We have implemented our validation technique and compared it with a state-of-the-art HLS scheduling verification method. The promising results show the effectiveness and efficiency of our method.

Complexity issues in the real world cannot be completely solved by experiments on the real society. ACP approach (Artificial society for modelling, Computations experiment for analysis and Parallel execution for control) has been proposed to solve to eliminate the gap of micro-to-macro emergent behaviours. Artificial society modelling is the consitituent of the ACP approach. However, there are no widely accepted and well-established methods to conduct and standardize artificial society modelling. This paper describes the main aspects of artificial society modelling, including agent population, environment and event. Based on the social organization metaphor, it presents an artificial society modelling approach, including the modelling process. This approach can effectively aid and guide artificial society modelling.