111 research outputs found
Approximate Analyzing of Labeled Transition Systems
As the most important formal semantic model, labeled transition systems are
widely used, which can describe the general concurrent systems or control systems
without disturbance. However, under normal circumstance, transition systems
are complex and difficult to use due to large amount of calculation and the state
space explosion problems. In order to overcome these problems, approximate
equivalent labeled transition systems are proposed by means of incomplete low-up
matrix decomposition factorization. This technique can reduce the complexity of
computation and calculate under the allowing errors. As for continuous-time linear
systems, we develop a modeling method of approximated transition system based
on the approximate solution of matrix, which provides a facility for approximately
formal semantic modeling for linear systems and to effectively analyze errors.
An example of application in the context of linear systems without disturbances is
studied
InviCloak: An End-to-End Approach to Privacy and Performance in Web Content Distribution
In today's web ecosystem, a website that uses a Content Delivery Network
(CDN) shares its Transport Layer Security (TLS) private key or session key with
the CDN. In this paper, we present the design and implementation of InviCloak,
a system that protects the confidentiality and integrity of a user and a
website's private communications without changing TLS or upgrading a CDN.
InviCloak builds a lightweight but secure and practical key distribution
mechanism using the existing DNS infrastructure to distribute a new public key
associated with a website's domain name. A web client and a website can use the
new key pair to build an encryption channel inside TLS. InviCloak accommodates
the current web ecosystem. A website can deploy InviCloak unilaterally without
a client's involvement to prevent a passive attacker inside a CDN from
eavesdropping on their communications. If a client also installs InviCloak's
browser extension, the client and the website can achieve end-to-end
confidential and untampered communications in the presence of an active
attacker inside a CDN. Our evaluation shows that InviCloak increases the median
page load times (PLTs) of realistic web pages from 2.0s to 2.1s, which is
smaller than the median PLTs (2.8s) of a state-of-the-art TEE-based solution
Gitor: Scalable Code Clone Detection by Building Global Sample Graph
Code clone detection is about finding out similar code fragments, which has
drawn much attention in software engineering since it is important for software
maintenance and evolution. Researchers have proposed many techniques and tools
for source code clone detection, but current detection methods concentrate on
analyzing or processing code samples individually without exploring the
underlying connections among code samples. In this paper, we propose Gitor to
capture the underlying connections among different code samples. Specifically,
given a source code database, we first tokenize all code samples to extract the
pre-defined individual information. After obtaining all samples individual
information, we leverage them to build a large global sample graph where each
node is a code sample or a type of individual information. Then we apply a node
embedding technique on the global sample graph to extract all the samples
vector representations. After collecting all code samples vectors, we can
simply compare the similarity between any two samples to detect possible clone
pairs. More importantly, since the obtained vector of a sample is from a global
sample graph, we can combine it with its own code features to improve the code
clone detection performance. To demonstrate the effectiveness of Gitor, we
evaluate it on a widely used dataset namely BigCloneBench. Our experimental
results show that Gitor has higher accuracy in terms of code clone detection
and excellent execution time for inputs of various sizes compared to existing
state-of-the-art tools. Moreover, we also evaluate the combination of Gitor
with other traditional vector-based clone detection methods, the results show
that the use of Gitor enables them detect more code clones with higher F1.Comment: 12 pages, 5 figure
Obfuscation-resilient Android Malware Analysis Based on Contrastive Learning
Due to its open-source nature, Android operating system has been the main
target of attackers to exploit. Malware creators always perform different code
obfuscations on their apps to hide malicious activities. Features extracted
from these obfuscated samples through program analysis contain many useless and
disguised features, which leads to many false negatives. To address the issue,
in this paper, we demonstrate that obfuscation-resilient malware analysis can
be achieved through contrastive learning. We take the Android malware
classification as an example to demonstrate our analysis. The key insight
behind our analysis is that contrastive learning can be used to reduce the
difference introduced by obfuscation while amplifying the difference between
malware and benign apps (or other types of malware).
Based on the proposed analysis, we design a system that can achieve robust
and interpretable classification of Android malware. To achieve robust
classification, we perform contrastive learning on malware samples to learn an
encoder that can automatically extract robust features from malware samples. To
achieve interpretable classification, we transform the function call graph of a
sample into an image by centrality analysis. Then the corresponding heatmaps
are obtained by visualization techniques. These heatmaps can help users
understand why the malware is classified as this family. We implement IFDroid
and perform extensive evaluations on two widely used datasets. Experimental
results show that IFDroid is superior to state-of-the-art Android malware
familial classification systems. Moreover, IFDroid is capable of maintaining
98.2% true positive rate on classifying 8,112 obfuscated malware samples
A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literature
This study presents a novel approach that synergizes community detection
algorithms with various Graph Neural Network (GNN) models to bolster link
prediction in scientific literature networks. By integrating the Louvain
community detection algorithm into our GNN frameworks, we consistently enhance
performance across all models tested. For example, integrating Louvain with the
GAT model resulted in an AUC score increase from 0.777 to 0.823, exemplifying
the typical improvements observed. Similar gains are noted when Louvain is
paired with other GNN architectures, confirming the robustness and
effectiveness of incorporating community-level insights. This consistent uplift
in performance reflected in our extensive experimentation on bipartite graphs
of scientific collaborations and citations highlights the synergistic potential
of combining community detection with GNNs to overcome common link prediction
challenges such as scalability and resolution limits. Our findings advocate for
the integration of community structures as a significant step forward in the
predictive accuracy of network science models, offering a comprehensive
understanding of scientific collaboration patterns through the lens of advanced
machine learning techniques
Approximate Equivalence of the Hybrid Automata with Taylor Theory
Hybrid automaton is a formal model for precisely describing a hybrid
system in which the computational processes interact with the physical
ones. The reachability analysis of the polynomial hybrid automaton is
decidable, which makes the Taylor approximation of a hybrid automaton
applicable and valuable. In this paper, we studied the simulation relation
among the hybrid automaton and its Taylor approximation, as well as
the approximate equivalence relation. We also proved that the Taylor approximation simulates its original hybrid automaton, and similar hybrid
automata could be compared quantitatively, for example, the approximate equivalence we proposed in the paper
Automata-Based Analysis of Stage Suspended Boom Systems
A stage suspended boom system is an automatic steeve system orchestrated by the PLC (programmable logic controller). Security and fault-recovering are two important properties. In this paper, we analyze and verify the boom system formally. We adopt the hybrid automaton to model the boom system. The forward reachability is used to verify the properties with the reachable states. We also present a case study to illustrate the feasibility of the proposed verification
Towards Understanding the Capability of Large Language Models on Code Clone Detection: A Survey
Code cloning, the duplication of code fragments, is common in software
development. While some reuse aids productivity, excessive cloning hurts
maintainability and introduces bugs. Hence, automatic code clone detection is
vital. Meanwhile, large language models (LLMs) possess diverse code-related
knowledge, making them versatile for various software engineering challenges.
However, LLMs' performance in code clone detection is unclear and needs more
study for accurate assessment. In this paper, we provide the first
comprehensive evaluation of LLMs for clone detection, covering different clone
types, languages, and prompts. We find advanced LLMs excel in detecting complex
semantic clones, surpassing existing methods. Adding intermediate reasoning
steps via chain-of-thought prompts noticeably enhances performance.
Additionally, representing code as vector embeddings, especially with text
encoders, effectively aids clone detection.Lastly, the ability of LLMs to
detect code clones differs among various programming languages. Our study
suggests that LLMs have potential for clone detection due to their language
capabilities, offering insights for developing robust LLM-based methods to
enhance software engineering.Comment: 13 pages, 3 figure
Long-term effects of different hypoglycemic drugs on carotid intima-media thickness progression: a systematic review and network meta-analysis
ObjectiveThe progression of carotid intima-media thickness (cIMT) can partially predict the occurrence of future cardiovascular events. This network meta-analysis compared the effects of 14 antidiabetic drugs (acarbose, alogliptin, exenatide, glibenclamide, glimepiride, ipragliflozin, metformin, nateglinide, pioglitazone, rosiglitazone, sitagliptin, tofoglifozin, troglitazone, voglibose) on the progression of cIMT.MethodPubMed, EMBASE, Cochrane Library, and Web of Science were searched to screen all clinical trials of treatment of cIMT with hypoglycemic agents before March 1, 2024. The differences in the changes in cIMT between the treatment group and control group were evaluated.ResultAfter screening 8395 citations, 25 studies (6675 patients) were included. The results indicated that exenatide had the best efficacy in slowing down cIMT progress, and exenatide [MD=-0.13,95%CI (-0.25, -0.01)], alogliptin [MD=-0.08,95%CI (-0.13, -0.02)] and metformin [MD=-0.05, 95%CI (-0.09, -0.02)] are more effective than placebo.ConclusionLong-term treatment of exenatide, alogliptin, and metformin may be more effective than other hypoglycemic drugs in slowing the progression of cIMT.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024519474
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