GRATDet:Smart Contract Vulnerability Detector Based on Graph Representation and Transformer

Smart contracts have led to more efficient development in finance and healthcare,but vulnerabilities in contracts pose high risks to their future applications.The current vulnerability detection methods for contracts are either based on fixed expert rules,which are inefficient,or rely on simplistic deep learning techniques that do not fully leverage contract semantic information.Therefore,there is ample room for improvement in terms of detection precision.To solve these problems,this paper proposes a vulnerability detector based on deep learning techniques,graph representation,and Transformer,called GRATDet.The method first performs swapping,insertion,and symbolization operations for contract functions,increasing the amount of small sample data.Each line of code is then treated as a basic semantic element,and information such as control and data relationships is extracted to construct a new representation in the form of a Line Graph(LG),which shows more structural features that differ from the serialized presentation of the contract.Finally,the node information and edge information of the graph are jointly learned using an improved Transformer-GP model to extract information globally and locally,and the fused features are used for vulnerability detection.The effectiveness of the method in reentrancy vulnerability detection is verified in experiments,where the F1 score reaches 95.16%,exceeding stateof-the-art methods.

DFE-GCN: Dual Feature Enhanced Graph Convolutional Network for Controversy Detection

With the development of social media and the prevalence of mobile devices,an increasing number of people tend to use social media platforms to express their opinions and attitudes,leading to many online controversies.These online controversies can severely threaten social stability,making automatic detection of controversies particularly necessary.Most controversy detection methods currently focus on mining features from text semantics and propagation structures.However,these methods have two drawbacks:1)limited ability to capture structural features and failure to learn deeper structural features,and 2)neglecting the influence of topic information and ineffective utilization of topic features.In light of these phenomena,this paper proposes a social media controversy detection method called Dual Feature Enhanced Graph Convolutional Network(DFE-GCN).This method explores structural information at different scales from global and local perspectives to capture deeper structural features,enhancing the expressive power of structural features.Furthermore,to strengthen the influence of topic information,this paper utilizes attention mechanisms to enhance topic features after each graph convolutional layer,effectively using topic information.We validated our method on two different public datasets,and the experimental results demonstrate that our method achieves state-of-the-art performance compared to baseline methods.On the Weibo and Reddit datasets,the accuracy is improved by 5.92%and 3.32%,respectively,and the F1 score is improved by 1.99%and 2.17%,demonstrating the positive impact of enhanced structural features and topic features on controversy detection.

The Detection of Fraudulent Smart Contracts Based on ECA-EfficientNet and Data Enhancement

With the increasing popularity of Ethereum,smart contracts have become a prime target for fraudulent activities such as Ponzi,honeypot,gambling,and phishing schemes.While some researchers have studied intelligent fraud detection,most research has focused on identifying Ponzi contracts,with little attention given to detecting and preventing gambling or phishing contracts.There are three main issues with current research.Firstly,there exists a severe data imbalance between fraudulent and non-fraudulent contracts.Secondly,the existing detection methods rely on diverse raw features that may not generalize well in identifying various classes of fraudulent contracts.Lastly,most prior studies have used contract source code as raw features,but many smart contracts only exist in bytecode.To address these issues,we propose a fraud detection method that utilizes Efficient Channel Attention EfficientNet(ECA-EfficientNet)and data enhancement.Our method begins by converting bytecode into Red Green Blue(RGB)three-channel images and then applying channel exchange data enhancement.We then use the enhanced ECA-EfficientNet approach to classify fraudulent smart contract RGB images.Our proposed method achieves high F1-score and Recall on both publicly available Ponzi datasets and self-built multi-classification datasets that include Ponzi,honeypot,gambling,and phishing smart contracts.The results of the experiments demonstrate that our model outperforms current methods and their variants in Ponzi contract detection.Our research addresses a significant problem in smart contract security and offers an effective and efficient solution for detecting fraudulent contracts.

Heptamethine cyanines in bioorthogonal chemistry

Due to their excellent fluorescence properties and biological function,cyanine dyes have been widely applied in biological imaging.Heptamethine cyanine(Cy7)dyes,as a type of classic near-infrared(NIR)fluorescent dyes,are considered as one of the effective fluorescent tools in the living organisms due to their good biocompatibility and very low background interference.Bioorthogonal reactions performed in living cells and tissues have developed by leaps and bounds in recent years.The NIR fluorescent labeling technique involving cyanine has attracted widespread attention.This review summarizes their recent application in the field of bioorthogonal imaging,mainly concluding Cy7-type dyes,labeling strategy,bioimaging application,etc.We expect this work can provide some helps for the studies of NIR bioorthogonal reaction in vivo.

tuning the properties of confined water in standard and lybrid nanotubes： An infrared spectroscopic study

Imogolite is a natural nanotubular aluminum silicate clay mineral found in volcanic soils. Its well-defined, tunable structure makes it a good candidate for studying water confinement in a one-dimensional （1D） structure. Water confinement in self-sustaining imogolite thin films was studied using infrared spectroscopy. Two types of synthetic imogolites were investigated： pristine imogolite （IMO-OH） with a hydrophilic inner surface covered with Si-OH groups and hybrid imogolite （IMO-CH3） with a hydrophobic inner surface covered with Si-CH3 groups. Both imogolites have an outer surface that is covered with Al-OH groups. Infrared spectra were recorded in the 20-4,000 cm^-1 spectral range as a function of relative humidity. Analysis of the O-H stretching band provides information on the H bonding of confined water molecules inside and outside the IMO-OH tubes. The scenario for water filling as a function of relative humidity is determined for both systems. Adsorption begins in the IMO-OH tubes at the lowest relative humidity （〈 10%）. The inner surface of the tubes is first covered with water molecules; then, the central part of the tubes is filled, leading to very strong H bonds and a structured spectrum. In contrast, the H bonds of water adsorbed on the outer surfaces of these tubes are weaker. A different scenario is observed for water inside IMO-CH3： Weakly H-bonded water molecules are present, similar to that observed in carbon nanotubes. Water confinement in imogolites is governed by the hydrophilicity of the inner walls. At similar partial pressures, the degree of H bonding depends on the interactions between water and the nanotube wall.