A Review of Deep Learning-Based Vulnerability Detection Tools for Ethernet Smart Contracts

In recent years,the number of smart contracts deployed on blockchain has exploded.However,the issue of vulnerability has caused incalculable losses.Due to the irreversible and immutability of smart contracts,vulnerability detection has become particularly important.With the popular use of neural network model,there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts.This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts.Subsequently,it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection.These tools are categorized based on their open-source status,the data format and the type of feature extraction they employ.Then we conduct a comprehensive comparative analysis of these tools,selecting representative tools for experimental validation and comparing them with traditional tools in terms of detection coverage and accuracy.Finally,Based on the insights gained from the experimental results and the current state of research in the field of smart contract vulnerability detection tools,we suppose to provide a reference standard for developers of contract vulnerability detection tools.Meanwhile,forward-looking research directions are also proposed for deep learning-based smart contract vulnerability detection.

Preserving Data Secrecy and Integrity for Cloud Storage Using Smart Contracts and Cryptographic Primitives

Cloud computing has emerged as a viable alternative to traditional computing infrastructures,offering various benefits.However,the adoption of cloud storage poses significant risks to data secrecy and integrity.This article presents an effective mechanism to preserve the secrecy and integrity of data stored on the public cloud by leveraging blockchain technology,smart contracts,and cryptographic primitives.The proposed approach utilizes a Solidity-based smart contract as an auditor for maintaining and verifying the integrity of outsourced data.To preserve data secrecy,symmetric encryption systems are employed to encrypt user data before outsourcing it.An extensive performance analysis is conducted to illustrate the efficiency of the proposed mechanism.Additionally,a rigorous assessment is conducted to ensure that the developed smart contract is free from vulnerabilities and to measure its associated running costs.The security analysis of the proposed system confirms that our approach can securely maintain the confidentiality and integrity of cloud storage,even in the presence of malicious entities.The proposed mechanism contributes to enhancing data security in cloud computing environments and can be used as a foundation for developing more secure cloud storage systems.

Mitigating Blackhole and Greyhole Routing Attacks in Vehicular Ad Hoc Networks Using Blockchain Based Smart Contracts

The rapid increase in vehicle traffic volume in modern societies has raised the need to develop innovative solutions to reduce traffic congestion and enhance traffic management efficiency.Revolutionary advanced technology,such as Intelligent Transportation Systems(ITS),enables improved traffic management,helps eliminate congestion,and supports a safer environment.ITS provides real-time information on vehicle traffic and transportation systems that can improve decision-making for road users.However,ITS suffers from routing issues at the network layer when utilising Vehicular Ad Hoc Networks(VANETs).This is because each vehicle plays the role of a router in this network,which leads to a complex vehicle communication network,causing issues such as repeated link breakages between vehicles resulting from the mobility of the network and rapid topological variation.This may lead to loss or delay in packet transmissions;this weakness can be exploited in routing attacks,such as black-hole and gray-hole attacks,that threaten the availability of ITS services.In this paper,a Blockchain-based smart contracts model is proposed to offer convenient and comprehensive security mechanisms,enhancing the trustworthiness between vehicles.Self-Classification Blockchain-Based Contracts(SCBC)and Voting-Classification Blockchain-Based Contracts(VCBC)are utilised in the proposed protocol.The results show that VCBC succeeds in attaining better results in PDR and TP performance even in the presence of Blackhole and Grayhole attacks.

The Impact of Corporate Governance and Fair Value Accounting on Debt Contracts

This paper investigates the theoretical relationship between corporate governance,fair value accounting,and debt contracts.It primarily examines the individual impacts of corporate governance and fair value accounting on debt contracts,while also exploring the influence of corporate governance on fair value accounting.The study emphasizes the importance of considering the interests and legal status of creditors in the context of debt contracts.The findings indicate that strong corporate governance can reduce the likelihood of debt default and that the company’s restructuring costs in the event of a default determine whether improved corporate governance will increase or decrease debt costs.Additionally,the study reveals that the strength of corporate governance affects the value relevance of fair value accounting.However,the impact of fair value accounting on debt contracts is not inherently positive or negative;for instance,companies may use fair value adjustments with manipulative intent to enhance performance.Ultimately,the research highlights that discussions about corporate governance should not prioritize shareholder interests exclusively but also consider the legitimate position of creditors.

石家庄市R&D经费投入现状分析与对策研究

R&D经费投入强度,是国际上通用的衡量一个国家或者地区科技投入强度的重要指标,成为评价其科技实力、核心竞争力和创新程度的重要标准。本文在对石家庄市研发投入开展调研的基础上,对R&D经费投入开展总体分析、结构分析、横纵向比较分析,找出存在的问题,提出加大政府对科研活动的支持、大力发展新兴产业、重视研发投入统计等对策与建议。

政府R&D补贴的技术创新效应分析--基于2009-2021年中国的经验数据

政府R&D补贴既可以通过提供公共服务的方式直接作用于技术创新,也可以通过对社会资本的引导作用间接影响技术创新,其总效应取决于两种影响的方向与大小。基于2009—2021年中国的经验数据,建立了由技术创新方程和引致方程共同组成的动态模型,对政府R&D补贴的技术创新效应进行了实证分析。结果发现:持续增加的政府R&D补贴带来了国内专利授权数量的稳定增加,技术创新效应较为显著;政府R&D补贴不仅对技术创新产生了积极的直接影响,也通过引导社会资本投资于研发领域而对技术创新产生了积极的间接影响;政府R&D补贴的技术创新效应存在区域差异,中东部地区为正,而西部地区为负。提高资金使用效率、积极引导社会资本投资技术研发、努力改善欠发达地区的研发环境,无疑是提高政府R&D补贴技术创新效应的必要选择。

云南省R&D经费投入结构动态研究

为研究云南省“十二五”以来R&D经费投入结构变化,分析R&D经费投入现状,并与全国,东、中、西部地区及临近省份进行了对比;从资金来源和资金配置视角,运用Cobb-Douglas效用函数及其修正模型对R&D经费投入结构展开动态分析。结果表明:云南省R&D经费投入结构不尽合理,资金来源中政府资金占比较低且呈下降趋势,资金配置中基础研究和应用研究投入不足,与最优结构偏离程度较大。针对存在的问题,提出持续加大政府R&D经费投入力度、围绕产业链提升基础研究和应用研究比重的建议。

政府R&D资助对OFDI逆向绿色创新的影响机制研究

应用中国分省区层面数据并采用面板门槛模型,实证考察了政府R&D资助影响OFDI逆向绿色创新的动态效应。研究发现,OFDI对国内绿色创新产生了显著的驱动效应,但这种影响呈现出明显的先负后正的“U”型动态演化特征;政府R&D资助会正向调节OFDI逆向绿色创新溢出,且存在最适宜于OFDI逆向绿色创新的政府R&D资助区间(0.366,0.428],过高或过低强度的政府R&D资助均会在一定程度上造成OFDI逆向绿色创新的红利损失;政府R&D资助调节下OFDI对绿色创新的动态影响具有显著时空异质性,即在时空维度上存在“激励效应”与“挤出效应”交替演化的鲜明特征。

线性化严格收缩的Peaceman-Reachford分裂方法

考虑具有线性约束的三块可分凸优化问题,在改进的严格收缩可分离的凸最小化模型分裂法(MSC-PRSM)的基础上,将原始问题的y-子问题和z-子问题的目标函数分别在y^(k)和z^(k)处进行线性化,并增加一个邻近项,使线性化的MSC-PRSM子问题更容易求解,降低了计算量,从计算时间角度比MSC-PRSM更有优势,并证明了新算法的收敛性.

R&D资本化背景下我国TFP增长率再测算

研究与试验发展(R&D)资本对经济增长和科技创新作用日益凸显,但对于R&D资本的核算亟待完善。本文基于SNA2008和CSNA2016框架,将R&D支出作为资本形成,对我国各省份物质资本存量和R&D资本存量进行规范化测算,使用随机前沿分析法重新估算1991—2019年间的全要素生产率(TFP)增长率。研究发现:第一,我国R&D资本存量规模呈总体递增和地区聚集趋势;第二,1991—2019年间,我国广义TFP增长率年平均增幅为2.10%,对经济增长存在正向作用;第三,将TFP增长率分解为技术进步、前沿技术效率和规模效应,发现技术进步对TFP增长率的贡献最大,是推动我国TFP增长的主导力量,但却呈现逐年递减的趋势;第四,随着我国对R&D投入的不断增加与重视,R&D对我国广义TFP增长率的贡献呈现递增的趋势,R&D的贡献逐渐成为推动我国科技进步的主要力量。

砂型铸造Mg-9Gd-3Y-0.5Zr合金的线收缩

研究铸件几何特征对砂型铸造Mg-9Gd-3Y-0.5Zr(VW93)合金线收缩的影响。利用3D扫描仪精确提取自由收缩和受阻收缩砂铸件的尺寸,并用于计算收缩系数。引入砂芯体积与铸件包络体积之比(γ)量化砂芯对铸件收缩的约束程度。通过分析自由收缩系数的统计分布和受阻收缩系数随γ的变化规律发现,VW93合金的自由收缩系数为1.96%,受阻收缩系数随γ的增大而线性减小,此关系为根据铸件几何结构预测VW93合金收缩率提供支持。

A Framework for Enhancing Privacy and Anonymity in Blockchain-Enabled IoT Devices

With the increase in IoT(Internet of Things)devices comes an inherent challenge of security.In the world today,privacy is the prime concern of every individual.Preserving one’s privacy and keeping anonymity throughout the system is a desired functionality that does not come without inevitable trade-offs like scalability and increased complexity and is always exceedingly difficult to manage.The challenge is keeping confidentiality and continuing to make the person innominate throughout the system.To address this,we present our proposed architecture where we manage IoT devices using blockchain technology.Our proposed architecture works on and off blockchain integrated with the closed-circuit television(CCTV)security camera fixed at the rental property.In this framework,the CCTV security camera feed is redirected towards the owner and renter based on the smart contract conditions.One entity(owner or renter)can see the CCTV security camera feed at one time.There is no third-party dependence except for the CCTV security camera deployment phase.Our contributions include the proposition of framework architecture,a novel smart contract algorithm,and the modification to the ring signatures leveraging an existing cryptographic technique.Analyses are made based on different systems’security and key management areas.In an empirical study,our proposed algorithm performed better in key generation,proof generation,and verification times.By comparing similar existing schemes,we have shown the proposed architectures’advantages.Until now,we have developed this system for a specific area in the real world.However,this system is scalable and applicable to other areas like healthcare monitoring systems,which is part of our future work.

Bridge Bidding via Deep Reinforcement Learning and Belief Monte Carlo Search

Contract Bridge,a four-player imperfect information game,comprises two phases:bidding and playing.While computer programs excel at playing,bidding presents a challenging aspect due to the need for information exchange with partners and interference with communication of opponents.In this work,we introduce a Bridge bidding agent that combines supervised learning,deep reinforcement learning via self-play,and a test-time search approach.Our experiments demonstrate that our agent outperforms WBridge5,a highly regarded computer Bridge software that has won multiple world championships,by a performance of 0.98 IMPs(international match points)per deal over 10000 deals,with a much cost-effective approach.The performance significantly surpasses previous state-of-the-art(0.85 IMPs per deal).Note 0.1 IMPs per deal is a significant improvement in Bridge bidding.

2021年副省级城市R&D经费投入分析

2017—2021年,副省级城市的R&D经费投入都保持持续增长的态势。深圳的R&D经费投入在副省级城市中一直处于遥遥领先的地位,西安由于科研院所和高校众多,2017年R&D经费投入总量在副省级城市中排名第四,2021年排名第七,被资源禀赋相似的南京、成都、武汉等城市超越。西安由于GDP总量不高,R&D经费投入强度一直保持在5%左右,2020年被深圳反超。基于数据的可获得性,本文从2021年副省级城市的R&D经费投入总量、R&D经费投入强度等方面进行解读和分析,并结合分析结果提出相关建议,以供参考。

Automated Vulnerability Detection of Blockchain Smart Contacts Based on BERT Artificial Intelligent Model

The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields.Cryptographic algorithms and smart contracts are critical components of blockchain security.Despite the benefits of virtual currency,vulnerabilities in smart contracts have resulted in substantial losses to users.While researchers have identified these vulnerabilities and developed tools for detecting them,the accuracy of these tools is still far from satisfactory,with high false positive and false negative rates.In this paper,we propose a new method for detecting vulnerabilities in smart contracts using the BERT pre-training model,which can quickly and effectively process and detect smart contracts.More specifically,we preprocess and make symbol substitution in the contract,which can make the pre-training model better obtain contract features.We evaluate our method on four datasets and compare its performance with other deep learning models and vulnerability detection tools,demonstrating its superior accuracy.

A Review on the Security of the Ethereum-Based DeFi Ecosystem

Decentralized finance(DeFi)is a general term for a series of financial products and services.It is based on blockchain technology and has attracted people’s attention because of its open,transparent,and intermediary free.Among them,the DeFi ecosystem based on Ethereum-based blockchains attracts the most attention.However,the current decentralized financial system built on the Ethereum architecture has been exposed to many smart contract vulnerabilities during the last few years.Herein,we believe it is time to improve the understanding of the prevailing Ethereum-based DeFi ecosystem security issues.To that end,we investigate the Ethereum-based DeFi security issues:1)inherited from the real-world financial system,which can be solved by macro-control;2)induced by the problems of blockchain architecture,which require a better blockchain platform;3)caused by DeFi invented applications,which should be focused on during the project development.Based on that,we further discuss the current solutions and potential directions ofDeFi security.According to our research,we could provide a comprehensive vision to the research community for the improvement of Ethereum-basedDeFi ecosystem security.

Contract Mechanism of Water Environment Regulation for Small and Medium Sized Enterprises Based on Optimal Control Theory

The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.

Bipartite consensus problems of Lurie multi-agent systems over signed graphs: A contraction approach

This paper examines the bipartite consensus problems for the nonlinear multi-agent systems in Lurie dynamics form with cooperative and competitive communication between different agents. Based on the contraction theory, some new conditions for the nonlinear Lurie multi-agent systems reaching bipartite leaderless consensus and bipartite tracking consensus are presented. Compared with the traditional methods, this approach degrades the dimensions of the conditions, eliminates some restrictions of the system matrix, and extends the range of the nonlinear function. Finally, two numerical examples are provided to illustrate the efficiency of our results.

High-Level Security Dimension Evaluation of Blockchain BaaS System and Key Technology

In recent years,blockchain technology integration and application has gradually become an important driving force for new technological innovation and industrial transformation.While blockchain technology and applications are developing rapidly,the emerging security risks and obstacles have gradually become prominent.Attackers can still find security issues in blockchain systems and conduct attacks,causing increasing losses from network attacks every year.In response to the current demand for blockchain application security detection and assessment in all industries,and the insufficient coverage of existing detection technologies such as smart contract detectiontechnology,this paper proposes a blockchain core technology security assessment system model,and studies the relevant detection and assessment key technologies and systems.A security assessment scheme based on a smart contract and consensus mechanism detection scheme is designed.And the underlying blockchain architecture supports the traceability of detection results using super blockchains.Finally,the functionality and performance of the system were tested,and the test results show that the model and solutions proposed in this paper have good feasibility.

Smart Contract Vulnerability Detection Method Based on Feature Graph and Multiple Attention Mechanisms

The fast-paced development of blockchain technology is evident.Yet,the security concerns of smart contracts represent a significant challenge to the stability and dependability of the entire blockchain ecosystem.Conventional smart contract vulnerability detection primarily relies on static analysis tools,which are less efficient and accurate.Although deep learning methods have improved detection efficiency,they are unable to fully utilize the static relationships within contracts.Therefore,we have adopted the advantages of the above two methods,combining feature extraction mode of tools with deep learning techniques.Firstly,we have constructed corresponding feature extraction mode for different vulnerabilities,which are used to extract feature graphs from the source code of smart contracts.Then,the node features in feature graphs are fed into a graph convolutional neural network for training,and the edge features are processed using a method that combines attentionmechanismwith gated units.Ultimately,the revised node features and edge features are concatenated through amulti-head attentionmechanism.The result of the splicing is a global representation of the entire feature graph.Our method was tested on three types of data:Timestamp vulnerabilities,reentrancy vulnerabilities,and access control vulnerabilities,where the F1 score of our method reaches 84.63%,92.55%,and 61.36%.The results indicate that our method surpasses most others in detecting smart contract vulnerabilities.