Penelitian ini menganalisis data historis cryptocurrency menggunakan metode Exploratory Data Analysis (EDA) untuk membantu investor pemula memahami pergerakan harga mata uang kripto. Cryptocurrency, sebagai uang digital yang tidak berwujud fisik, memiliki tingkat volatilitas tinggi yang sering menyebabkan kerugian bagi investor yang kurang berpengalaman dalam menganalisis data historis. Menggunakan dataset dari CoinMarketCap yang terdiri dari 679.183 baris dan 13 kolom periode 2017-2022, penelitian ini menerapkan metodologi EDA dengan pendekatan visualisasi data. Prosedur penelitian mencakup analisis masalah, data acquisition melalui web scraping, data cleaning, dan exploratory data analysis. Hasil analisis menunjukkan persaingan antara Bitcoin dan Ethereum. Berdasarkan marketval, Bitcoin mencapai $140.000.000.000, sementara Ethereum $80.000.000.000. Volume Bitcoin mencapai $8.000.000.000, sedangkan Ethereum $4.000.000.000. Analisis price movement menunjukkan Ethereum mencapai $140.000, sementara Bitcoin $1. Dalam analisis moving average, Ethereum menunjukkan performa lebih baik dengan grafik mencapai $105.000, dibandingkan Bitcoin yang hanya mencapai $0,8. Penelitian ini berkontribusi dalam membantu investor pemula memahami dinamika pasar cryptocurrency melalui analisis data historis. Hasil visualisasi dan analisis dapat digunakan sebagai acuan pengambilan keputusan investasi dan meminimalisir risiko kerugian. Studi ini merekomendasikan penggunaan data historis sebagai alat prediksi dibandingkan pengambilan keputusan berbasis intuisi dalam investasi cryptocurrency.
Social media has become an indispensable part of modern life, promoting information dissemination, interpersonal communication, brand promotion, and profoundly influencing people’s social habits and ways of obtaining information. In the rapidly developing technology field, social network platforms have gradually evolved into an important part of the social structure. On the other hand, with the rise and expansion of-blockchain technology from decentralized and distributed ledger technology to many fields, its inherent advantages have also begun to be valued. This study deeply analyzes the current situation and problems encountered in the integration process of social network and block chain and puts forward corresponding solutions. Through detailed case investigation, this paper shows the specific obstacles of the integration link and discusses the potential solutions. The research aims to promote the perfect integration of social media and blockchain technology and aims to provide valuable references for these two rapidly growing areas to support their future development.
As Industry 5.0 emerges, the convergence of advanced technologies like the Internet of Things (IoT) and blockchain is vital in shaping the future of industrial automation. Industry 5.0 emphasizes the collaborative relationship between humans and machines, requiring robust, decentralized systems to ensure security, accountability, and trust in interconnected ecosystems. Currently, IoT data processing is cloud-centric, which introduces challenges like fragmented data silos, limiting the potential for seamless and secure real-time analytics. Blockchain technology offers a solution by providing a decentralized and transparent ledger that can enhance data integrity and security across IoT applications. This study investigates the integration of blockchain with the IoT in the context of Industry 5.0, highlighting the potential for improved data management, security, and human-machine collaboration. By conducting a comprehensive analysis of IoT application designs and blockchain platforms, we evaluate existing literature to uncover the challenges, benefits, and limitations of this integration. Our research contributes by proposing a framework for selecting optimal blockchain platforms for IoT applications in Industry 5.0, providing actionable recommendations for enhanced data trust and resilience. Future research directions are also outlined to address the evolving demands of this technological convergence, ensuring that IoT ecosystems are secure, scalable, and human-centered in the era of Industry 5.0.
В. В. Мартиненко, Тетяна Коляда, Maryna Skoryk, Olga Sokolova · 6 authors
The article examines the impact of the financial autonomy of local governments on the implementation of the Sustainable Development Goals (SDGs) in Ukraine. The main objective of the study is to analyze the financial support of local budgets and their role in achieving key national and global development goals, such as poverty reduction, quality education, healthcare, reduction of inequality, etc. The paper also assesses how effectively decentralization contributes to increasing the financial autonomy of communities and how this affects their ability to independently allocate resources to address pressing socio-economic issues.The main results show that in the period 2015–2023, local budgets of Ukraine demonstrated a steady trend towards the growth of their own revenues, which had a positive impact on the financial autonomy of communities. The share of own revenues in total local budget revenues increased from 41% in 2015 to 68% in 2023. In addition, the expenditure coverage ratio of own revenues has also increased to over 68%, indicating that communities are increasingly able to finance their own needs. However, several problems have been identified, including an imperfect legislative framework, low institutional capacity, and a lack of specialists in local financial management.The findings of the study emphasize the importance of financial decentralization for the sustainable development of communities, especially in the context of martial law. Increased financial autonomy allows communities to respond more effectively to the challenges of the modern world, in particular by investing in local infrastructure, education, healthcare and the environment. At the same time, the authors emphasize the need to improve governance mechanisms further at the local level, as well as the importance of ensuring transparency and accountability of local governments. These measures are key to achieving the SDGs and improving the quality of life of the population.
The Shakti series of 100M, 250M, and 500M models offers compact, resource-efficient language models designed for edge AI deployment. Unlike large models like GPT-3 and LLaMA that demand cloud-based infrastructure, Shakti models operate seamlessly on low-resource devices, including smartphones, smart TVs, IoT systems, drones, and low-end GPUs. They ensure minimal energy consumption, privacy-preserving computation, and real-time performance without internet dependency. Optimized for efficiency, Shakti models come in quantized versions (int8, int5, int4) for even faster, lighter execution on edge devices. The 2.5B Shakti model has demonstrated strong performance while maintaining low latency, paving the way for the smaller, highly efficient 100M, 250M, and 500M models. Built on Responsible AI principles, Shakti prioritizes fairness, transparency, and trust while mitigating risks such as bias, privacy concerns, and high carbon footprints. These models are ideal for sensitive domains like finance, healthcare, and legal services, providing cost-effective, sustainable, and scalable AI solutions with on-device data security. Each model is tailored for specific applications. Shakti-100M excels in text generation, summarization, and chatbots for IoT and mobile apps. Shakti-250M specializes in domain-specific tasks such as contract analysis and personalized financial or healthcare advice. Shakti-500M, a versatile model, enhances customer support, content creation, and virtual assistants with multilingual capabilities and long-context understanding. By decentralizing AI, the Shakti series democratizes access to intelligent, ethical, and impactful AI solutions across industries.
Cryptocurrency is the most innovative financial and technological breakthrough of this generation. Investment in cryptocurrency grew from USD 11.18 billion in December 2016 to USD 2.147 trillion in April 2024; however, the rationality of investor exuberance is uncertain. This paper explores stakeholders' perceptions of cryptocurrency using a machine-learning approach based on artificial intelligence (AI). In particular, we employ a lexicon-based emotion-detection sentiment analysis to investigate stakeholder perceptions, using 2.3 million open-source data points. We divide the findings into positive, neutral, and negative stakeholder perception pillars based on factors such as trustworthiness in cryptocurrency, motives, cryptocurrency awareness and knowledge, ownership, socioeconomic characteristics of users, and usage. Our analysis reveals that 51 percent of the stakeholders have a positive perception of cryptocurrency, whereas 40 percent have a neutral perception and 9 percent a negative perception. After identifying the perceptions, we investigate the relationship between cryptocurrency prices and stakeholder perceptions using the autoregressive distributed lag (ARDL) framework with time-series data from August 2017 to July 2023. The long- and short-term results confirm that positive and negative perceptions have statistically significant effects on cryptocurrency prices. Individual investors comprise the largest share of those with a positive perception, as 54 percent have a positive view of cryptocurrency. Institutional investors, however, have the largest share of those with a neutral perception because of the lack of a well-established regulatory framework for cryptocurrency. However, 39 percent of institutional investors hold a positive perception is growing, a sign of a growing trend, as they are among the major investor groups with an interest in investing in crypto. Other stakeholders, such as the government, academia, and other miscellaneous groups, have a negative perception. Our results demonstrate that cryptocurrency has affected social change, social inclusion, and sustainability. Moreover, our findings offer social insights about crypto stakeholders’ perceptions about the design of strategies to promote cryptocurrency and the establishment of a sustainable crypto ecosystem. • The study investigates the stakeholders' perception towards cryptocurrency. • AI-based sentiment analysis identifies that 51% of stakeholders have a positive perception towards cryptocurrency. • The Autoregressive Distributed Lag model integrated with the UECM model was used to investigate the cointegration between cryptocurrency and stakeholder perception. • A positive perception of cryptocurrency has a significant positive effect on its price.
Dong Liu, Juan S. Giraldo, Peter Pálenský, Pedro P. Vergara
Model-free power flow calculation, driven by the rise of smart meter (SM) data and the lack of network topology, often relies on artificial intelligence neural networks (ANNs). However, training ANNs require vast amounts of SM data, posing privacy risks for households in distribution networks. To ensure customers' privacy during the SM data gathering and online sharing, we introduce a privacy preserving PF calculation framework, composed of two local strategies: a local randomisation strategy (LRS) and a local zero-knowledge proof (ZKP)-based data collection strategy. First, the LRS is used to achieve irreversible transformation and robust privacy protection for active and reactive power data, thereby ensuring that personal data remains confidential. Subsequently, the ZKP-based data collecting strategy is adopted to securely gather the training dataset for the ANN, enabling SMs to interact with the distribution system operator without revealing the actual voltage magnitude. Moreover, to mitigate the accuracy loss induced by the seasonal variations in load profiles, an incremental learning strategy is incorporated into the online application. The results across three datasets with varying measurement errors demonstrate that the proposed framework efficiently collects one month of SM data within one hour. Furthermore, it robustly maintains mean errors of 0.005 p.u. and 0.014 p.u. under multiple measurement errors and seasonal variations in load profiles, respectively.
Smart contracts are programs that permanently store and automatically execute on the blockchain system such as Ethereum. Due to the non-tamperable nature of the underlying blockchain, smart contracts are difficult to update once deployed, which requires redeploying the contracts and migrating the data. It means that the observation of smart contract evolution in the real world makes more sense. Hence, in this paper, we conducted the first large-scale empirical study to characterize the evolution of smart contracts in Ethereum. For evolution identification, we presented a contract similarity-based search algorithm, digEvolution, and evaluated its effectiveness with five different search strategies. Then we applied this algorithm to 80,152 on-chain contracts we collected from Ethereum, to dig out the evolution among these contracts. We then explored three research questions. We first studied whether the evolution of smart contracts is common (RQ1), then we studied how do the Gas consumption (RQ2) and the vulnerability (RQ3) of smart contracts vary during the evolution. Our research results show that the evolution of smart contracts is not very common. There are some contract components that have vulnerability but still be called by users. The Gas consumption of most smart contracts doesn’t vary during the evolution, contract is Gas-efficient before and after the evolution. The vulnerability of most smart contracts doesn’t vary during the evolution, both are secure before and after the evolution.
Shusheng Ding, Xiangling Wu, Tianxiang Cui, John W. Goodell · 5 authors
Climate change is a highly controversial topic within the socioeconomic context. Climate Policy Uncertainty (CPU) arises from the process of climate policies formulation and implementation. This uncertainty impacts financial market volatilities, including cryptocurrency markets . In this paper, we demonstrate the substantial role of CPU in forecasting volatilities in cryptocurrency markets using Genetic Programming (GP). Our study shows that different cryptocurrency markets respond differently to CPU across time scales. Our paper contributes to the literature by illustrating the impact of CPU on cryptocurrency market volatilities and analyzes it across different time horizons. Second, we build three volatility forecasting models for different cryptocurrency markets by incorporating CPU, which outperform traditional models. Our models can thereby illuminate portfolio construction and hedging strategies, providing valuable insights for investors and policymakers.
Large language models (LLMs) have transformed code generation. However, most existing approaches focus on mainstream languages such as Python and Java, neglecting the Solidity language, the predominant programming language for Ethereum smart contracts. Due to the lack of adequate benchmarks for Solidity, LLMs' ability to generate secure, cost-effective smart contracts remains unexplored. To fill this gap, we construct SolEval, the first repository-level benchmark designed for Solidity smart contract generation, to evaluate the performance of LLMs on Solidity. SolEval consists of 1,507 samples from 28 different repositories, covering 6 popular domains, providing LLMs with a comprehensive evaluation benchmark. Unlike the existing Solidity benchmark, SolEval not only includes complex function calls but also reflects the real-world complexity of the Ethereum ecosystem by incorporating Gas@k and Vul@k. We evaluate 16 LLMs on SolEval, and our results show that the best-performing LLM achieves only 26.29% Pass@10, highlighting substantial room for improvement in Solidity code generation by LLMs. Additionally, we conduct supervised fine-tuning (SFT) on Qwen-7B using SolEval, resulting in a significant performance improvement, with Pass@5 increasing from 16.67% to 58.33%, demonstrating the effectiveness of fine-tuning LLMs on our benchmark. We release our data and code at https://github.com/pzy2000/SolEval.
In the context of escalating climate change and mounting environmental challenges, green finance has emerged as a crucial mechanism for fostering sustainable development. This paper presents an experimental analysis that illustrates how the integration of blockchain technology into financial technology (fintech) strategies can significantly enhance the efficacy of green investments. Our proposed framework facilitates the optimization of these strategies by improving transparency and fund traceability in environmentally focused projects. Through rigorous testing and data-driven insights, we demonstrate the potential of blockchain to streamline financing processes, mitigate risks associated with fraudulent practices, and promote accountability among stakeholders. By establishing a synergistic relationship between fintech and ecological responsibility, this research provides a novel approach that contributes to both academic discourse and practical applications in green finance. The proposed approach showcases experimental originality by integrating blockchain technology with green finance, setting a precedent for future research in this interdisciplinary field. Our findings reveal that blockchain can significantly enhance the efficiency of financing processes, reducing transactional delays and fostering transparency that mitigates risks related to fraud. Moreover, this study highlights the potential of this synergistic model to cultivate a robust framework for accountability among stakeholders, ultimately guiding investment toward environmentally sustainable initiatives and bolstering the integrity of green financial practices.
Blockchain technology, characterized by its decentralized, immutable, and transparent nature, opens up transformative opportunities for enhancing accounting systems. Traditional accounting practices typically depend on centralized databases, which can be vulnerable to data manipulation, fraud, and security breaches. This paper proposes an innovative accounting system built on the Ethereum public blockchain, designed to effectively manage critical functions such as inventory management, customer interactions, sales transactions, and invoicing. By leveraging smart contracts, this system automates key processes—including invoicing and payments—resulting in reduced manual intervention and fewer human errors while improving operational efficiency. The decentralized framework of blockchain guarantees that every transaction is permanently recorded in an immutable ledger, offering unparalleled security and transparency. Additionally, this system reduces reliance on intermediaries, streamlining business operations and decreasing costs. However, challenges such as scalability and fluctuating transaction (gas) fees still persist within the Ethereum ecosystem. As Ethereum continues to evolve with the introduction of Layer 2 scaling solutions and Ethereum 2.0, these issues are anticipated to be resolved, positioning blockchain as a revolutionary solution for the accounting sector.
The research paper will focus on the influence of major cryptocurrencies, especially Bitcoin and Ethereum, on world financial markets and traditional financial systems. It looks at how, because of their decentralized nature, these digital assets have brought new dynamics to financial markets in the price of other assets, their volatility, and their means of investment. The research design is of a mixed-methods nature, combining quantitative data from financial market indices with qualitative insights from expert interviews. Some of the main lessons learnt are declining value with other financial assets, the interdependency between movements in crypto assets and other linked assets and disruptions in banking, payments and investment. Besides, there are regulation decisions that should consider the fluctuations of the market and security requirements, as well as the analysis of many initiatives in order to provide sufficient regulation frameworks on the international level. The concluding advice proposed how not only to accommodate the disturbance of current financial stability through innovations but also to integrate the utilization of cryptocurrencies.
The article examines promising directions for improving access to justice in criminal cassation proceedings using modern digital technologies. It is stated that digitalization in this area should expand the possibilities of realization of the right to judicial protection and not create additional procedural barriers or restrictions on ac-cess to justice. The author proposes a system of main directions for digital transformation of cassation pro-ceedings, including creating a mechanism for digital interaction between participants, forming a procedure for remote implementation of procedural rights through personalized electronic access, implementing distributed ledger technology, and developing electronic identification procedures for participants. The necessity of main-taining procedural guarantees while implementing digital innovations is substantiated. Specific amendments to criminal procedure legislation are proposed to implement these directions.
Research on Bitcoin (BTC) transactions is a matter of interest for both economic and network science fields. Although this cryptocurrency is based on a decentralized system, making transaction details freely accessible, making raw blockchain data analyzable is not straightforward due to the Bitcoin protocol specificity and data richness. To address the need for an accessible dataset, we present ORBITAAL, the first comprehensive dataset based on temporal graph formalism. The dataset covers all Bitcoin transactions from January 2009 to January 2021. ORBITAAL provides temporal graph representations of entity-entity transaction networks, snapshots, and stream graph. Each transaction value is given in Bitcoin and US dollar regarding daily-based conversion rate. This dataset also provides details on entities such as their global BTC balance and associated public addresses.
Regina Cibelle de Oliveira, Edson Mello Lucas, Gustavo Barbosa Libotte
Smart Contracts are autonomous, self-executable programs that facilitate agreement execution without the need for intermediaries. These contracts are also susceptible to software defects, leading to vulnerabilities that can be exploited by attackers. The use of models for predicting software defects is a well-studied research area. However, applying these models with Smart Contract metrics is an area that remains underexplored. The aim of this study is to evaluate whether deep learning models used in the prediction of traditional software defects produce equivalent results with specific Smart Contract metrics. Machine learning models were applied to four data sets, and performances were evaluated using Precision, Recall, F-score, Area under the curve (AUC), Precision-recall curve (PRC), and Matthews Correlation Coefficient (MCC). This approach complements traditional formal verification methods, which, although accurate, are often slower and less adaptable to emerging vulnerabilities. By employing deep learning, the model enables faster and more cost-effective analysis of large volumes of Smart Contracts. Unlike conventional techniques that rely on expert-defined rules and require substantial computational resources, this model offers scalable and continuous monitoring. Consequently, the research provides a complementary solution that can significantly enhance the security of the smart contract ecosystem, allowing for the detection of potential defects before exploitation occurs.
Tarihin başlangıcından itibaren sürekli evrim geçiren para, insanlık tarafından geliştirilen en önemli araçlardan biridir. Para, insanların gelecekteki ve anlık ihtiyaçlarını karşılamak için belirlenen bir değeri temsil eder. Para kavramı, dönemin koşullarına ve imkânlarına göre farklı şekillerde ortaya çıkar. Kripto paraların temelleri 1980'lerde atılmış olup, 2008 yılında Satoshi Nakamoto tarafından Bitcoin'in tanıtılmasıyla hayatımıza girmiştir. Geleneksel paralara alternatif olarak ortaya çıkan kripto paralar, teknolojik bir yenilik olup her geçen gün daha da popüler hale gelmektedir. Bitcoin, merkezi bir otorite tarafından yönetilmeyen ilk kripto paradır ve popülerliği ve başarısı diğer alternatif kripto paraların oluşmasına yol açmıştır. Julong Deng tarafından 1982 yılında geliştirilen “Gri Sistem Teorisi”, belirsiz sistemlerin davranışlarını tahmin etmek için kullanılan bir yöntem olup GM (1,1) modeli en sıklıkla kullanılan gri modeldir. Bu çalışma, Bitcoin'in fiyatlarını GM (1,1) modeli kullanarak tahmin etmeyi amaçlamaktadır. Araştırma sonucunda, modelin gelecek dönem tahminleri için uygun olduğu ve başarılı tahminler yaptığı belirlenmiştir
In the context of growing threats in the digital space, cyber security has established itself as a critical component for protecting information systems. With the increasing reliance on digital technology and the Internet, the risks of cyber-attacks and unauthorized access to sensitive data have greatly increased. This highlights the need to develop advanced security mechanisms that ensure the overall safety of systems. Traditional security methods often fail to cope with the complexity of modern threats. Organizations are therefore exploring new technologies, such as blockchain and artificial intelligence, that offer higher levels of security. Blockchain provides innovative solutions that decentralize identity management and reduce dependence on centralized servers. This paper aims to present a methodology combining two blockchain authentication methods – MetaMask Authentication and Smart Contract-Based Authentication, for implementation and analysis, ensuring overcoming challenges in real applications.
Cryptocurrency, a decentralized digital currency, has brought about significant social impacts. This essay explores its effects on crime rates, global economic integration, and the GPU market while also delving into the underlying technologies of blockchain and cryptography. The introduction introduces the concept of cryptocurrency as a decentralized, pseudo-anonymous digital currency. The historical journey and the impacts of cryptocurrency are discussed in section 2, focusing on its social implications. The technological underpinnings are further discussed in section 3. The paper concludes by addressing the core technologies: cryptography and blockchain. Cryptography ensures security and anonymity, with SHA-256 as a fundamental algorithm. Blockchain, a decentralized database, interlinks transaction blocks, making tampering difficult due to Proof-of-Work validation. In essence, cryptocurrency’s social impacts and technological foundations intersect, offering insights into its intricate landscape.
This study explores the feasibility of Bitcoin as a legal currency and a store of value in comparison to traditional fiat currencies. Through a comprehensive literature review and discussion, the study examines Bitcoin’s core characteristics such as circulation limitations, scarcity, price stability, intrinsic value, and associated security risks. The analysis highlights key challenges, including Bitcoin’s limited acceptance in global commerce, high volatility, and the potential risks posed by its decentralized nature. While Bitcoin’s scarcity and technological innovation position it as a unique asset, its viability as a mainstream currency remains uncertain due to its lack of regulatory support and price stability. The paper concludes that although Bitcoin holds promise as a digital asset, it faces significant obstacles in replacing fiat currencies as a stable medium of exchange or a reliable store of value. Recommendations are provided for governments and institutions on regulatory approaches and the integration of cryptocurrencies into the existing financial system.
The cryptocurrency wallet security has been among the hot issues concerning its adoption. It hinders most users and government from adopting crypto-market. There are several security issues reported by the study such as scams, hacks, and theft in digital coins particularly cryptocurrency. This study has deployed literature review techniques to bring awareness on the frequently used vulnerabilities and to suggest the way to mitigate and prevent such vulnerabilities. Numerous methodologies suggested by prior-literatures has been used to mitigate, detect, and prevent any security threats towards crypto-wallet. The study has found that the cryptocurrency wallet is subjected to several attacks which are born by the general internet protocols loopholes, those attacks born by the presence of security vulnerabilities awareness, and those attacks resulted from the nature of blockchain systems. Several users have been victimized by the scammers, hackers, and fake investment schemes due to lack of knowledge base concerning security vulnerabilities. This study recommends the usage of several security policies to combat those vulnerabilities which include usage of Insurance policies, Security scans policies, and deployment of AI systems to mitigate any security vulnerabilities.
The rapid growth of decentralized finance (DeFi) has revolutionized the global financial landscape, providing decentralized alternatives to traditional financial services. This study investigates the asymmetric multifractal behavior of nine DeFi markets—AAVE, Pancake Swap (CAKE), Compound (COMP), Curve Finance (CRV), Maker DAO (MKR), Synthetix (SNX), Sushi Swap (SUSHI), UniSwap (UNis), and Yearn Finance (YFI)—using Asymmetrical Multifractal Detrended Fluctuation Analysis (A-MFDA). The use of generalized Hurst exponents, Rényi exponents, and singularity spectrum functions revealed that DeFi markets exhibit multifractal behaviors. The analysis uncovered clear differences between uptrend and downtrend fluctuation functions, highlighting asymmetric multifractal behavior. The asymmetry intensity was analyzed through excess differences in uptrend and downtrend generalized Hurst exponents. AAVE, COMP, SNX, UNis, SUSHI, and MKR exhibit negative asymmetry, with stronger correlations during negative trends. CAKE shifts from positive to negative asymmetry, showing sensitivity to both trends. CRV is more volatile in negative trends, while YFI consistently displays positive asymmetry across market fluctuations. The results also reveal that long-term correlations and heavy-tailed distributions contribute to the multifractality of DeFi assets. This study highlights the need for dynamic risk management in DeFi markets, urging investors to adopt adaptive strategies for volatile assets and prepare for sudden price fluctuations to safeguard investments.
The non-fungible tokens (NFTs) market has evolved over the past decade, with NFTs serving as unique digital iden-tifiers on a blockchain that certify ownership and authenticity. The trading attributes of NFTs have drawn many users and investors. However, their high value also attracts attackers who exploit vulnerabilities in NFT smart contracts for illegal profits, thereby harming the NFT ecosystem. One notable vulnerability in NFT smart contracts is sleep minting, which allows attackers to illegally transfer others' tokens. Although some research has been conducted on sleepminting, these studies are basically qualitative analyses or based on historical transaction data. There is a lack of understanding from the contract code perspective, which is crucial for identifying such issues and preventing attacks before they occur. To address this gap, in this paper, we categorize the sleep-minting issue and find four distinct types of sleepminting in NFT smart contracts. Each type is accompanied by a comprehensive definition and illustrative code examples to provide a clear understanding of how these vulnerabilities manifest within the contract code. Furthermore, to help detect the defined defects before the sleepminting problem occurrence, we propose a tool named WakeMint, which is built on a symbolic execution framework. WakeMint is designed to be compatible with both high and low versions of Solidity, ensuring broad applicability across various smart contracts. The tool also employs a pruning strategy to shorten the detection period. Additionally, WakeMint gathers some key information, such as the owner of an NFT and emissions of events related to the transfer of the NFT's ownership during symbolic execution. Then, it analyzes the features of the transfer function based on this information so that it can judge the existence of sleepminting. We ran WakeMint on 11,161 real-world NFT smart contracts and evaluated the results. We found 115 instances of sleep minting issues in total, and the precision of our tool is 87.8 %.