Dynamic Trust Decay: Adaptive Profiling Mechanism for Blockchain Oracles

Information and communication technology based inter-organizational systems enable companies to integrate information and conduct business electronically across different parts of the organization. For organizations embracing blockchain, smart contracts provide automation and operational efficiency for inter-organizational systems. Initially utilised for financial transactions, smart contract are extended beyond banking and deployed in wide number of organizations. Smart contracts are regarded as self-executing type of contract consisting of agreement’s terms embedded directly into the code which plays a vital role in operability for inter-organizational systems, however, smart contract vulnerabilities can arise due to programming errors, leading to security issues. The effects of smart contract vulnerabilities can be significant, including loss of funds, unauthorized access to sensitive information, manipulation of data, and loss of trust in the application leading to catastrophic financial losses followed by legal implications for an organization based on blockchain technology. The goal of smart contracts exploiting vulnerabilities is to discover and eliminate potential security vulnerabilities in smart contract code prior to it being deployed. Detecting vulnerabilities in a timely manner helps to prevent financial losses, unauthorized access, and data manipulation. In order to provide a robust solution to detect vulnerabilities in smart contracts, the proposed methodology presents a novel approach for rapid detection of vulnerabilities by integrating genetic algorithm with isolation forest. Furthermore, enhancing smart contract vulnerability identification with higher accuracy and false-positive rate provides a reliable gateway for organizations to adopt blockchain.

Smart contracts are executable programs that enable the building of a\nprogrammable trust mechanism between multiple entities without the need of a\ntrusted third-party. Researchers have developed several security scanners in\nthe past couple of years. However, many of these analyzers either do not scale\nwell, or if they do, produce many false positives. This issue is exacerbated\nwhen bugs are triggered only after a series of interactions with the functions\nof the contract-under-test. A depth-n vulnerability, refers to a vulnerability\nthat requires invoking a specific sequence of n functions to trigger. Depth-n\nvulnerabilities are time-consuming to detect by existing automated analyzers,\nbecause of the combinatorial explosion of sequences of functions that could be\nexecuted on smart contracts.\n In this paper, we present a technique to analyze depth-n vulnerabilities in\nan efficient and scalable way by combining symbolic execution and data\ndependency analysis. A significant advantage of combining symbolic with static\nanalysis is that it scales much better than symbolic alone and does not have\nthe problem of false positive that static analysis tools typically have. We\nhave implemented our technique in a tool called MPro, a scalable and automated\nsmart contract analyzer based on the existing symbolic analysis tool\nMythril-Classic and the static analysis tool Slither. We analyzed 100 randomly\nchosen smart contracts on MPro and our evaluation shows that MPro is about\nn-times faster than Mythril-Classic for detecting depth-n vulnerabilities,\nwhile preserving all the detection capabilities of Mythril-Classic.\n

This paper aims to explore the application of deep learning in smart contract vulnerabilities detection. Smart contracts are an essential part of blockchain technology and are crucial for developing decentralized applications. However, smart contract vulnerabilities can cause financial losses and system crashes. Static analysis tools are frequently used to detect vulnerabilities in smart contracts, but they often result in false positives and false negatives because of their high reliance on predefined rules and lack of semantic analysis capabilities. Furthermore, these predefined rules quickly become obsolete and fail to adapt or generalize to new data. In contrast, deep learning methods do not require predefined detection rules and can learn the features of vulnerabilities during the training process. In this paper, we introduce a solution called Lightning Cat which is based on deep learning techniques. We train three deep learning models for detecting vulnerabilities in smart contract: Optimized-CodeBERT, Optimized-LSTM, and Optimized-CNN. Experimental results show that, in the Lightning Cat we propose, Optimized-CodeBERT model surpasses other methods, achieving an f1-score of 93.53%. To precisely extract vulnerability features, we acquire segments of vulnerable code functions to retain critical vulnerability features. Using the CodeBERT pre-training model for data preprocessing, we could capture the syntax and semantics of the code more accurately. To demonstrate the feasibility of our proposed solution, we evaluate its performance using the SolidiFI-benchmark dataset, which consists of 9369 vulnerable contracts injected with vulnerabilities from seven different types.

The nursery and finishing phases are critical for profitability and sustainability in swine production, but effective methods for early disease detection in these stages remain underdeveloped. This study used routinely collected production indicators from nursery (42–56 days postfarrowing) and finishing lots (115–120 days postfarrowing) to create production benchmark curves for anomaly detection. These curves were developed for farms without diagnosed health challenges and compared to those with diagnosed health issues, based on tissue submissions and diagnostic codes (Dx codes). Statistical methods such as resampling techniques, Bayesian statistics, and standard deviation (SD) thresholds were employed to build the benchmark curves. The main objective was to test and compare the benchmarks using detection, early detection rate (EDR), time-to-detect (TTD), and false positive rate (FPR). Results showed that bootstrapping (BOOT), jackknife (JK), and Markov chain Monte Carlo (MCMC) methods provided the highest EDRs, although they were prone to false positives. For nursery lots, it was observed that using cumulative average with one SD for feed disappearance (EDR 49.2% and FPR 9.8%) and estimated weight (EDR 47.2% and FPR 8.8%) showed the best balance between EDR and FPR, and using MCMC for mortality showed the best balance between EDR and FPR (EDR 38.8% and FPR 13.8%). For finishing lots, using cumulative average with one SD showed a more balanced performance with FPR below 14.0% and EDR of 21.3% for feed disappearance, 67.4% weight, and 59.7% for mortality. These findings demonstrate the potential of using production indicators for early health challenge detection in swine operations.

The inception of blockchain techniques has been revolutionizing various domains, e.g., Internet of Things, supply chain and healthcare. Ethereum smart contracts emerge as the promising blockchain application, which could enable distrustful parties to participate in the automatic and trustful transactions. Given the increasing importance of Ethereum smart contracts, understanding them becomes imperative. However, prior work only studied smart contracts with general high-level patterns, and one critical question has not been answered yet: how do smart contracts behave individually? In this paper, we present a behavior-aware profiling of individual smart contract from a multi-party perspective, which improves the visibility of the smart contract ecosystem. We conduct a detailed study of the behavior of individual smart contract on two real-world datasets, and our profiling reveals interesting and surprising observations. For example, a few contract completion chains have more than 50 contracts and all of them belong to the Finance category. We also discuss the implications that lead to recommendations to improve the security and performance of the smart contract ecosystem. Overall, our work effectively complements previous work towards generating a comprehensive understanding of smart contracts.

To estimate the detection rates (DRs) and false-positive rates (FPRs) in the incidental identification of trisomy 18 (T18) and trisomy 13 (T13) as part of antenatal screening for Down's syndrome (DS) using the Combined, Quadruple and Integrated test markers. Screening marker levels on 224 T18 and 67 T13 pregnancies screened for DS were evaluated. Estimated means, standard deviations and correlation coefficients were used with published estimates for unaffected pregnancies to derive detection algorithms for the two disorders. DRs and FPRs of the algorithms were estimated using Monte Carlo simulation. In T18 and T13 pregnancies first trimester nuchal translucency was raised, free β-human chorionic gonadotrophin (hCG) and pregnancy associated plasma protein-A reduced. In T18 pregnancies second trimester alphafetoprotein, unconjugated oestriol and free β-hCG were reduced. In T13 pregnancies second trimester inhibin-A was raised. These markers specified T18 and T13 algorithms. The DS Combined test algorithm detected 42% of T18 and 59% of T13 (2.00% FPR); 88% and 74% by adding the T18 Combined test algorithm (2.17% FPR) and 89% and 75% by further adding the T13 Combined test algorithm (2.19% FPR). The corresponding detection rates for the Quadruple test were: 5% and 21% (2.00% FPR), 59% and 21% (2.16% FPR) and 59% and 24% (2.28% FPR), and for the Integrated test were: 40% and 60% (2.00% FPR), 92% and 68% (2.12% FPR) and 92% and 74% (2.18% FPR).[Corrected] Antenatal screening for DS detects about 40% of T18 and about 60% of T13 pregnancies. The addition of a T18 algorithm substantially increases the detection of both trisomies with a small increase in the FPR. The further addition of a T13 algorithm results in a small increase in the detection of T13.

In the traditional approach to a digital asset management system, the data processing mechanism is not transparent or visible to the data owners since the data is managed solely by the service provider. With the rapid development of blockchain technology, the above issues can be resolved by leveraging the tamper-resistance and decentralization characteristics of blockchain. However, post the implementation of the EU General Data Protection Rules (GDPR) in 2018, the protection of data owners has taken center stage. This has led to several principles of personal data deletion, such as Storage Limit and the Right to Be Forgotten to conflict with the blockchain. It is also observed that, out of the various smart contracts deployed to manage digital assets, often only specific smart contracts are invoked, while the rest of the deployed smart contracts are rarely invoked, leading to smart contract designs exhibiting similar patterns with very little creativity. This current scenario has motivated us to propose SPChain, a smarter and private GDPR-compliant digital asset management framework enabled by blockchain. In this approach, a decentralized InterPlanetary File System has been adopted to solve the problem of SPOF. In addition, the combination of digital assets with artificial intelligence models has been proposed so as to make digital assets accessible to a larger number of applications and to enable better creativity. In this design, artificial intelligence models have been run in independent, virtualized containers and invoked through smart contracts. The proposed SPChain can be applied to the field of digital art management to provide a complete implementation based on the Hyperledger Fabric. Using this proposed framework, model developers, digital art creators, collectors, service providers, as well as third parties can not only benefit from securely managing digital assets and combining them with AI models, but also from simultaneously complying with the rights stipulated in the GDPR. During the course of the experiments conducted, the latency, throughput, and resource consumption of different functions in the smart contracts have been measured. After adjusting the batch timeout of the block and the maximum number of transactions in a block, the throughputs were observed to be about 500 TPS, with 10 to 15 TPS for reading and writing operations, respectively. The latency ranges were found to range from 0 to 7 seconds, with 2.5 to 5 seconds for reading and writing operations, respectively.

Labor productivity is a fundamental piece of information for estimating and scheduling a construction project. The current practice of labor productivity estimation relies primarily on either published productivity data or an individual's experience. There is a lack of a systematic approach to measuring and estimating labor productivity. Although historical project data hold important predictive productivity information, the lack of a consistent productivity measurement system and the low quality of historical data may prevent a meaningful analysis of labor productivity. In response to these problems, this paper presents an approach to measuring productivity, collecting historical data, and developing productivity models using historical data. This methodology is applied to model steel drafting and fabrication productivities. First, a consistent labor productivity measurement system was defined for steel drafting and shop fabrication activities. Second, a data acquisition system was developed to collect labor productivity data from past and current projects. Finally, the collected productivity data were used to develop labor productivity models using such techniques as artificial neural network and discrete-event simulation. These productivity models were developed and validated using actual data collected from a steel fabrication company.

Java is a very promising language for use in the simulation of physical models due to its object-oriented nature, portability, robustness and support for multithreading. This paper presents JWarp, a Java library for discrete-event parallel simulations. It is based on an optimistic model for synchronization of the simulation entities: the Time Warp mechanism. We introduce the main features of the library and discuss some of the implementation details. © 1998 John Wiley & Sons, Ltd.

To determine the quantitative effect on overall screening performance (detection rate for a given false-positive rate) of using several moderately strong, independent risk factors in combination as screening markers. Theoretical statistical analysis. For the purposes of this analysis, it was assumed that all risk factors were independent, had Gaussian distributions with the same standard deviation in affected and unaffected individuals and had the same screening performance. We determined the overall screening performance associated with using an increasing number of risk factors together, with each risk factor having a detection rate of 10%, 15% or 20% for a 5% false-positive rate. The overall screening performance was estimated as the detection rate for a 5% false-positive rate. Combining the risk factors increased the screening performance, but the gain in detection at a constant false-positive rate was relatively modest and diminished with the addition of each risk factor. Combining three risk factors, each with a 15% detection rate for a 5% false-positive rate, yields a 28% detection rate. Combining five risk factors increases the detection rate to 39%. If the individual risk factors have a detection rate of 10% for a 5% false-positive rate, it would require combining about 15 such risk factors to achieve a comparable overall detection rate (41%). It is intuitively thought that combining moderately strong risk factors can substantially improve screening performance. For example, most cardiovascular risk factors that may be used in screening for ischaemic heart disease events, such as serum cholesterol and blood pressure, have a relatively modest screening performance (about 15% detection rate for a 5% false-positive rate). It would require the combination of about 15 or 20 such risk factors to achieve detection rates of about 80% for a 5% false-positive rate. This is impractical, given the risk factors so far discovered, because there are too few risk factors and their associations with disease are too weak.

A new breed of consumer-facing financial applications called decentralized financial applications (DeFi) is structured using smart contracts and runs on permissionless blockchain technologies. In this chapter, the authors contextualize the DeFi idea within the theoretical framework of blockchain and related technologies and their potential real-world applications. In addition, the authors looked at the basic DeFi concepts and application scenarios for DeFi today and pinpointed distributed ledger technologies, artificial intelligence, machine learning, and zero-knowledge proofs as the three key technologies that help revolutionize DeFi. The authors concluded that blockchain is the foundation of DeFi, and AI and ML capabilities help organizations deliver crypto insights to their users to cope with the market volatility, and zero-knowledge proofs ensure the security of digital transactions in an unregulated, decentralized finance world.

Relationships between age and visual field (VF) reliability indices were investigated using a large real-world dataset (42,421 VF data points from 11,525 eyes of 5930 subjects). All VFs tested and stored at Shimane University Hospital between 1988 and 2019 were exported. Correlations between age, mean deviation (MD), pattern standard deviation (PSD), and reliability indices including fixation losses (FLs), false negatives (FNs), and false positives (FPs) were analyzed. The mean ± standard deviation age was 65.0 ± 15.1 years; MD—−6.9 ± 8.1 decibels (dB); PSD—6.3 ± 4.6 dB; FL—8.6 ± 11.7%; FN—5.3 ± 8.3%; and FP—2.6 ± 5.0%. Univariate analyses showed strong associations between age and FNs (correlation coefficient, ρ = 0.20, p < 0.0001) and MD (ρ = −0.21, p < 0.0001). All FLs, FNs, and FPs were lowest during the third decade (20–29 years) of life. FLs were elevated consistently after that decade, and FNs were elevated sharply after the seventh decade. FPs were relatively stable after the fourth decade (30–39 years). Mixed-effect regression analyses in subjects 40 years and older showed that older age was associated with worse FLs (p < 0.0001) and FNs (p < 0.0001) but not FPs (p = 0.4126). Aging affects FLs and FNs with different modes but had minimal effects on FPs. Decreased VF sensitivity, deteriorated macular function, and technical difficulties with testing may be mechanisms of age-related changes in FLs and FNs.

Abstract: Cryptocurrencies have emerged as a significant asset class with unprecedented growth and market volatility. This paper presents an analysis of the growth patterns and market dynamics of specific cryptocurrencies, examining their historical performance, weekly top gainers and losers, and trading volumes. The study leverages comprehensive data sets and employs statistical analysis techniques to evaluate the growth trajectories of selected cryptocurrencies. By analyzing historical price data and market indicators, we assess the overall growth patterns, identify market trends, and highlight the most prominent gainers and losers within weekly intervals. This analysis provides valuable insights for investors, traders, and researchers seeking to understand the market dynamics and make informed decisions. Furthermore, the study investigates the trading volumes of cryptocurrencies, as volume plays a crucial role in assessing market liquidity and investor participation. We explore the relationship between trading volumes and price movements, examining whether higher volumes correlate with increased price volatility or stability. These insights contribute to a deeper understanding of market behavior and provide a foundation for developing trading strategies and risk management techniques. The findings of this research shed light on the performance and market dynamics of specific cryptocurrencies, enhancing our understanding of their growth patterns and investor sentiment. Analysis serves as a valuable resource for market participants, enabling them to identify potential investment opportunities, evaluate market trends, and manage risk effectively.

This paper describes a way to include the end users of the electrical network in the Demand Response mechanism using the Blockchain technology and the Smart Contract concepts, thus developing a distributed system where customers can communicate directly with the system operator to provide their flexibility in a transparent, secure and traceable way.The historical data on energy consumption and production of the customers is recorded on the Blockchain, with the Smart Contract evaluating the baseline and the potential support that each customer can provide to fulfill the requested load adaptation communicated by the grid operator. The baseline and the optimal power reduction for a customer (evaluated by the Smart Contract) are provided as input data to an Energy Management System developed by the Authors with the aim of shifting the consumption of each user in response to the Demand Response event in an automated way, satisfying the demand for reduction in the best possible way for customer and grid operator at the same time.The Energy Management System is based on historical trends and performs an economic optimization minimizing the energy supply cost for the end user. Results confirm the possibility to successfully integrate the Blockchain technology to this particular application, and the cooperation with an Energy Management System automates the process by improving the speed and effectiveness of the customer response.

Inter-discharge interval (IDI) distribution analysis of motor unit firing patterns is a valuable tool in EMG decomposition and analysis. However, the firing pattern obtained by EMG decomposition may have detection errors: false positives (incorrectly classified firings) and false negatives (missed firings). In this paper, the mathematical derivation of an IDI distribution model that accommodates false positives and false negatives of the detection process is presented. An approximation of the general model to adapt to specific EMG decomposition conditions is also presented. To illustrate the usefulness of the model, the obtained distribution is used to derive the maximum likelihood estimates of the statistics of motor unit firing patterns, the IDI mean and standard deviation, and estimates of the false negative and false positive ratios. Results obtained from simulation experiments and tests with real motor unit firing patterns show an enhanced estimation performance when compared to previously available algorithms. Goodness-of-fit tests applied to estimations for real data corrupted with false positives showed that the model-driven estimations fitted the uncorrupted data better than EFE estimations: 82% versus 52% not rejectable, respectively, when false positives were about 10% of IDIs. With about 5% false positives, the not rejectable estimations were 85% versus 70%.