Smart contracts (SCs), self-executing digital contracts deployed on blockchain networks, are becoming increasingly more prevalent in various sectors, such as finance, thanks to their automation, transparency, and cost efficiency. Given the substantial size of assets managed by them, SCs have become attractive targets for hackers, who exploit vulnerabilities in them to steal funds. Blockchain’s inherent immutability means vulnerabilities cannot be fixed quickly, and the immaturity of the Solidity programming language, which introduces potential security threats to SCs, exacerbates this problem. As such, there is a pressing need to develop security measures to identify vulnerabilities in SCs. Non-learning-based detection methods utilizing heuristics designed by experts often cannot handle the evolving complexity of SC vulnerabilities. In contrast, though typically outperforming non-learning-based solutions, learning-based solutions generally do not pinpoint the locations of vulnerabilities in SCs. Learning-based approaches that identify the locations of vulnerabilities come with several challenges: First, they convert SCs into graphs, incurring computational overhead and making the learning system more complex. Second, most require line- or function-level labels to be trained, which are difficult to gather. Lastly, their coverage of vulnerability types is not extensive, exposing the user to vulnerabilities not covered by them. This work presents SCsVulSegLytix, a learning-based approach for detecting and extracting vulnerable segments in SCs. SCsVulSegLytix uses a source code-based Transformer model trained with contract-level labels to classify entire contracts as vulnerable, followed by a post-hoc interpretability method to extract vulnerable segments in SCs according to relevance scores. Unlike previous extraction models, SCsVulSegLytix requires no line-level annotations and can be trained using contract-wide labels only, which are much easier to collect. Moreover, it operates directly on Solidity source code, substantially improving efficiency compared to expensive graph-based models. Finally, it extends support to several important classes of SC vulnerabilities, meaning developers are protected against various potential attacks. Experiments show that our model outperforms existing models concerning both contract- and line-level vulnerability identification while achieving greater computation efficiency.

This paper presents a plan for preparing for changes in the business environment by analyzing and predicting business district data in Seoul. The COVID-19 pandemic and economic crisis caused by inflation have led to an increase in store closures and a decrease in sales, which has had a significant impact on commercial districts. The number of stores and sales are critical factors that directly affect the business environment and can help prepare for changes. This study conducted correlation analysis to extract factors related to the commercial district’s environment in Seoul and estimated the number of stores and sales based on these factors. Using the Kendaltau correlation coefficient, the study found that existing population and working population were the most influential factors. Linear regression, tensor decomposition, Factorization Machine, and deep neural network models were used to estimate the number of stores and sales, with the deep neural network model showing the best performance in RMSE and evaluation indicators. This study also predicted the number of stores and sales of the service industry in a specific area using the population prediction results of the neural prophet model. The study’s findings can help identify commercial district information and predict the number of stores and sales based on location, industry, and influencing factors, contributing to the revitalization of commercial districts.

Abstract Human pose estimation is one of the issues that have gained many benefits from using state-of-the-art deep learning-based models. Human pose, hand and mesh estimation is a significant problem that has attracted the attention of the computer vision community for the past few decades. A wide variety of solutions have been proposed to tackle the problem. Deep Learning-based approaches have been extensively studied in recent years and used to address several computer vision problems. However, it is sometimes hard to compare these methods due to their intrinsic difference. This paper extensively summarizes the current deep learning-based 2D and 3D human pose, hand and mesh estimation methods with a single or multi-person, single or double-stage methodology-based taxonomy. The authors aim to make every step in the deep learning-based human pose, hand and mesh estimation techniques interpretable by providing readers with a readily understandable explanation. The presented taxonomy has clearly illustrated current research on deep learning-based 2D and 3D human pose, hand and mesh estimation. Moreover, it also provided dataset and evaluation metrics for both 2D and 3DHPE approaches.