Non-Negative Paratuck2 Tensor Decomposition Combined to LSTM Network for Smart Contracts Profiling

Background: Past few months have seen the rise of blockchain and cryptocurrencies. In this context, the Ethereum platform, an open-source blockchain-based platform using Ether cryptocurrency, has been designed to use smart contracts programs. These are self-executing blockchain contracts. Due to their high volume of transactions, analyzing their behavior is very challenging. We address this challenge in our paper. Methods: We develop for this purpose an innovative approach based on the non-negative tensor decomposition Paratuck2 combined with long short-term memory. The objective is to assess if predictive analysis can forecast smart contracts activities over time. Three statistical tests are performed on the predictive analytics, the mean absolute percentage error, the mean directional accuracy and the Jaccard distance. Results: Among dozens of GB of transactions, the Paratuck2 tensor decomposition allows asymmetric modeling of the smart contracts. Furthermore, it highlights time dependent latent groups. The latent activities are modeled by the long short term memory network for predictive analytics. The highly accurate predictions underline the accuracy of the method and show that blockchain activities are not pure randomness. Conclusion: Herein, we are able to detect the most active contracts, and predict their behavior. In the context of future regulations, our approach opens new perspective for monitoring blockchain activities

Autom Constr

Field or laboratory data collected for work-related musculoskeletal disorder (WMSD) risk assessment in construction often becomes unreliable as a large amount of data go missing due to technology-induced errors, instrument failures or sometimes at random. Missing data can adversely affect the assessment conclusions. This study proposes a method that applies Canonical Polyadic Decomposition (CPD) tensor decomposition to fuse multiple sparse risk-related datasets and fill in missing data by leveraging the correlation among multiple risk indicators within those datasets. Two knee WMSD risk-related datasets-3D knee rotation (kinematics) and electromyography (EMG) of five knee postural muscles-collected from previous studies were used for the validation and demonstration of the proposed method. The analysis results revealed that for a large portion of missing values (40%), the proposed method can generate a fused dataset that provides reliable risk assessment results highly consistent (70%-87%) with those obtained from the original experimental datasets. This signified the usefulness of the proposed method for use in WMSD risk assessment studies when data collection is affected by a significant amount of missing data, which will facilitate reliable assessment of WMSD risks among construction workers. In the future, findings of this study will be implemented to explore whether, and to what extent, the fused dataset outperforms the datasets with missing values by comparing consistencies of the risk assessment results obtained from these datasets for further investigation of the fusion performance.CC999999/ImCDC/Intramural CDC HHS/United States2021-04-23T00:00:00Z33897107PMC8064735956

Non-Negative Paratuck2 Tensor Decomposition Combined to LSTM Network For Smart Contracts Profiling

Smart contracts are programs stored and executed on a blockchain. The Ethereum platform, an open source blockchain-based platform, has been designed to use these programs offering secured protocols and transaction costs reduction. The Ethereum Virtual Machine performs smart contracts runs, where the execution of each contract is limited to the amount of gas required to execute the operations described in the code. Each gas unit must be paid using Ether, the crypto-currency of the platform. Due to smart contracts interactions evolving over time, analyzing the behavior of smart contracts is very challenging. We address this challenge in our paper. We develop for this purpose an innovative approach based on the nonnegative tensor decomposition PARATUCK2 combined with long short-term memory (LSTM) to assess if predictive analysis can forecast smart contracts interactions over time. To validate our methodology, we report results for two use cases. The main use case is related to analyzing smart contracts and allows shedding some light into the complex interactions among smart contracts. In order to show the generality of our method on other use cases, we also report its performance on video on demand recommendation

An In-Depth Investigation of the Effects of Work-Related Factors on the Development of Knee Musculoskeletal Disorders among Construction Roofers

Construction roofers have the uppermost likelihood of developing knee musculoskeletal disorders (MSDs). Roofers spend more than 75% of their total working time being restricted to awkward kneeling postures and repetitive motions in a sloped roof setting. However, the combined effect of knee-straining posture, roof slope and their association to knee MSDs among roofers are still unknown. This dissertation aimed to provide empirical evidence of the effects of two roofing work-related factors namely, roof slope and kneeling working posture, on the development of knee MSDs among construction roofers. These two factors were assessed as potential to increase knee MSD risks in roofing by evaluating the awkward knee rotations and heightened activation of knee postural muscles that might occur in sloped-shingle installation. Moreover, a novel ranking-based ergonomic risk analysis method was developed to identify the riskiest working phase in the sloped-shingle installation operation. In addition, a data fusion method was developed for treating multiple incomplete experimental risk related datasets that would affect the accuracy of risk assessments due to human and technology-induced errors during experimental data collection. The findings revealed that roof slope, working posture and their interaction have significant impacts on developing knee MSDs among roofers. Knees are likely to have increased exposure to MSD risks during placing and nailing shingles on sloped roof surfaces. The established data fusion method has been proven feasible in handling up to 40% missing data in MSD risk-related datasets. The contributions lie in enhanced understanding of the physical risk exposures of roofers\u27 knee MSDs and creation of the ranking-based ergonomic analysis method and the fusion method that will help improve the MSD risk assessment in construction. In the long run, these outcomes will help develop new knee joint biomechanical models, effective interventions, and education and training materials that will improve the workplace to promote health and safety of roofers