Bytecode-based approach for Ethereum smart contract classification by Dan LIN, Kaixin LIN, Jiajing WU, Zibin ZHENG

“…In recent years, blockchain technology has been widely used and concerned in many fields, including finance, medical care and government affairs.However, due to the immutability of smart contracts and the particularity of the operating environment, various security issues occur frequently.On the one hand, the code security problems of contract developers when writing contracts, on the other hand, there are many high-risk smart contracts in Ethereum, and ordinary users are easily attracted by the high returns provided by high-risk contracts, but they have no way to know the risks of the contracts.However, the research on smart contract security mainly focuses on code security, and there is relatively little research on the identification of contract functions.If the smart contract function can be accurately classified, it will help people better understand the behavior of smart contracts, while ensuring the ecological security of smart contracts and reducing or recovering user losses.Existing smart contract classification methods often rely on the analysis of the source code of smart contracts, but contracts released on Ethereum only mandate the deployment of bytecode, and only a very small number of contracts publish their source code.Therefore, an Ethereum smart contract classification method based on bytecode was proposed.Collect the Ethereum smart contract bytecode and the corresponding category label, and then extract the opcode frequency characteristics and control flow graph characteristics.The characteristic importance is analyzed experimentally to obtain the appropriate graph vector dimension and optimal classification model, and finally the multi-classification task of smart contract in five categories of exchange, finance, gambling, game and high risk is experimentally verified, and the F1 score of the XGBoost classifier reaches 0.913 8.Experimental results show that the algorithm can better complete the classification task of Ethereum smart contracts, and can be applied to the prediction of smart contract categories in reality.…”
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Synthesis, Characterization, Molecular Docking, and In Silico ADME Study for Some New Different Derivatives for Succiniohydrazide by Asmaa A. Maryoosh, Oday H. R. Al-Jeilawi

“…The Swiss ADME method with boiled egg prediction was used to analyze its pharmacokinetic properties. …”
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Isokinetic Dynamometry for External and Internal Rotation Shoulder Strength in Youth Athletes: A Scoping Review by Ian Leahy, Erin Florkiewicz, Mary P. Shotwell

“…The dynamic assessment provided by ID may enhance upper extremity evaluation, aiding in the prediction of injury risk and the determination of return-to-sport criteria for overhead athletes…”
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MSRD-CNN: Multi-Scale Residual Deep CNN for General-Purpose Image Manipulation Detection by Kapil Rana, Gurinder Singh, Puneet Goyal

“…Firstly, a multi-scale residual module is employed in pre-processing stage to extract the prediction error or noise features adaptively. Afterwards, the obtained noise features are processed by feature extraction network having multiple Feature Extraction Blocks (FEBs) for the extraction of high-level image tampering features. …”
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Exhaled Nitric Oxide Is Useful in Symptomatic Radioactive Pneumonia: A Retrospective Study by Jiancheng Li, Xiaobin Fu, Jie Fu

“…The aim was to defect the exhaled nitric oxide (eNO) prediction value of symptomatic radioactive pneumonia (SRP). 64 cases of lung cancer or esophagus cancer, who had the primary radiotherapy (intensity-modulated radiation therapy), were included from 2015 June to 2016 January. …”
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A New Lebanese Medication Adherence Scale: Validation in Lebanese Hypertensive Adults by R. Bou Serhal, P. Salameh, N. Wakim, C. Issa, B. Kassem, L. Abou Jaoude, N. Saleh

“…Objectives were to validate the new adherence scale and its prediction of hypertension control, compared to MMAS-8, and to assess adherence rates and factors. …”
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A New Method for Measuring Higgs Mass by Berger Thomas, Tian Junping

“…One prominent exam-ple is for the SM prediction of the Higgs partial decay width H → WW* or H → ZZ*, in which the Higgs mass uncertainty becomes one of the leading sources of parametric theory error. …”
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GaitVision: Real-Time Extraction of Gait Parameters Using Residual Attention Network by Mohammad Farukh Hashmi, B. Kiran Kumar Ashish, Prabhu Chaitanya, Avinash Keskar, Sinan Q. Salih, Neeraj Dhanraj Bokde

“…The end layer comprises of a Softmax classifier to classify the final prediction of the subject identity. This state-of-the-art work creates a gait-based access authentication that can be used in highly secured premises. …”
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'Salsa', a Dataset for Beat Estimation in Salsa Music by Daniel Gómez-Marín, Rafael Ospina-Caicedo, Javier Díaz-Cely, Jesús Paz, Sergi Jordà, Perfecto Herrera

“…We detail the dataset, outline the methodology carried out for compiling and validating beat annotations, and finally test two contemporary beat prediction models on the dataset. Our contributions include the establishment of a labeled dataset for beat estimation research in salsa music and a robust methodology for identifying beat occurrences. …”
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Glucose Metabolism of Human Prostate Cancer Mouse Xenografts by Hossein Jadvar, Xiankui Li, Antranik Shahinian, Ryan Park, Michel Tohme, Jacek Pinski, Peter S. Conti

“…Our results support the notions that FDG PET may be useful in the imaging evaluation of response to androgen ablation therapy and in the early prediction of hormone refractoriness in men with metastatic prostate cancer.…”
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A Novel Approach for Model Interpretability and Domain Aware Fine-Tuning in AdaBoost by Raj Joseph Kiran, J. Sanil, S. Asharaf

“…Abstract The success of machine learning in real-world use cases has increased its demand in mission-critical applications such as autonomous vehicles, healthcare and medical diagnosis, aviation and flight safety, natural disaster prediction, early warning systems, etc. Adaptive Boosting (AdaBoost) is an ensemble learning method that has gained much traction in such applications. …”
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Exploring the dynamics of financing, fan influence, and leadership and governance in soccer club performance by Daniel Getnet, Zelalem Melkamu, Sisay Mengistu

“…Conclusions: Fan influence and financing of football clubs play a significant role in predicting soccer club performance, while leadership and governance also have a strong impact. …”
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Changes on Sugar and Starch Contents during Seed Development of Synergistic Sweet Corn and Implication on Seed Quality by Bhornchai Harakotr, Warisa Sutthiluk, Panumart Rithichai

“…The regression analysis revealed that seed germinability could be predicted by total starch content in synergistic sweet corn lines during seed development stages; however, this prediction seemed to be negligible in sweet corn genotypes equipped with a single-recessive gene. …”
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Impact of Rutting on Traffic Safety: A Synthesis of Research Findings by Ali Fares, Man-Nok Wong, Tarek Zayed, Nour Faris

“…It also established rutting limits and developed machine learning-based prediction models for accident rates caused by rutting under varying conditions. …”
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Plant interactions associated with a directional shift in the richness range size relationship during the Glacial-Holocene transition in the Arctic by Ying Liu, Simeon Lisovski, Jérémy Courtin, Kathleen R. Stoof-Leichsenring, Ulrike Herzschuh

“…However, the complexity of the mechanism limits its applicability for conservation or range prediction. We explore whether the relationship holds over time, and whether plant speciation, environmental heterogeneity, or plant interactions are major factors of the relationship within northeast Siberia and Alaska. …”
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Activins and Follistatin in Chronic Hepatitis C and Its Treatment with Pegylated-Interferon-α Based Therapy by Bassem Refaat, Ahmed Mohamed Ashshi, Adel Galal El-Shemi, Esam Azhar

“…The currently used markers to monitor the response to treatment are based on viral kinetics and their performance in the prediction of treatment outcome is moderate and does not combine accuracy and their values have several limitations. …”
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Experimental Study on Groutability of Sand Layer concerning Permeation Grouting by Yan-Xu Guo, Qing-Song Zhang, Lian-Zhen Zhang, Ren-Tai Liu, Xin Chen, Yan-Kai Liu

“…However, influenced by the complex properties of sand layer and slurry, an accurate prediction of the groutability of the sand layer remains to be a hard work. …”
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Cognitive Strategies and Physical Activity in Older Adults: A Discriminant Analysis by Nathalie André, Claude Ferrand, Cédric Albinet, Michel Audiffren

“…Although a number of studies have examined sociodemographic, psychosocial, and environmental determinants of the level of physical activity (PA) for older people, little attention has been paid to the predictive power of cognitive strategies for independently living older adults. …”
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A comparative analysis of Deep Neural Networks and Gradient Boosting Algorithms in long-term wind power forecasting by Ivanović Luka, Milić Saša D., Sokolović Živko, Rakić Aleksandar

“…Gradient boosting algorithms combine the advantages of a few machine learning models (decision trees, random forests, etc.) to produce a powerful prediction model. In addition to conventional recurrent neural networks, the article deals with long short-term memory and gated recurrent unit as cutting-edge models for time series analysis and predictions. …”
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Recurrent Deep Learning for Beam Pattern Synthesis in Optimized Antenna Arrays by Armando Arce, Fernando Arce, Enrique Stevens-Navarro, Ulises Pineda-Rico, Marco Cardenas-Juarez, Abel Garcia-Barrientos

“…Idealized and test beam patterns are employed as inputs for the DNN, demonstrating their effectiveness in scenarios with high prediction complexity and closely spaced elements. Additionally, a comparative analysis is conducted among the three DNN architectures. …”
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