SQL-Eval is an open-source PostgreSQL evaluation dataset released by Defog, constructed based on Spider. The original link can be found at https://github.com/defog-ai/sql-eval. Our evaluation methodology is more stringent, as it compares the execution accuracy of the predicted SQL queries against the sole ground truth SQL query.

Execution Management System Market Outlook

In 2023, the global market size for Execution Management Systems (EMS) was valued at approximately $3.5 billion, and it is projected to reach around $10.2 billion by 2032, growing at a robust CAGR of 12.5%. The primary growth factors driving this expansion include the increasing need for automation in business processes, the rising complexity of trading operations, and the growing demand for real-time data analytics and decision-making tools.

One of the key growth drivers for the EMS market is the escalating complexity of trading operations in financial markets. As financial instruments and trading strategies become more sophisticated, there is a burgeoning need for advanced systems that can manage and execute trades with high efficiency and accuracy. Execution Management Systems provide the necessary infrastructure to handle large volumes of trades, optimize execution, and comply with regulatory requirements, thus making them indispensable for modern trading environments.

Another significant factor contributing to the growth of the EMS market is the increasing adoption of automation and artificial intelligence (AI) in various industries. Businesses are progressively leveraging AI and machine learning algorithms to analyze vast amounts of data and make informed decisions. Execution Management Systems, equipped with advanced AI capabilities, enable organizations to automate routine tasks, thereby increasing operational efficiency and reducing the likelihood of human errors. This trend is particularly pronounced in sectors like finance, healthcare, and manufacturing, where precision and speed are critical.

The surge in the demand for real-time data analytics is also a pivotal growth factor for the EMS market. Companies are increasingly relying on real-time data to make strategic business decisions. Execution Management Systems provide a platform for the seamless integration of real-time data from various sources, offering comprehensive analytics and insights. This capability is essential for industries such as retail and telecommunications, where timely and accurate data can significantly impact customer satisfaction and operational efficiency.

In the context of the manufacturing industry, the integration of a Manufacturing Execution System (MES) is becoming increasingly vital. MES solutions provide manufacturers with real-time insights into production processes, enabling them to optimize operations, enhance product quality, and reduce production costs. By bridging the gap between enterprise resource planning (ERP) systems and the shop floor, MES facilitates seamless communication and data exchange, ensuring that production schedules are adhered to and resources are utilized efficiently. This level of integration is crucial for manufacturers aiming to remain competitive in a rapidly evolving market landscape.

Geographically, North America holds a dominant position in the EMS market, driven by the presence of major financial institutions and advanced technological infrastructure. The region is followed by Europe and Asia Pacific, where the market is growing rapidly due to increasing digitalization and the adoption of advanced trading systems. The Middle East & Africa and Latin America are also witnessing steady growth, albeit at a slower pace, as these regions continue to develop their technological capabilities and infrastructure.

Component Analysis

The EMS market is segmented into software and services based on components. The software segment holds a substantial share of the market, driven by its critical role in managing and optimizing trade execution processes. Advanced EMS software solutions offer a range of functionalities, including order routing, trade execution, performance analysis, and compliance monitoring. These capabilities are essential for financial institutions that aim to enhance trading efficiency and comply with stringent regulatory requirements. The continuous advancements in software technology, such as the integration of AI and machine learning, further propel the growth of this segment.

On the other hand, the services segment, although smaller compared to software, is gaining traction due to the growing need for professional services such as consulting, implementation, and maintenance. As businesses increasingly adopt execution management systems, they require expert guidance to ensure seamless integration with existing infrast

Appendix, implementation, benchmark programs, and experimental data for the paper currently in submission.

1. dynaboost.zip: Main DynaBoost implementation
2. bingo-ci-exp.tgz: Benchmarks + Sparrow output
3. dynaout.zip: Output for DynaBoost
4. dynaout-sample.zip: DynaBoost output for Figure 7
5. workspace.zip: Instrumented programs and testing logs

The uploaded data set was generated by the fuzzy model published in T. Galli, F. Chiclana, and F. Siewe. Genetic algorithm-based fuzzy inference system for describing execution tracing quality. Mathematics, 9(21), 2021. ISSN 2227-7390. doi: https://doi.org/10.3390/ma th9212822. URL https://www.mdpi.com/2571-5577/4/1/20.

The goal of the data generation is to make the published model available in the form of data points in a 5D space, which facilitates the construction of simpler models to approximate the original model. The names of the columns in the .csv file constitute the quality properties of execution tracing: (1) accuracy, (2) legibility, (3) implementation, and (4) security, while column (5) contains execution tracing quality derived from the fuzzy model. The indices in brackets show the column indices in the .csv file.

All variables lie in the continuous range [0, 100], where 100 means the best possible quality value and 0 the complete lack of quality or the lack of the given quality property. While generating the data, the inputs were increased by a step-size 5 and the model's output was collected, i.e. 4 inputs, from including 0 to 100 with 21 data points (21^4 = 194481).

Comparison of error values and execution time of Chicago, New York, and Lahore datasets for monthly crime prediction using BiLSTM.

Abstract: Current architectures provide many control knobs for the reduction of power consumption of applications, like reducing the number of used cores or scaling down their frequency. However, choosing the right values for these knobs in order to satisfy requirements on performance and/or power consumption is a complex task and trying all the possible combinations of these values is an unfeasible solution since it would require too much time. For this reasons, there is the need for techniques that allow an accurate estimation of the performance and power consumption of an application when a specific configuration of the control knobs values is used. Usually, this is done by executing the application with different configurations and by using these information to predict its behaviour when the values of the knobs are changed. However, since this is a time consuming process, we would like to execute the application in the fewest number of configurations possible. In this work, we consider as control knobs the number of cores used by the application and the frequency of these cores. We show that on most Parsec benchmark programs, by executing the application in 1% of the total possible configurations and by applying a multiple linear regression model we are able to achieve an average accuracy of 96% in predicting its execution time and power consumption in all the other possible knobs combinations.

This dataset includes the raw data of the experiments as well as the scripts used to plot them.

BackgroundThe use of Artificial Intelligence (AI) is exponentially rising in the healthcare sector. This change influences various domains of early identification, diagnosis, and treatment of diseases.PurposeThis study examines the integration of AI in healthcare, focusing on its transformative potential in diagnostics and treatment, and the challenges and methodologies. shaping its future development.MethodsThe review included 68 academic studies retracted from different databases (WOS, Scopus and Pubmed) from January 2020 and April 2024. After careful review and data analysis, AI methodologies, benefits and challenges, were summarized.ResultsThe number of studies showed a steady rise from 2020 to 2023. Most of them were the results of a collaborative work with international universities (92.1%). The majority (66.7%) were published in top-tier (Q1) journals and 40% were cited 2–10 times. The results have shown that AI tools such as deep learning methods and machine learning continue to significantly improve accuracy and timely execution of medical processes. Benefits were discussed from both the organizational and the patient perspective in the categories of diagnosis, treatment, consultation and health monitoring of diseases. However, some challenges may exist, despite these benefits, and are related to data integration, errors related to data processing and decision making, and patient safety.ConclusionThe article examines the present status of AI in medical applications and explores its potential future applications. The findings of this review are useful for healthcare professionals to acquire deeper knowledge on the use of medical AI from design to implementation stage. However, a thorough assessment is essential to gather more insights into whether AI benefits outweigh its risks. Additionally, ethical and privacy issues need careful consideration.

Execution time (seconds) of machine learning models.

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This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). The primary objective of this method is to create an effective data dimensionality reduction technique for eliminating redundant, irrelevant, and noisy features within high-dimensional datasets. Drawing inspiration from the Chinese idiom “Chai Lang Hu Bao,” hybrid algorithm mechanisms, and cooperative behaviors observed in natural animal populations, we amalgamate the GWO algorithm, the Lagrange interpolation method, and the GJO algorithm to propose the multi-strategy fusion GJO-GWO algorithm. In Case 1, the GJO-GWO algorithm addressed eight complex benchmark functions. In Case 2, GJO-GWO was utilized to tackle ten feature selection problems. Experimental results consistently demonstrate that under identical experimental conditions, whether solving complex benchmark functions or addressing feature selection problems, GJO-GWO exhibits smaller means, lower standard deviations, higher classification accuracy, and reduced execution times. These findings affirm the superior optimization performance, classification accuracy, and stability of the GJO-GWO algorithm.

不同强度三分球运动员眼球运动特征及其与投篮命中率的相关性研究

Mean uni-modal (train and test on listening data) and cross-modal (train on listening, test on execution) classification accuracy and p-values of the other areas.

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About the Data

Overview of Data

This data set contains execution logs of 12,000 Android applications and machine learning feature matrices constructed from them.

Paper Abstract

With Android being the most widespread mobile platform, protecting it against malicious applications is essential. Android users typically install applications from large remote repositories, which provides ample opportunities for malicious newcomers. In this paper, we evaluate a few techniques for detecting malicious Android applications on a repository level. The techniques perform automatic classification based on tracking system calls while applications are executed in a sandbox environment. We implemented the techniques in the maline tool, and performed extensive empirical evaluation on a suite of around 12,000 applications. The evaluation considers the size and type of inputs used in analyses. We show that simple and relatively small inputs result in an overall detection accuracy of 93% with a 5% benign application classification error, while results are improved to a 96% detection accuracy with up-sampling. This indicates that system-call based techniques are viable to be used in practice. Finally, we show that even simplistic feature choices are effective, suggesting that more heavyweight approaches should be thoroughly (re)evaluated.

Average classification accuracy of different algorithms on datasets.

This repository contains the data and code necessary to derive the main results of the paper “An optical neural network using less than 1 photon per multiplication”. It includes the datasets generated during the characterization of dot-product accuracy (Figure 2) and the execution of the ONN using different photon budgets (Figure 3), along with the code used to analyze them. Besides, the codes for training the neural network model and collecting data in the experimental setup are also included.

The scripts and data in "Figure 2.zip" ("Figure 3.zip") were used to generate Figure 2 (Figure 3) in the main manuscript.

"ONN-device-control.zip" contains the device control codes in the experiments for data collection, available at https://github.com/mcmahon-lab/ONN-device-control.

"ONN-QAT-SQL.zip" contains the trained model and training scripts (Python 3.8.3, PyTorch version 1.7.0, torchvision version 0.8.1) for the neural network executed in the experiment, available at https://github.com/mcmahon-lab/ONN-QAT-SQL.

For more details, please refer to the README files in the root directory and each separate folder.

Comparison of Chicago district-wise prediction error values for a month and a week using statistical and deep learning methods.

AI enabled benefits and challenges in the healthcare setting.

Mean accuracy (%) and back-transformed mean RT data (ms) with 95% confidence intervals (CI) for the secondary task by domain, dual task load, age group and strategy.

This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). The primary objective of this method is to create an effective data dimensionality reduction technique for eliminating redundant, irrelevant, and noisy features within high-dimensional datasets. Drawing inspiration from the Chinese idiom “Chai Lang Hu Bao,” hybrid algorithm mechanisms, and cooperative behaviors observed in natural animal populations, we amalgamate the GWO algorithm, the Lagrange interpolation method, and the GJO algorithm to propose the multi-strategy fusion GJO-GWO algorithm. In Case 1, the GJO-GWO algorithm addressed eight complex benchmark functions. In Case 2, GJO-GWO was utilized to tackle ten feature selection problems. Experimental results consistently demonstrate that under identical experimental conditions, whether solving complex benchmark functions or addressing feature selection problems, GJO-GWO exhibits smaller means, lower standard deviations, higher classification accuracy, and reduced execution times. These findings affirm the superior optimization performance, classification accuracy, and stability of the GJO-GWO algorithm.