Using Machine Learning to Improve Public Reporting on U.S. Government Contracts

2021 ◽  
Author(s):  
William A. Muir ◽  
Daniel Reich
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Historical Data ◽  
Superior Performance ◽  
Government Contracts ◽  
The Public ◽  
Goods And Services ◽  
Software Applications ◽  
Government Purchases ◽  
The Hierarchical Structure

The U.S. government procures more than $500 billion annually in goods and services on public contracts, which it classifies using a hierarchical product and service taxonomy. Classification serves several purposes, including transparency in the use of taxpayer funding; reporting, tracing, and segmenting government expenditures; budgeting; and forecasting. Government acquisition personnel have historically performed these classifications manually, resulting in a process that is time-consuming and error-prone and offers limited visibility into government purchases. The problem faced is not unique to the public sector and is common across retail, manufacturing, and healthcare, among other settings. Using almost 4 million historical data records on governmental purchases, we fit a series of classifiers and demonstrate (a) superior performance when explicitly modeling the hierarchical structure of information domains through the use of top-down strategies and (b) the effectiveness of character-level convolutional neural networks when textual inputs are terse and contain irregularities such as abnormal character combinations and misspellings, which are common in government contracts. Our machine learning models are embedded in multiple software applications, including a web application that we developed, used by federal government personnel and other contracting professionals.

scholarly journals Data-Driven Intelligent Application for Youtube Video Popularity Analysis using Machine Learning and Statistics

2021 ◽  
Author(s):  
Wenxi Gao ◽  
Ishmael Rico ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Data Processing ◽  
Web Application ◽  
Data Driven ◽  
Accuracy Evaluation ◽  
Video Content ◽  
The Public ◽  
Left Behind ◽  
The World ◽  
Web App

People now prefer to follow trends. Since the time is moving, people can only keep themselves from being left behind if they keep up with the pace of time. There are a lot of websites for people to explore the world, but websites for those who show the public something new are uncommon. This paper proposes an web application to help YouTuber with recommending trending video content because they sometimes have trouble in thinking of the video topic. Our method to solve the problem is basically in four steps: YouTube scraping, data processing, prediction by SVM and the webpage. Users input their thoughts on our web app and computer will scrap the trending page of YouTube and process the data to do prediction. We did some experiments by using different data, and got the accuracy evaluation of our method. The results show that our method is feasible so people can use it to get their own recommendation.

scholarly journals A Real-Time Machine Learning Based Public Transport Bus-Passenger Information System

2021 ◽  
Author(s):  
Menzi Skhosana ◽  
Absalom Ezugwu
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Public Transport ◽  
Public Transportation ◽  
Web Application ◽  
Predictive Analytics ◽  
Current Status ◽  
Transport Sector ◽  
The Public ◽  
Cost Efficient

The era of Big Data and the Internet of Things is upon us, and it is time for developing countries to take advantage of and pragmatically apply these ideas to solve real-world problems. Many problems faced daily by the public transportation sector can be resolved or mitigated through the collection of appropriate data and application of predictive analytics. In this body of work, we are primarily focused on problems affecting public transport buses. These include the unavailability of real-time information to commuters about the current status of a given bus or travel route; and the inability of bus operators to efficiently assign available buses to routes for a given day based on expected demand for a particular route. A cloud-based system was developed to address the aforementioned. This system is composed of two subsystems, namely a mobile application for commuters to provide the current location and availability of a given bus and other related information, which can also be used by drivers so that the bus can be tracked in real-time and collect ridership information throughout the day, and a web application that serves as a dashboard for bus operators to gain insights from the collected ridership data. These were integrated with a machine learning model trained on collected ridership data to predict the daily ridership for a given route. Our novel system provides a holistic solution to problems in the public transport sector, as it is highly scalable, cost-efficient and takes full advantage of the currently available technologies in comparison with other previous work in this topic.

scholarly journals Estimating the True Size of Public Procurement to Assess Sustainability Impact

2021 ◽  
Vol 13 (3) ◽  
pp. 1448
Author(s):  
Fatima Hafsa ◽  
Nicole Darnall ◽  
Stuart Bretschneider
Keyword(s):  
The Netherlands ◽  
Market Power ◽  
Public Procurement ◽  
Oecd Countries ◽  
Current Measurement ◽  
The Public ◽  
Goods And Services ◽  
Government Purchases ◽  
The U.S

Public procurement, the government’s purchase of goods and services, is an important tool to advance sustainability objectives. Since government is the largest consumer in the economy, it can have a sizable impact on the market by purchasing sustainably. However, its sustainability impact (both environmental and social) is undermined because the public procurement’s size is underestimated. Previous estimates of public procurement only consider contract-based purchases or non-defense purchases. In other instances, data are too limited to estimate government purchases appropriately. These factors lead to underestimations of the extent to which government purchasing can be leveraged to advance sustainability objectives. To understand the true impact of government purchases, we estimated the size of public procurement by considering all aspects of public procurement. We used this estimation to assess whether current measurement processes misrepresent the size of public procurement and identify key elements that may be missing from the current public procurement measures. We applied our estimate to four OECD countries, the U.S., the U.K., Italy, and the Netherlands for two years (2017 and 2018). Our results showed that that across all levels of government, public procurement as a percentage of GDP in the U.S., the U.K., Italy, and the Netherlands ranged between 19–24%, 13–56%, 3–10%, and 12–38%, respectively. Our findings revealed that governments have substantially greater market power than previously estimated, which can be leveraged to pursue sustainability goals. Our findings also illustrate systemic data challenges to how public procurement data are collected and analyzed.

Government contracts and public law

Legal Studies ◽  
1990 ◽  
Vol 10 (3) ◽  
pp. 231-244 ◽  
Author(s):  
Sue Arrowsmith
Keyword(s):  
Public Law ◽  
Government Contracts ◽  
Public Authorities ◽  
Employment Contracts ◽  
The Public ◽  
Goods And Services ◽  
Contractual Arrangements ◽  
Transport Services ◽  
The Government ◽  
Public Transport Services

In carrying out their functions government bodies frequently enter into contractual arrangements, both with private persons and with other public authorities. Like private individuals, for example, they make leases, employment contracts, and contracts of procurement to obtain the goods and services they require. Frequently they make contracts with the public in the course ofproviding public services and amenities – for example, in running public transport services, or in providing facilities such as recreation centres or museums to the public on payment of a fee. In addition, the government uses contract as a method ofcontrolling behaviour as an alternative to enacting regulations. It may, for instance, control the behaviour of those granted licences to trade or carry on other activities through contractually stipulated conditions

scholarly journals An Integrative Decision Support Model for Smart Agriculture Based on Internet of Things and Machine Learning

2021 ◽  
Vol 2 (2) ◽  
pp. 1-19
Author(s):  
Sadaf Saqib ◽  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Historical Data ◽  
Home Automation ◽  
Decision Support Model ◽  
Software Applications ◽  
Information Monitoring ◽  
Prediction Module ◽  
Smart Agriculture ◽  
The Internet Of Things

The Internet of Things (IoT) has achieved an upset in a considerable lot of the circles of our current lives, like automobile, medical services offices, home automation, retail, education, manufacturing, and many more. The Agriculture and Farming ventures significantly affect the acquaintance of the IoT with the world. Machine learning (ML) is a part of artificial intelligence (AI) that permits software applications to turn out to be more precise at foreseeing results without being expressly customized to do as such. It uses historical data as input to predict new result values. In the event, a specific industry has sufficient recorded information to help the machine "learn", AI or ML can create outstanding outcomes. Farming is likewise one such important industry profiting and advancing from machine learning at large. ML can possibly add to the total lifecycle of farming, at all phases. This incorporates computer vision, automated irrigation, and harvesting, predicting the soil, weather, temperature, moisture values, and robots for picking off the crude harvest. In this paper, I'll work on a smart agricultural information monitoring framework that gathers the necessary information from the IoT sensors set in the field, measures it, and drives it, from where it streams to store in the cloud space. The information is then shipped off the prediction module where the necessary analysis is done using ML algorithms and afterward sent to the UI for its corresponding application.

On Mechanisms of Realization of the Public Procurement of Construction Works (The Case of Road Const ruction)

2003 ◽  
pp. 68-80
Author(s):  
A. Dementiev ◽  
A. Zolotareva ◽  
A. Reus
Keyword(s):  
Public Procurement ◽  
Road Construction ◽  
International Experience ◽  
The Public ◽  
Government Purchases ◽  
Efficiency And Effectiveness ◽  
Construction Works ◽  
Budget Expenditure

The most important measures stimulating the increase of efficiency and effectiveness of budget expenditures on road construction are the improvement of pricing mechanisms and increasing efficiency of the procedures of government purchases of goods, works and services. The paper includes the analysis of main problems that arise in the process of government purchases and construction pricing with the reference to budget expenditure on road construction. It includes the review and analysis of international experience and possible measures of increasing the efficiency and effectiveness of government purchases and (road) construction pricing in Russia.

A Database for Counterfeit Electronics and Automatic Defect Detection Based on Image Processing and Machine Learning

2016 ◽  
Author(s):  
Navid Asadizanjani ◽  
Sachin Gattigowda ◽  
Mark Tehranipoor ◽  
Domenic Forte ◽  
Nathan Dunn
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Integrated Circuits ◽  
Web Application ◽  
The United States ◽  
Global Market ◽  
Online Database ◽  
Ongoing Effort ◽  
Time Period ◽  
Physical Defects

Abstract Counterfeiting is an increasing concern for businesses and governments as greater numbers of counterfeit integrated circuits (IC) infiltrate the global market. There is an ongoing effort in experimental and national labs inside the United States to detect and prevent such counterfeits in the most efficient time period. However, there is still a missing piece to automatically detect and properly keep record of detected counterfeit ICs. Here, we introduce a web application database that allows users to share previous examples of counterfeits through an online database and to obtain statistics regarding the prevalence of known defects. We also investigate automated techniques based on image processing and machine learning to detect different physical defects and to determine whether or not an IC is counterfeit.

scholarly journals Enabling Knowledge Sharing within e-Government Back-Office through Ontological Engineering

2007 ◽  
Vol 2 (1) ◽  
pp. 33-48
Author(s):  
Graciela Brusa ◽  
María Laura Caliusco ◽  
Omar Chiotti
Keyword(s):  
Organizational Innovation ◽  
Santa Fe ◽  
Ontological Engineering ◽  
The Public ◽  
Software Applications ◽  
Process Characteristics ◽  
Main Requirement ◽  
The Government ◽  
Back Office

Nowadays, organizational innovation constitutes the government challenges for providing better and more efficient services to citizens, enterprises or other public offices. E–government seems to be an excellent opportunity to work on this way. The applications that support front-end services delivered to users have to access information systems of multiple government areas. This is a significant problem for e-government back-office since multiple platforms and technologies coexist. Moreover, in the back-office there is a great volume of data that is implicit in the software applications that support administration activities. In this context, the main requirement is to make available the data managed in the back-office for the e-government users in a fast and precise way, without misunderstanding. To this aim, it is necessary to provide an infrastructure that make explicit the knowledge stored in different government areas and deliver this knowledge to the users. This paper presents an approach on how ontological engineering techniques can be applied to solving the problems of content discovery, aggregation, and sharing in the e-government back-office. This approach is constituted by a specific process to develop an ontology in the public sector and an ontology-based architecture. In order to present the process characteristics, a case study applied to a local government domain is analyzed. This domain is the budget and financial information of Santa Fe Province (Argentine).

Prediction of aircraft estimated time of arrival using machine learning methods

2021 ◽  
pp. 1-15
Author(s):  
O. Basturk ◽  
C. Cetek
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Time Of Arrival ◽  
Learning Models ◽  
Trajectory Data ◽  
Different Sources ◽  
Machine Learning Models

ABSTRACT In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.

scholarly journals Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies

2021 ◽  
Vol 11 (15) ◽  
pp. 6787
Author(s):  
Jože M. Rožanec ◽  
Blaž Kažič ◽  
Maja Škrjanc ◽  
Blaž Fortuna ◽  
Dunja Mladenić
Keyword(s):  
Machine Learning ◽  
Automotive Industry ◽  
Demand Management ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Forecast Errors ◽  
Manufacturing Companies ◽  
Case Scenario ◽  
Crucial Component

Demand forecasting is a crucial component of demand management, directly impacting manufacturing companies’ planning, revenues, and actors through the supply chain. We evaluate 21 baseline, statistical, and machine learning algorithms to forecast smooth and erratic demand on a real-world use case scenario. The products’ data were obtained from a European original equipment manufacturer targeting the global automotive industry market. Our research shows that global machine learning models achieve superior performance than local models. We show that forecast errors from global models can be constrained by pooling product data based on the past demand magnitude. We also propose a set of metrics and criteria for a comprehensive understanding of demand forecasting models’ performance.

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