SOLICITATION.pdf

SOLICITATION/CONTRACT/ORDER FOR COMMERCIAL ITEMS

OFFEROR TO COMPLETE BLOCKS 12, 17, 23, 24, & 30

1. REQUISITION NUMBER PAGE

NFFM7210-19-02915

1OF 36

2. CONTRACT NO. 3. AWARD/EFFECTIVE 4. ORDER NUMBER 5. SOLICITATION NUMBER 6. SOLICITATION ISSUE

DATE 1305M219QNFFM0254 DATE

a. NAME b. TELEPHONE NUMBER (No collect 8. OFFER DUE DATE/

7. FOR SOLICITATION LINDA MULLEN calls) LOCAL TIME

INFORMATION CALL: linda.mullen@noaa.gov (757) 441-3436 AUG 15, 2019

04:00 PM ET

9. ISSUED BY CODE AJF10014 10. THIS ACQUISITION IS UNRESTRICTED OR SET ASIDE: 100 % FOR:

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TION UNLESS BLOCK IS

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14. METHOD OF SOLICITATION

RFQ IFB RFP

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FISHERIES SAMPLING BR /F/NEC21

NOAA/NMFS 166 WATER STREET

WOODS HOLE MA 02543-1026

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Attachment Page

DEPARTMENT OF COMMERCE (DOC)

NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION (NOAA)

NATIONAL MARINE FISHERIES SERVICE (NMFS)

NORTHEAST FISHERIES SCIENCE CENTER (NEFSC)

Capitalizing on a groundfish image library to test automated image classification in the northeast region

STATEMENT OF WORK

C.1.0 GENERAL

The Department of Commerce (DOC), National Oceanic and Atmospheric Administration

(NOAA), National Marine Fisheries Service (NMFS), Northeast Fisheries Science Center

(NEFSC) is seeking a contractor to develop software solutions that more closely align the video review process currently occurring as part of electronic monitoring (hereafter EM) programs with machine learning based video analysis. Specifically, we seek a contractor to develop software and documentation in an open manner that both allows for the application of machine learning algorithms to video footage and allows for the further development of annotated training data sets. The goal of this work is to produce tools that can be shared with companies working to process the review footage. The Contractor personnel will be working under the guidance of

NMFS staff, and with project partners in the northeast region.

The base period will be 14 months and will include project scoping, algorithm development, the production of software, and the creation of accompanying documentation. The project is expected to begin in Fall 2019 with meetings to coordinate the planning and specification of technical requirements. This will entail the development of a project plan as well as a project timeline and governance structure. The Contractor will be responsible for developing algorithm based tools for detecting catch items, estimating the length of these items, and performing species identification via machine learning that are suitable for processing video data from fisheries in the northeast region (e.g., the Multispecies Groundfish Fishery).

The Contractor will be expected to leverage existing image libraries for machine learning algorithm and model development. Additionally the contractor will be encouraged to build upon existing open source software tools. The development of software solutions that both implements machine learning models and allow for the generation of additional training data will primarily be the responsibility of the Contractor; however, NMFS will assist and provide feedback on development at regular intervals. All software, models, and data (including annotations) that are generated by the project will be the property of the Government. The

Contractor shall work with NMFS staff to develop software solutions that can be of use to the broader scientific community.

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This development aligns with NMFS’s longer term goal of implementing machine learning in fisheries monitoring to aid in the development of resources with which to enhance the efficiency and accuracy of automated species identification. The information gained through this study will help ultimately enhance EM in New England and Mid-Atlantic by providing the means to make

EM video review more efficient and thereby a more viable option for collecting fisheries data.

C.2.0 BACKGROUND

C.2.1 Overview

DOC/NOAA/NMFS is the Government agency responsible for monitoring and managing Federal fisheries. Within the NEFSC, the Fishery Monitoring and Research Division’s (FMRD) Fisheries

Sampling Branch (FSB) is responsible for monitoring and observing fishing in the Northeast and mid-Atlantic U.S. The FSB collects, processes, manages, and provides fishery dependent data and biological samples obtained from fishing vessels. Collection of these data is mandated or otherwise needed for management under the Magnuson-Stevens Fishery Conservation and

Management Act, the Marine Mammal Protection Act, the Atlantic Tuna Convention Act, and the

Endangered Species Act. The branch staff and observers under contract perform at-sea operations aboard commercial vessels to collect information on fishing operations, fishing effort, catch, including bycatch and discard information, economic data and vessel efficiency, and biological samples of landed catch and discard. The Greater Atlantic Regional Fisheries Office

(GARFO) receives and reviews EM summary data to conduct data analysis and comparisons with other data sources where applicable.

EM is being considered as a tool for catch monitoring and reporting compliance in fisheries around the world. Nationally there are several EM initiatives and programs, but implementation in the northeast has been limited to date. As part of the Greater Atlantic Region’s Electronic

Technology (ET) Implementation Plan, the Councils and NMFS are considering implementation of EM in the Atlantic herring and northeast multispecies groundfish fisheries to improve catch monitoring and accountability. This project builds off of the recommendations and action items from the following completed projects and current initiatives:

● National EM Workshop, January 2014, GARFO and the NEFSC, in collaboration with

The Nature Conservancy (TNC), hosted a two (2) day workshop in May 2014 for the purpose of developing a shared strategy of implementing EM.

● The NEFSC completed the final phase of its New England Electronic Monitoring Project in 2013 and published the final report in August 2014. This project yielded initial technical specifications and onboard configurations for EM systems, catch handling protocols, methods for weight estimation, and other best practices for a groundfish EM program.

● TNC, the Gulf of Maine Research Institute, the Maine Coast Community Sector, and

Ecotrust Canada, have collaborated with GARFO and NEFSC, to operationalize an EM program in the groundfish fishery. The final project goal is to develop standards, process, and analysis to support the use of EM in lieu of At-Sea Monitors (ASMs) in the

New England multispecies fishery and implement an operational program in 2018.

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● NEFSC and GARFO have partnered in 2016, 2017, 2018, and 2019 to test the applicability of EM to provide specific monitoring services in the Atlantic herring fishery.

● In 2018 and 2019 NEFSC expanded EM to a true audit model program (100% video and partial review).

This project seeks to leverage existing image data sets (see Attachment 1) and open source EM video review software with the goal of producing an integrated software solution that can be used to advance catch accounting methods. This project is consistent with the objectives of the

New England Fisheries Management Council to improve monitoring and to address bycatch in the multispecies groundfish fishery.

A summary of how EM works in the groundfish fishery follows: A typical EM system includes three to four cameras as well as other sensors to capture fishing activity (i.e., GPS or hydraulic pressure). Generally, video is collected from the time a trip starts to the time the vessel returns to the dock. During each trip captains and crew follow highly specified catch handling, and are only allowed to discard fish from specific locations (control points). Camera views and catch handling is laid out in documents called vessel monitoring plans that are approved by NMFS for each vessel to ensure that a reviewer is able to estimate portions of catch that are most relevant to management and monitoring.

All of the data from cameras and sensors are recorded on hard drives that are delivered to an

EM service provider. The EM service providers use software to annotate footage and create EM data summaries the outline the fishing activity relevant to managers (Attachment 2). Video review is conducted in two parts by the current provider. First, an initial review where reviewers inspect the video and sensor data for issues that prevent review (camera failures, malfunctioning sensors, etc.). A more thorough review of the video is then conducted where the bycatch is estimated from individual fish (lateral images on a marked board) and non-compliant fish handling behavior is noted. These annotations are turned into a summarized JSON file and submitted to the GARFO for in season catch accounting (Attachment 3).

Currently EM is used as a monitoring option on ~25 vessels in New England and these vessels use a range of gear types (e.g., trawl, handline, longline) but all have specified handling for catch estimation (see above). The number of trips taken by these vessels has recently increased to ~400 trips per fishing year. Trips generally last one to three days. Currently EM review is conducted for all of these trips and video review is time consuming. Typically, each review takes between 2 and 10 hours. The time and resources required to annotate video footage is not efficient or effective and currently limit scaling up to fleet-wide participation (which would be as many as 200 vessels and reviewing footage from longer multi-day trips).

Speeding up video review with some automation (such as machine learning based video processing) is essential for moving electronic monitoring forward. Automating key aspects of quality control and catch estimation (i.e., detection, identification, and length estimation) with modern machine learning techniques is the goal we are working towards. Additionally, incorporating modern tools in EM review software that improve video review, but also allow for

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the continued development of image data sets suitable for furthering machine learning model training. Specifically, because more data is generally better for training machine learning algorithms, leveraging the existing human review to build annotated training data sets would be an ideal use of EM review software. Implementing these features in an open source software package is the key to taking EM to the next level in New England and Nationally

C.2.2 Examples of open source software related to EM

There are a growing number of open source software options in the EM sphere. These include software that is designed for recording and reviewing of EM video and sensor data. Perhaps the most notable example comes from the combined efforts of Chordata LLC, Saltwater Inc, and

Sea State Inc (see: https://pt.chrdta.com/em/). Open EM is another similar EM review software that is open source (see: https://github.com/openem-team/openem). These tool sets include software solutions that allow for the full lifecycle of EM data collection (from shipboard data acquisition to a review user interface). Additionally, open source software for image and video annotation is also being produced. These are generally distinct software packages that aim to generate data sets on single video streams (e.g., https://github.com/cvisionai/Tator, https://github.com/opencv/cvat). This project aims to merge the functionalities into these types of software to applying machine learning to EM in a straightforward manner.

The ideal product of this work would be software that could ‘plug-in’ with existing EM review software. iBecause of NMFS’s interest in having video reviewed by 3rd parties this would be a product that could interface with the current software used by EM service providers. These providers generally stream video and allow for review of these data via web portals.

C.2.3 Machine Learning and Image Analysis in the Northeast todate

There are a growing number of groups interested in applying a machine learning approach to

EM nationally. Much of the innovation in this realm is being driven by collaborations between

NOAA staff and researchers from the University of Washington. In New England there have been a couple of projects that have tried this using data collected as part of regional EM development. These include, but are not limited to: a recent Kaggle competition organized by

TNC1; second Kaggle competition aimed at identifying individual right whales organized by the

NEFSC2. Additional projects underway utilize data currently being collected as a part of EM pre-implementation monitoring (collaborations between TNC, UMass Dartmouth's School for Marine

Science & Technology (SMAST), and a private company CVision Consulting).

In addition to these ongoing projects, a large library of images to train machine learning models was identified as a regional priority and NOAA strategic initiative. Because existing data sets were seen as not sufficient to train models to distinguish the species found in the region, a project was funded to do just this using the NMFS bottom trawl survey. The NMFS bottom trawl

1 Please see the Kaggle winnners: http://drivendata.co/blog/fish-winners/ and general competition description: https://www.drivendata.org/competitions/48/identify-fish-challenge/ 2 Please see this right whale competition: https://www.kaggle.com/c/noaa-right-whale-recognition http://drivendata.co/blog/fish-winners/ http://www.drivendata.org/competitions/48/identify-fish-challenge/ http://www.drivendata.org/competitions/48/identify-fish-challenge/ http://www.kaggle.com/c/noaa-right-whale-recognition http://www.kaggle.com/c/noaa-right-whale-recognition

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survey provides perhaps the best venue to develop a large data set of images for model training if images and data can be collected without impacting existing sampling practices by pairing images of specimens processed by the regular operations of the NMFS research vessel with the data currently collected by the FSCS. This work capitalized on the large number of specimens processed by the bottom trawl survey as well as the large number of annotations currently entered into FSCS. These large quantities of sample images are an essential part of training machine learning algorithms, and thus are an essential ingredient in developing the ability to automatically classify images. The image collection for this project (see Attachment 1) has recently finished. Therefore, it is expected that the funded proposal will utilize this new resource to bring machine learning to EM review.

C.3.0 OBJECTIVES

NEFSC is proposing to develop algorithms and software solutions to help ease the burden of

EM video review. The machine learning components are expected to utilize the existing image data sets and if feasible work with existing open source EM review software (e.g., https://pt.chrdta.com/em/). These software solutions produced by this work are expected to be open source. Through this project (as well as other ongoing regional projects), NMFS intends to eventually meet the following strategic goals:

● Enhance the efficiency and accuracy of fisheries data collection

● Reduce the reporting burden on fishermen

● Reduce the overall cost of fisheries monitoring

● Expedite video review

This software development project aims to develop a novel software and algorithm solutions for

EM. The goal is to leverage existing data sets as much as possible for algorithm development, while also highlighting future image training set needs. An additional focus is to utilize open source software to bring together EM video review and machine learning data set annotation.

To achieve these objectives, NMFS has identified the following Contractor specific project deliverables:

● Through an iterative process develop a plan for training machine learning algorithms.

● Through an iterative process develop a plan for implementing machine learning algorithms in EM review software.

● Through an iterative process develop a plan for implementing training data collection via

EM review software.

● Develop machine learning algorithms that help to automate EM review following the plan developed with NMFS.

● Develop a means of integrating machine learning algorithms in open source EM software.

● Develop a means of using EM review efforts to develop novel training data sets for further training of machine learning algorithms.

● Document products produced in a final report that allows for further development.

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C.4.0 SERVICE REQUIREMENTS

C.4.1 Project Phases

C.4.1.1 Project Setup: a period of up to three months (depending on the date of the contract award) involving consultation and development of algorithms and software plan requirements described in Sections 4.2 (Project Initiation). Additional tasks related to establishing project roles and and pre-emptive work needed to complete sections 4.3 (Algorithm Development), 4.4

(Bringing Machine Learning to EM Review), 4.5 (Using EM Review to Develop Machine

Learning Training Data), and 4.6 (Final Report and Other Documentation).

C.4.1.2 A ten (10) month algorithm and software development period (initiated approximately three (3) months post contract award) involving the development of algorithms in Sections 4.3

(Algorithm Development), 4.4 (Bringing Machine Learning to EM Review), and 4.5 (Using EM

Review to Develop Machine Learning Training Data).

C.4.1.3 Analysis and reporting: one (1) month period (initiated twelve (13) months post contract award) involving the service requirements described in Section 4.6 (Final Report and Other

Documentation).

C.4.2 Project Initiation

C.4.2.1 The Contractor shall participate in regular contact with appropriate NMFS staff during algorithm and software planning phases. Frequent and consistent communication with NMFS staff will help build a good working relationship, reinforcement commitment (NMFS staff and

Contractor), and will serve to address any outstanding questions or concerns either party may have. The Contractor and NMFS will also use these opportunities to describe project goals, evaluate the progress towards project objectives since the last meeting, and provide feedback on the state of development. Contact will be conducted in this manner to ensure that clear software requirements are developed and to ensure that the ultimate goal of this project are clear to the Contractor. The Contractor will track all contact and document the purpose of each event in a project progress tracking sheet which will be accessible to NMFS.

C.4.2.2 The Contractor shall also participate in occasional coordination meetings with NMFS staff and other stakeholders throughout the course of the contract. Meetings will be led, organized, and scheduled by NMFS. Outreach may include software demos and detailed discussions about algorithm development.

C.4.2.3 The Contractor shall engage in regular communication (every week or more frequently as required) with NMFS staff (in person or by conference call) to review development objectives, software requirements, development plans, project deliverables, project spending and budget plans (quarterly), and staffing/resources.

C.4.2.4 The Contractor shall lead the planning phases of algorithm and software development

(with the assistance of NMFS). Specifically, the Contractor shall perform an iterative process of

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coordination with NMFS staff to ensure that the algorithms and software being developed will have the greatest utility feasible given the existing constraints (data, funding, and time available). Algorithms must be developed and implemented such that they provide data comparable to that currently recorded by humans doing EM review. The Contractor shall provide a detailed description of their algorithm and software development plan and its components in their quote and as part of their reference sheet. The Contractor must also include a detailed description of how the existing image data sets and existing open source software will be capitalized upon. Additionally, the Contractor shall lay layout the general plan for software development that specifies key attributes of the software including where the software will run (in the cloud or locally) and how the software will interface with existing open source EM review software. Finally, the Contractor should explicitly outline how the accuracy of algorithms will be assessed by users in applications where they are applied.

C.4.3 Algorithm Development

The Contractor shall develop algorithms using industry standard open source tools to meet the needs of EM video review. These algorithms will meet the goals identified through consultation with NMFS during the project initiation (4.2). Examples of tasks that may be required include

(but are not limited to): 1) species identification for groundfish from New England’s waters, 2) the estimation of length of an individual catch items passed over a measuring board, and 3) the tracking of individual fish so as to develop piece counts of the fish processed on a vessel. The

Contractor shall follow the best open source software development protocols including modern version control. The algorithms will be made such that they can be updated as simply as possible as new data becomes available. Utilization of modern machine learning techniques is essential for this task.

C.4.4 Bringing Machine Learning to EM Review

Following the development of machine learning algorithms, the Contractor shall being developing mechanisms to import these algorithms into EM review software. This process will follow the plan developed in coordination with NMFS. The Contractor shall work to the greatest extent practicable to make a tool that can be widely used. This is includes a software framework that allows access at multiple levels and that is impeccably documented. To the extent that it is practical this software should utilize existing open source software resources.

The Contractor shall create and maintain a detailed plan for progressing through the stages of development for this software. This includes developing the required project management framework and clearly establishing team roles. This information shall be made available to

NMFS on request NMFS. As soon as possible the Contractor shall provide NMFS access to software to allow for feedback and experts in EM review to provide feedback. Clear documentation of these interactions and software request shall be maintained even if feature requests fall outside the scope of this project. The Contractor shall provide instruction on the use of the software product to NMFS. This includes instructions for basic software installation and use. Also, detailed descriptions of software features. If time allows the Contractor will assist

NMFS in performing pilot trials of the software on EM footage.

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C.4.5 Using EM Review to Develop Machine Learning Training Data

The Contractor will work to leverage existing image or video annotation tools to create a novel software tool that allows for the development of annotated training data during the course of regular EM review. This software may be stand alone or integrated with other tools developed in this project. The key feature being a means to annotate and store images associated with annotations in a format that can be used for further algorithm development. The interface should mirror those currently existing, and provide the greatest flexibility possible. Exported meta-data should align with industry standards for machine learning.

The Contractor shall create and maintain a detailed plan for progressing through the stages of software development. This includes developing the required project management framework and clearly establishing team roles. This information shall be made available to NMFS. The

Contractor shall provide NMFS access to software to allow for feedback and experts in EM review to provide feedback. Clear documentation of these interactions and software request shall be maintained even if feature requests fall outside the scope of this project. The Contractor shall provide instruction on the use of the software product to NMFS. This includes instructions for basic software installation and use. Also, detailed descriptions of software features. Finally, the Contractor shall assist NMFS in performing pilot trials of the software on EM footage such that both software and model performance can be evaluated.

C.4.6 Final report and Other Documentation

The Contractor shall provide NOAA Fisheries with a draft and the final report on this project in a format to be approved by NMFS. The draft report is due one (1) month prior to the final report.

The draft shall allow NOAA Fisheries to provide comments and edit text. The final report, incorporating changes suggested by NOAA Fisheries, shall be clear, straightforward and will incorporate an overview of the algorithm developed, the software produced, lessons learned and next steps in using automated image processing to improve the efficiency of fisheries monitoring.

C.4.8 Ownership of materials

All, reports, summaries, information collected or tools developed during this project and submitted to NMFS as required, and developed products will be the property of NOAA Fisheries and will be maintained onsite by the NMFS. All data will be delivered in an accessible manner to

NMFS. The data collected throughout this project may be used for outreach purposes, trainings

(video auditors, NMFS staff, observers, etc.), and presentations as needed. The Contractor will be required to store all (specimen and image) data for three (3) years from the project start date.

C.5.0 SCHEDULE OF DELIVERABLES

Following is a schedule of all deliverables, including administrative deliverables, required during the period of performance of this contract:

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Item Description Quantity Due Date Deliver

To:

Reference

1 Planning document for algorithm and software development

1 units Up to 3 months after award

COR Section C, C.4.2

2 Project meetings with NMFS

As needed

Potentially every week or more frequently if required

COR Section C, C.4.2

3 Schedule for algorithm and software delivery of final data summary

1 Up to 3 months after award

COR Section C, C.4.2

4 Spending and project budget reports

1 Quarterly over the course of the contract

COR Section C, C.4.2

5 Machine learning algorithms

1 Up to 13 months after award

COR Section C, C.4.3

6 Software for using algorithms to review footage

1 Up to 13 months after award

COR Section C, C.4.4

7 Inventory of all code libraries, third party software tools, algorithms, models, scripts, etc that are required to run along with license type and

1 At project completion COR Section C, C.4.4

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versions (like “Google

TensorFlow, v2.1, GPL”) should be included.

8 Software for developing training data via EM review

1 Up to 13 months after award

COR Section C, C.4.5

9 Software and algorithm documentation and algorithm/software evaluation

1 By project conclusion COR Section C, C.4.6

10 Software demos and other collaborative meetings

As needed

As required COR Section C, C.4.5, C.4.6

11 Detailed software requirements for future developers

1 By project conclusion COR Section C, C.4.6

12 Draft Report 1 1 month prior to the due date for final report

COR Section C, C.4.7

13 Final Report 1 At project completion COR Section C, C.4.7

14 Delivery of all raw software and final algorithm products

1 2 months following completion of project

COR Section C, C.4.8

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ITEM NO. SUPPLIES/SERVICES QUANTITY UNIT UNIT PRICE AMOUNT

Offerors shall submit a quote that contains, at a minimum, the following information:

1. The Request for Quote (RFQ) number

(1305M219QNFFM0245);

2. The Name, Address, Telephone Number, and

DUNS

Number of the Vendor;

3. Include a statement specifying the extent of agreement with all terms, conditions, and provisions included in the RFQ. Quotes that reject the terms and condition s of the RFQ may be excluded from consideration;

4. Acknowledgment of Solicitation Amendments

(SF-30),

if any, and;

5. Provide a technical description of the items being offered in sufficient detail to evaluate compliance with the requirements in the solicitation. This may include product literature, or other documents, if necessary;

6. Address technical capability in meeting the requirement per the Statement of Work;

8. Vendor shall be registered in the System for Award

Management (SAM) (www.sam.gov) in order to be considered for award.

DUNS NO.:

COMPLETION DATE: 14 months ARO

Funds to support contract work to develop algorithms from a fish image library in support of Electronic

Monitoring (EM) development in the Northeast (FIS/NOP funds) in accordance with the attached Statement of

Work.

1.00

LS

SCHEDULE Continued

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Table of Contents

CLAUSES

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CLAUSES

CLAUSES

CONTRACT CLAUSES

The following FAR clauses are provided in full text:

FAR 52.212-5 CONTRACT TERMS AND CONDITIONS REQUIRED TO IMPLEMENT STATUTES OR

EXECUTIVE ORDERS-COMMERCIAL ITEMS (MAY 2019)

(a) The Contractor shall comply with the following Federal Acquisition Regulation (FAR) clauses, which are incorporated in this contract by reference, to implement provisions of law or Executive orders applicable to acquisitions of commercial items:

(1) 52.203-19, Prohibition on Requiring Certain Internal Confidentiality Agreements or Statements (Jan 2017) (section 743 of Division E, Title VII, of the Consolidated and Further Continuing Appropriations Act, 2015 (Pub. L. 113-235) and its successor provisions in subsequent appropriations acts (and as extended in continuing resolutions)).

(2) 52.204-23, Prohibition on Contracting for Hardware, Software, and Services Developed or Provided by Kaspersky Lab and Other Covered Entities (Jul 2018) (Section 1634 of Pub. L. 115-91).

(3) 52.209-10, Prohibition on Contracting with Inverted Domestic Corporations (Nov 2015).

(4) 52.233-3, Protest After Award (Aug 1996) (31 U.S.C. 3553).

(5) 52.233-4, Applicable Law for Breach of Contract Claim (Oct 2004) (Public Laws 108-77 and 108-78

(19 U.S.C. 3805 note)).

(b) The Contractor shall comply with the FAR clauses in this paragraph (b) that the Contracting Officer has indicated as being incorporated in this contract by reference to implement provisions of law or Executive orders applicable to acquisitions of commercial items:

[Contracting Officer check as appropriate.]

(1) 52.203-6, Restrictions on Subcontractor Sales to the Government (Sept 2006), with Alternate I

(Oct 1995) (41 U.S.C. 4704 and 10 U.S.C. 2402).

(2) 52.203-13, Contractor Code of Business Ethics and Conduct (Oct 2015) (41 U.S.C. 3509)).

(3) 52.203-15, Whistleblower Protections under the American Recovery and Reinvestment Act of 2009

(June 2010) (Section 1553 of Pub. L. 111-5). (Applies to contracts funded by the American Recovery and Reinvestment Act of 2009.)

_X_ (4) 52.204-10, Reporting Executive Compensation and First-Tier Subcontract Awards (Oct 2018) (Pub. L. 109-282) (31 U.S.C. 6101 note).

(5) [Reserved].

(6) 52.204-14, Service Contract Reporting Requirements (Oct 2016) (Pub. L. 111-117, section 743 of

Div. C).

(7) 52.204-15, Service Contract Reporting Requirements for Indefinite-Delivery Contracts (Oct 2016)

(Pub. L. 111-117, section 743 of Div. C).

_X_ (8) 52.209-6, Protecting the Government’s Interest When Subcontracting with Contractors Debarred, Suspended, or Proposed for Debarment. (Oct 2015) (31 U.S.C. 6101 note).

(9) 52.209-9, Updates of Publicly Available Information Regarding Responsibility Matters (Oct 2018)

(41 U.S.C. 2313).

(10) [Reserved].

(11)(i) 52.219-3, Notice of HUBZone Set-Aside or Sole-Source Award (Nov 2011) (15 U.S.C.657a).

(ii) Alternate I (Nov 2011) of 52.219-3.

(12)(i) 52.219-4, Notice of Price Evaluation Preference for HUBZone Small Business Concerns

(Oct 2014) (if the offeror elects to waive the preference, it shall so indicate in its offer) (15 U.S.C. 657a).

(ii) Alternate I (Jan 2011) of 52.219-4.

(13) [Reserved] _X_ (14)(i) 52.219-6, Notice of Total Small Business Set-Aside (Nov 2011) (15 U.S.C.644).

(ii) Alternate I (Nov 2011).

(iii) Alternate II (Nov 2011).

(15)(i) 52.219-7, Notice of Partial Small Business Set-Aside (June 2003) (15 U.S.C. 644).

(ii) Alternate I (Oct 1995) of 52.219-7.

(iii) Alternate II (Mar 2004) of 52.219-7.

(16) 52.219-8, Utilization of Small Business Concerns (Oct 2018) (15 U.S.C. 637(d)(2) and (3)).

(17)(i) 52.219-9, Small Business Subcontracting Plan (Aug 2018) (15 U.S.C. 637(d)(4))

(ii) Alternate I (Jan 2017) of 52.219-9.

PAGE 16 OF 36 1305M219QNFFM0254

(iii) Alternate II (Nov 2016) of 52.219-9.

(iv) Alternate III (Nov 2016) of 52.219-9.

(v) Alternate IV (Aug 2018) of 52.219-9

(18) 52.219-13, Notice of Set-Aside of Orders (Nov 2011) (15 U.S.C. 644(r)).

(19) 52.219-14, Limitations on Subcontracting (Jan 2017) (15 U.S.C.637(a)(14)).

(20) 52.219-16, Liquidated Damages-Subcontracting Plan (Jan 1999) (15 U.S.C. 637(d)(4)(F)(i)).

(21) 52.219-27, Notice of Service-Disabled Veteran-Owned Small Business Set-Aside (Nov 2011)

(15 U.S.C. 657f).

_X_ (22) 52.219-28, Post Award Small Business Program Representation (Jul 2013) (15 U.S.C. 632(a)(2)).

(23) 52.219-29, Notice of Set-Aside for, or Sole Source Award to, Economically Disadvantaged

Women-Owned Small Business Concerns (Dec 2015) (15 U.S.C. 637(m)).

(24) 52.219-30, Notice of Set-Aside for, or Sole Source Award to, Women-Owned Small Business

Concerns Eligible Under the Women-Owned Small Business Program (Dec 2015) (15 U.S.C. 637(m)).

_X_ (25) 52.222-3, Convict Labor (June 2003) (E.O.11755).

(26) 52.222-19, Child Labor-Cooperation with Authorities and Remedies (Jan 2018) (E.O.13126).

_X_ (27) 52.222-21, Prohibition of Segregated Facilities (Apr 2015).

_X_ (28)(i) 52.222-26, Equal Opportunity (Sept 2016) (E.O.11246).

(ii) Alternate I (Feb 1999) of 52.222-26.

(29)(i) 52.222-35, Equal Opportunity for Veterans (Oct 2015) (38 U.S.C. 4212).

(ii) Alternate I (July 2014) of 52.222-35.

_X_ (30)(i) 52.222-36, Equal Opportunity for Workers with Disabilities (Jul 2014) (29 U.S.C.793).

(ii) Alternate I (July 2014) of 52.222-36.

(31) 52.222-37, Employment Reports on Veterans (Feb 2016) (38 U.S.C. 4212).

(32) 52.222-40, Notification of Employee Rights Under the National Labor Relations Act (Dec 2010)

(E.O. 13496).

_X_ (33)(i) 52.222-50, Combating Trafficking in Persons (Jan 2019) (22 U.S.C. chapter 78 and E.O.

13627).

(ii) Alternate I (Mar 2015) of 52.222-50 (22 U.S.C. chapter 78 and E.O. 13627).

(34) 52.222-54, Employment Eligibility Verification (Oct 2015). (Executive Order 12989). (Not applicable to the acquisition of commercially available off-the-shelf items or certain other types of commercial items as prescribed in 22.1803.)

(35)(i) 52.223-9, Estimate of Percentage of Recovered Material Content for EPA–Designated Items (May 2008) (42 U.S.C. 6962(c)(3)(A)(ii)). (Not applicable to the acquisition of commercially available off-the-shelf items.)

(ii) Alternate I (May 2008) of 52.223-9 (42 U.S.C. 6962(i)(2)(C)). (Not applicable to the acquisition of commercially available off-the-shelf items.)

(36) 52.223-11, Ozone-Depleting Substances and High Global Warming Potential Hydrofluorocarbons (Jun 2016) (E.O. 13693).

(37) 52.223-12, Maintenance, Service, Repair, or Disposal of Refrigeration Equipment and Air Conditioners (Jun2016) (E.O. 13693).

(38)(i) 52.223-13, Acquisition of EPEAT®-Registered Imaging Equipment (Jun 2014) (E.O.s 13423 and 13514).

(ii) Alternate I (Oct 2015) of 52.223-13.

(39)(i) 52.223-14, Acquisition of EPEAT®-Registered Televisions (Jun 2014) (E.O.s 13423 and 13514).

(ii) Alternate I (Jun 2014) of 52.223-14.

(40) 52.223-15, Energy Efficiency in Energy-Consuming Products (Dec 2007) (42 U.S.C. 8259b).

(41)(i) 52.223-16, Acquisition of EPEAT®-Registered Personal Computer Products (Oct 2015) (E.O.s

13423 and 13514).

(ii) Alternate I (Jun 2014) of 52.223-16.

(42) 52.223-18, Encouraging Contractor Policies to Ban Text Messaging While Driving (Aug 2011)

(E.O. 13513).

(43) 52.223-20, Aerosols (Jun 2016) (E.O. 13693).

(44) 52.223-21, Foams (Jun 2016) (E.O. 13693).

(45)(i) 52.224-3 Privacy Training (Jan 2017) (5 U.S.C. 552 a).

(ii) Alternate I (Jan 2017) of 52.224-3.

(46) 52.225-1, Buy American-Supplies (May 2014) (41 U.S.C. chapter 83).

__ (47)(i) 52.225-3, Buy American-Free Trade Agreements-Israeli Trade Act (May 2014) (41

U.S.C. chapter 83, 19 U.S.C. 3301 note, 19 U.S.C. 2112 note, 19 U.S.C. 3805 note, 19 U.S.C. 4

PAGE 17 OF 36 1305M219QNFFM0254

001 note, Pub. L. 103-182, 108-77, 108-78, 108-286, 108-302, 109-53, 109-169, 109-283, 110-138, 112-41, 112-42, and 112-43.

(ii) Alternate I (May 2014) of 52.225-3.

(iii) Alternate II (May 2014) of 52.225-3.

__ (iv) Alternate III (May 2014) of 52.225-3.

(48) 52.225-5, Trade Agreements (Aug 2018) (19 U.S.C. 2501, et seq., 19 U.S.C. 3301 note).

_X_49) 52.225-13, Restrictions on Certain Foreign Purchases (June 2008) (E.O.’s, proclamations, and statutes administered by the Office of Foreign Assets Control of the Department of the Treasury).

(50) 52.225-26, Contractors Performing Private Security Functions Outside the United States

(Oct 2016) (Section 862, as amended, of the National Defense Authorization Act for Fiscal Year 2008;10 U.S.C. 2302 Note).

(51) 52.226-4, Notice of Disaster or Emergency Area Set-Aside (Nov 2007) (42 U.S.C. 5150).

(52) 52.226-5, Restrictions on Subcontracting Outside Disaster or Emergency Area (Nov 2007)

(42 U.S.C. 5150).

(53) 52.232-29, Terms for Financing of Purchases of Commercial Items (Feb 2002)

(41 U.S.C.4505, 10 U.S.C.2307(f)).

(54) 52.232-30, Installment Payments for Commercial Items (Jan 2017)

(41 U.S.C.4505, 10 U.S.C.2307(f)).

_X_ (55) 52.232-33, Payment by Electronic Funds Transfer-System for Award Management (Oct 2018)

(31 U.S.C. 3332).

(56) 52.232-34, Payment by Electronic Funds Transfer-Other than System for Award Management

(Jul 2013) (31 U.S.C.3332).

(57) 52.232-36, Payment by Third Party (May 2014) (31 U.S.C.3332).

(58) 52.239-1, Privacy or Security Safeguards (Aug 1996) (5 U.S.C. 552a).

(59) 52.242-5, Payments to Small Business Subcontractors (Jan 2017) (15 U.S.C. 637(d)(13)).

(60)(i) 52.247-64, Preference for Privately Owned U.S.-Flag Commercial Vessels (Feb 2006)

(46 U.S.C. Appx. 1241(b) and 10 U.S.C. 2631).

(ii) Alternate I (Apr 2003) of 52.247-64.

(iii) Alternate II (Feb 2006) of 52.247-64.

(c) The Contractor shall comply with the FAR clauses in this paragraph (c), applicable to commercial services, that the Contracting Officer has indicated as being incorporated in this contract by reference to implement provisions of law or Executive orders applicable to acquisitions of commercial items:

[Contracting Officer check as appropriate.]

(1) 52.222-17, Nondisplacement of Qualified Workers (May 2014)(E.O. 13495).

(2) 52.222-41, Service Contract Labor Standards (Aug 2018) (41 U.S.C. chapter 67).

(3) 52.222-42, Statement of Equivalent Rates for Federal Hires (May 2014)

(29 U.S.C. 206 and 41 U.S.C. chapter 67).

(4) 52.222-43, Fair Labor Standards Act and Service Contract Labor Standards-Price Adjustment

(Multiple Year and Option Contracts) (Aug 2014) (29 U.S.C. 206 and 41 U.S.C. chapter 67).

(5) 52.222-44, Fair Labor Standards Act and Service Contract Labor Standards-Price Adjustment

(May 2014) (29 U.S.C. 206 and 41 U.S.C. chapter 67).

(6) 52.222-51, Exemption from Application of the Service Contract Labor Standards to Contracts for

Maintenance, Calibration, or Repair of Certain Equipment-Requirements (May 2014) (41 U.S.C. chapter 67).

(7) 52.222-53, Exemption from Application of the Service Contract Labor Standards to Contracts for Certain Services-Requirements (May 2014) (41 U.S.C. chapter 67).

(8) 52.222-55, Minimum Wages Under Executive Order 13658 (Dec 2015).

(9) 52.222-62, Paid Sick Leave Under Executive Order 13706 (Jan 2017) (E.O. 13706).

(10) 52.226-6, Promoting Excess Food Donation to Nonprofit Organizations (May 2014)

(42 U.S.C. 1792).

(d) Comptroller General Examination of Record. The Contractor shall comply with the provisions of this paragraph (d) if this contract was awarded using other than sealed bid, is in excess of the simplified acquisition threshold, and does not contain the clause at 52.215-2, Audit and Records-Negotiation.

(1) The Comptroller General of the United States, or an authorized representative of the Comptroller General, shall have access to and right to examine any of the Contractor’s directly pertinent records involving transactions related to this contract.

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(2) The Contractor shall make available at its offices at all reasonable times the records, materials, and other evidence for examination, audit, or reproduction, until 3 years after final payment under this contract or for any shorter period specified in FAR subpart 4.7, Contractor Records Retention, of the other clauses of this contract. If this contract is completely or partially terminated, the records relating to the work terminated shall be made available for 3 years after any resulting final termination settlement. Records relating to appeals under the disputes clause or to litigation or the settlement of claims arising under or relating to this contract shall be made available until such appeals, litigation, or claims are finally resolved.

(3) As used in this clause, records include books, documents, accounting procedures and practices, and other data, regardless of type and regardless of form. This does not require the Contractor to create or maintain any record that the Contractor does not maintain in the ordinary course of business or pursuant to a provision of law.

(e)(1) Notwithstanding the requirements of the clauses in paragraphs (a), (b), (c), and (d) of this clause, the Contractor is not required to flow down any FAR clause, other than those in this paragraph (e)(1) in a subcontract for commercial items. Unless otherwise indicated below, the extent of the flow down shall be as required by the clause-

(i) 52.203-13, Contractor Code of Business Ethics and Conduct (Oct 2015) (41 U.S.C. 3509).

(ii) 52.203-19, Prohibition on Requiring Certain Internal Confidentiality Agreements or Statements (Jan

2017) (section 743 of Division E, Title VII, of the Consolidated and Further Continuing Appropriations Act, 2015 (Pub. L. 113-235) and its successor provisions in subsequent appropriations acts (and as extended in continuing resolutions)).

(iii) 52.204-23, Prohibition on Contracting for Hardware, Software, and Services Developed or Provided by Kaspersky Lab and Other Covered Entities (Jul 2018) (Section 1634 of Pub. L. 115-91).

(iv) 52.219-8, Utilization of Small Business Concerns (Oct 2018) (15 U.S.C.637(d)(2) and (3)), in all subcontracts that offer further subcontracting opportunities. If the subcontract (except subcontracts to small business concerns) exceeds $700,000 ($1.5 million for construction of any public facility), the subcontractor must include 52.219-8 in lower tier subcontracts that offer subcontracting opportunities.

(v) 52.222-17, Nondisplacement of Qualified Workers (May 2014) (E.O. 13495). Flow down required in accordance with paragraph (l) of FAR clause 52.222-17.

(vi) 52.222-21, Prohibition of Segregated Facilities (Apr 2015).

(vii) 52.222-26, Equal Opportunity (Sept 2015) (E.O.11246).

(viii) 52.222-35, Equal Opportunity for Veterans (Oct 2015) (38 U.S.C.4212).

(ix) 52.222-36, Equal Opportunity for Workers with Disabilities (Jul 2014) (29 U.S.C.793).

(x) 52.222-37, Employment Reports on Veterans (Feb 2016) (38 U.S.C.4212)

(xi) 52.222-40, Notification of Employee Rights Under the National Labor Relations Act (Dec 2010) (E.O.

13496). Flow down required in accordance with paragraph (f) of FAR clause 52.222-40.

(xii) 52.222-41, Service Contract Labor Standards (Aug 2018) (41 U.S.C. chapter 67).

(xiii)

(A) 52.222-50, Combating Trafficking in Persons (Jan 2019) (22 U.S.C. chapter 78 and E.O 13627).

(B) Alternate I (Mar 2015) of 52.222-50(22 U.S.C. chapter 78 and E.O 13627).

(xiv) 52.222-51, Exemption from Application of the Service Contract Labor Standards to Contracts for

Maintenance, Calibration, or Repair of Certain Equipment-Requirements (May 2014) (41 U.S.C. chapter 67).

(xv) 52.222-53, Exemption from Application of the Service Contract Labor Standards to Contracts for Certain Services-Requirements (May 2014) (41 U.S.C. chapter 67).

(xvi) 52.222-54, Employment Eligibility Verification (Oct 2015) (E.O. 12989).

(xvii) 52.222-55, Minimum Wages Under Executive Order 13658 (Dec 2015).

(xviii) 52.222-62, Paid Sick Leave Under Executive Order 13706 (Jan 2017) (E.O. 13706).

(xix)

(A) 52.224-3, Privacy Training (Jan 2017) (5 U.S.C. 552a).

(B) Alternate I (Jan 2017) of 52.224-3.

(xx) 52.225-26, Contractors Performing Private Security Functions Outside the United States (Oct 2016)

(Section 862, as amended, of the National Defense Authorization Act for Fiscal Year 2008; 10 U.S.C. 2302 Note).

(xxi) 52.226-6, Promoting Excess Food Donation to Nonprofit Organizations (May 2014) (42 U.S.C. 1792).

Flow down required in accordance with paragraph (e) of FAR clause 52.226-6.

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(xxii) 52.247-64, Preference for Privately Owned U.S.-Flag Commercial Vessels (Feb 2006) (46 U.S.C.

Appx.1241(b) and 10 U.S.C.2631). Flow down required in accordance with paragraph (d) of FAR clause 52.247-64.

(2) While not required, the Contractor may include in its subcontracts for commercial items a minimal number of additional clauses necessary to satisfy its contractual obligations.

(End of clause)

FAR 52.217-9 OPTION TO EXTEND THE TERM OF THE CONTRACT (MAR 2000)

(a) The Government may extend the term of this contract by written notice to the Contractor within FIVE

(5) DAYS; provided that the Government gives the Contractor a preliminary written notice of its intent to extend at least ONE (1) DAY before the contract expires. The preliminary notice does not commit the

Government to an extension.

(b) If the Government exercises this option, the extended contract shall be considered to include this option clause.

(c) The total duration of this contract, including the exercise of any options under this clause, shall not exceed _ONE YEAR/SIX MONTHS (months) (years).

(End of Clause)

FAR 52.252-2, CLAUSES INCORPORATED BY REFERENCE (FEB 1998)

This contract incorporates one or more clauses by reference, with the same force and effect as if they were given in full text. Upon request, the Contracting Officer will make their full text available. Also, the full text of a clause may be accessed electronically at this address: www.acquisition.gov

The following contract clauses are incorporated by reference:

FAR 52.204-13, System for Award Management Maintenance (OCT 2018) FAR 52.212-4, Contract terms and conditions, Commercial Items (OCT 2018) FAR 52.232-39, Unenforceability of Unauthorized Obligations (JUN 2013) FAR 52.232-40, Providing Accelerated Payments to Small Business Subcontractors (DEC 2013) CAR 1352.201-70 Contracting Officer's Authority (APR 2010) CAR 1352.209-73 Compliance with the Laws…