SAMDaily.us | PRESOL | A | Adaptive Multi Source Exploitation of Documents (AMUSED)

SAMDAILY.US - ISSUE OF JUNE 05, 2020 SAM #6763
SOLICITATION NOTICE

A -- Adaptive Multi Source Exploitation of Documents (AMUSED)

Notice Date: 6/3/2020 5:36:24 AM
Notice Type: Presolicitation
NAICS: 541715 — Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)
Contracting Office: FA8750 AFRL RIK ROME NY 13441-4514 USA
ZIP Code: 13441-4514
Solicitation Number: FA8750-18-S-7009
Archive Date: 09/30/2022
Point of Contact: Gail E. Marsh, Contracting Officer, Phone: 315-330-7518
E-Mail Address: Gail.Marsh@us.af.mil
(Gail.Marsh@us.af.mil)
Description: AMENDMENT 2 for BAA FA8750-18-S-7009 The purpose of this modification is to republish the original announcement pursuant to FAR 35.016(c). This republishing also includes the following changes: � (a) Part I-updated Contracting Officer point of contact � (b) Section I, added information regarding Fundamental Research and Cloud Computing; � (c) Section III, Eligibility Information has been updated; � (d) Section IV, 4.a., updated the Cost Sharing or Matching language; � (e) Section IV, 4.c., updated the link in the Executive Compensation and First-Tier Subcontract/Subrecipient paragraph; � (f) Section V, updated White Paper/Proposal Review Process and the Simplified Acquisition Threshold in paragraph 3; � (g) Section VI, Proposal Formattting, updated the date for the RI-Specific Proposal Preparation Instructions; � (h) Section VI, updated Administrative and National Policy Requirements language, and added Small Business Participation language; � (i) Section VII, Agency Contacts are updated; No other changes have been made. AMENDMENT 3 to BAA FA8750-18-S-7009 The purpose of this modification is to republish the original announcement, incorporating any previous amendments, pursuant to FAR 35.016(c). This republishing also includes the following changes: Part I, Overview Information: Updates FBO reference to Beta SAM website;� Updates the types of instruments that may be awarded; � Part II, Full Text Announcement: Section III.2.a.2, updates the DCSA website; Section IV.3.a, updates the DCSA website; Section IV.3.c, updates the mailing instructions; Section IV.4.e, updated the link to reference the Beta SAM website; Section VI.1, updates the RI Specific Proposal Preparation Instructions date and Beta SAM link; Section VI.4, updates paragraph lettering and adds paragraphs d and e; Section VI.7, adds paragraph d; Section VII: updated the OMBUDSMAN. No other changes have been made. ________________________________________ NAICS CODE:� 541715 FEDERAL AGENCY NAME: Department of the Air Force, Air Force Materiel Command, AFRL - Rome Research Site, AFRL/Information Directorate, 26 Electronic Parkway, Rome, NY, 13441-4514 BAA ANNOUNCEMENT TYPE:� Modification/Amendment � BROAD AGENCY ANNOUNCEMENT (BAA) TITLE:� Adaptive Multi Source Exploitation of Documents (AMUSED)�� BAA NUMBER: FA8750-18-S-7009 � PART I � OVERVIEW INFORMATION This announcement is for an Open, Two (2) Step BAA which is open and effective until 30 Sep 2022.� Only white papers are due at this time.� While white papers will be considered if received prior to 3:00 PM on 30 Sep 2022, the following submission dates are suggested to best align with projected funding: FY18 � The initial white paper submissions for each Focus Area is due as follows: �� 16 Jan 18 � Focus Area C/Text Analytics for Cyber Domain �� 12 Mar 18 � Focus Area A/Global Treat Discovery and Identification �� 16 Apr 18 � Focus Area B/Emerging Threat Analytics � Thereafter suggested submissions dates for all the focus areas are: � FY19 by 30 Mar 2018 FY20 by 29 Mar 2019 FY21 by 27 Mar 2020 FY22 by 26 Mar 2021 � Offerors should monitor the Contract Opportuities on the Beta SAM website at https://beta.SAM.gov in the event this announcement is amended. �� CONCISE SUMMARY OF FUNDING OPPORTUNITY:� The Air Force Research Laboratory, Information Directorate is seeking innovative analytics, analytical tools, algorithm developments, projects, and experiments focused on achieving Adaptive Multi-Source Exploitation of Documents (AMUSED).�� This BAA is a follow-on to BAA AFRL-RIK-2015-0019 Multi-Source Information Extraction and Network Analysis (MUSIENA). BAA ESTIMATED FUNDING:� Total funding for this BAA is approximately $24.9M.� Individual awards will not normally exceed 36 months with dollar amounts normally ranging from $100K to $900K each.� There is also the potential to make awards up to any dollar value as long as the value does not exceed the available BAA ceiling amount.� ANTICIPATED INDIVIDUAL AWARDS:� Multiple Awards are anticipated TYPE OF INSTRUMENTS THAT MAY BE AWARDED: Procurement contracts, grants, cooperative agreements or other transactions (OT) depending upon the nature of the work proposed. In the event that an Other Transaction for Prototype agreement is awarded as a result of this competitive BAA, and the prototype project is successfully completed, there is the potential for a prototype project to transition to award of a follow-on production contract or transaction. The Other Transaction for Prototype agreement itself will also contain a similar notice of a potential follow-on production contract or agreement. AGENCY CONTACT INFORMATION:� All white paper and proposal submissions and any questions of a technical nature shall be directed to the cognizant Technical Point of Contact (TPOC) as specified below (unless otherwise specified in the technical area): BAA MANAGER:�� TPOC: Edward DePalma �� Mailing Address: AFRL/RIEA, 525 Brooks Road, Rome NY 13441-4505 �� Telephone:� (315)330-3069 �� Email: edward.depalma@us.af.mil Questions of a contractual/business nature shall be directed to the cognizant contracting officer, as specified below (email requests are preferred): �� Amber Buckley �� Telephone (315) 330-3605 �� Email:� Amber.Buckley@us.af.mil Emails must reference the solicitation (BAA) number and title of the acquisition. Communication between Prospective Offerors and Government Representatives:� Dialogue between prospective offerors and Government representatives is encouraged.� Technical and contracting questions can be resolved in writing or through open discussions. Discussions with any of the points of contact shall not constitute a commitment by the Government to subsequently fund or award any proposed effort. Only Contracting Officers are legally authorized to commit the Government. � Offerors are cautioned that evaluation ratings may be lowered and/or proposal rejected if proposal preparation (Proposal format, content, etc.) and/or submittal instructions are not followed. � � � � � � � � � � � � � � � � � � � � � � � PART II � FULL TEXT ANNOUNCEMENT � BROAD AGENCY ANNOUNCEMENT (BAA) TITLE:� Adaptive Multi Source Exploitation of Documents (AMUSED) �� BAA NUMBER: BAA FA8750-18-S-7009 CATALOG OF FEDERAL DOMESTIC ASSISTANCE (CFDA) Number: 12.800 & 12.910 � I. �FUNDING OPPORTUNITY DESCRIPTION: The Information Directorate, Information Fusion Branch, is soliciting white papers under this announcement for unique and innovative technologies to explore and develop Adaptive Multi Source Exploitation of Documents (AMUSED) capabilities including but not limited to, analytics, analytical tools, algorithm developments, projects, and experiments that will provide the Air Force the means to better conduct analytical operations in support of their Intelligence, Surveillance, and Reconnaissance mission including Cyber. This announcement is comprised of three research areas: (1) Global Threat Discovery and Identification (GTD-ID); (2) Emerging Threat Analytics (ETA); and (3) Text Analytics for Cyber Domain (TA4CD), where each has research areas that taken together comprise the focus of AMUSED research and development. BACKGROUND:� Past research in text analysis has led to the automated capabilities that are now in use to extract relevant information from large volumes of textual data. The development of this technology has reduced textual data overload, increased the accuracy of analysis, and decreased the cycle time and manpower requirements needed to assess threats and vulnerabilities. However, this is a situation that has not remained static from either the perspective of the anticipated number of data sources or projected analytical needs.� Further development is required to not just keep pace but to move beyond current performance levels, to overcome limitations in moving to new data types and domains, and to achieve new, more sophisticated capabilities. Fundamentally the analysis of textual content must produce higher levels of comprehension and understanding than presently exists. As textual information has increased in both quantity and complexity the demands for greater analytical capabilities have also grown dramatically. While basic documents still comprise a large portion of textual information, valuable content can now be extracted from a range of other sources including a variety of social media material (chat, email, blogs, etc.), many open source materials and the metadata descriptors that relate back to additional media forms (video, imagery, speech, etc.). The value of textual analysis going forward will now be gauged by the ability to work effectively in and across these and other components of a complex data environment while advancing the capabilities in exploiting traditional sources. Current network discovery and analysis science has focused on static relationship or event based networks of interest. This occurs primarily on one or two particular data sources. These capabilities are adept at enabling an analyst to effectively analyze network data within a single data source, but the analyst is then left to make mental correlations of observations and conclusions drawn from one data source to other data sources. Furthermore, current input methods do not account for semantic equivalences during the ingestion of the data, making the analyst�s job even more difficult. One of the greatest technical challenges facing all decision support systems is the heterogeneous aspect of the data that is collected by millions of sensors and the different stovepipe architectures used to store this data. In order to perform useful analytics, a composite picture of the key entities, events, and locations need to be pieced together from the original disparate data sources. The ingesting and integrating of information from disparate data sources remains a difficult and unresolved problem. In the Cyber Domain, multiple analyst groups, with diverse Mission Areas, need rapid, effective means to identify Essential Element of Interest (EEIs), in support of both Cyber Operations and Defensive Cyber Analysis.�� EEIs are pieces of information that answer questions deemed critical to mission accomplishment (see formal definition in Joint Publication 2-0, Joint Intelligence, dated 22 Oct 2013). OBJECTIVES:� The three research areas within this AMUSED BAA are (1) Global Threat Discovery and Identification (GTD-ID), (2) Emerging Threat Analytics (ETA) and (3) Text Analytics for Cyber Domain (TA4CD) with each containing the key technical Focus Areas for development. Submissions/White Papers should clearly identify the AMUSED research area or specify the individual Focus Area being addressed. A. Global Threat Discovery and Identification (GTD-ID):�� The GTD-ID research area must deliver advanced text exploitation to provide deeper understanding of the information that can be pulled from multi-source unstructured text. The individual focus areas of this research area are User Driven Domain Customization, Complex Event Extraction and Text Exploitation Platform for Multi-Source Analysis (MSA).� These technologies are key enablers that will more widely deliver affordable, easy to use, accurate text exploitation technology to AF users and each may be the emphasis of a white paper submission under GTD-ID. Focus Area A1 - User Driven Domain Customization As text exploitation tools gain acceptance in the DOD and Intelligence communities, they are exposed to a wider variety of domains of interest.� Even within the Air Force, different user groups are interested in different types of documents, on different topics, with different entities, relations and events of interest.� In order to achieve maximum results for each individual user, GTD-ID products must support customization for their specific use-case.� This will enable ready transition and adoption of this technology through the development of effective user-driven domain customization allowing subject matter experts (SME) to tailor GTD-ID capabilities to their environment rather than require specialized technical expertise to perform this customization. An analyst should not be required to understand the underlying technology; they should be able to concentrate on what they do best, which is the task of analyzing data.� This focus area will develop robust techniques allowing domain experts to quickly, affordably and effectively customize text extraction capabilities to work on their new text types and domains.� While AFRL/RI has sponsored prior R&D in domain porting which has made progress towards this goal, there is still considerable work to be done to reach the higher level of domain customization performance that will leverage advanced learning techniques to provide a user-driven capability. Focus Area A2 - Complex Event Extraction Complex event extraction means moving beyond individual events that do not provide a sufficient level of situational awareness and toward a broader and more comprehensive understanding of collective events and their importance.� Achieving success in complex event extraction should include extracting/associating event arguments with events, extracting relations between events (e.g., before, prerequisite-of, causal); within-document event co-reference; cross-document event co-reference; and developing algorithms that are capable of understanding how events fit together in a logical way. To truly understand what is going on there must be this understanding of how the individual events fit together conceptually and into some higher-level logical structure (e.g., based on causality, temporal ordering, etc.).� Research into event structures, event scenarios, event schemas, and event scripts would be of particular benefit in addressing this need. As the amount of event information to be assessed accumulates, there will be a need to measure the confidence level in the understanding of the larger event (scenario) in total. The overall intent of this research thrust is to provide the technological foundation to enable more powerful event-centric analysis (from information from text). Focus Area A3 - Text Exploitation Platform for Multi-Source Analysis (TE Platform for MSA) The TE Platform for MSA will achieve a comprehensive, robust, flexible, scalable, web-based text exploitation framework for multi-source text analysis. The framework will support �end-to-end� functionality, including (but not limited to) data preparation and ingestion, including document zoning to enable separate processing of structured (header, footer, tables, lists, etc.) and unstructured portions of text; textual data analysis and visualization; multi-source analysis; search/filtering of document collections; Information Extraction (IE) and content management.� Additionally there are key design requirements which include scalability, flexibility and robustness that will promote application for new/different use-cases along with adaptability and vendor-independence which will promote openness of the solution. Adhering to best practices and standards that help the framework attain these design requirements is encouraged, such as the Apache Unstructured Information Management Architecture (UIMA). The end result will be a strong and viable technology platform for rapidly transitioning new/maturing text exploitation capabilities to our users in support of Multi-Source Analysis. B.� Emerging Threat Analytics (ETA):� ETA will address key challenges in multi-source fusion and social media exploitation empowering the analyst with high performance, high accuracy and easily adaptable tools for threat assessment, explanation, and anticipation surrounding individuals of interest, groups and events. This research area will support the design and implementation of state-of-the-art technologies to achieve data alignment for large-scale, disparate data sources and create analytical models for estimating �tactical� phenomena in society at large based on phenomena that occur in social media. The individual focus areas of this research area are Multi-Source Data Fusion and Social Media Analytics. Focus Area B1 - Multi-Source Data Fusion The objective of ETA is to employ a flexible and adaptive framework through which layered multi-modal network analysis (LMMNA) can be achieved, to provide the analyst the ability to understand and interrogate the inter and intra source relationships that have and continue to evolve spatially and temporally within and across the spectrum of intelligence data. The framework will incorporate technologies that amalgamate and align disparate data layers (sources and domains); apply entity resolutions to associate and cluster like-entities within and across layers to derive a single graph; and provide access to batch and real-time graph analytic algorithms and heuristics to derive and maintain graph measures, graph matching and graph querying, leveraged to identify valuable semantic and structural elements, and discover new patterns of interest.� Simply put, the framework will ultimately facilitate the alignment, association, and analysis of disparate layers of intelligence data to yield a cohesive and comprehensive picture of the evolving cross layer situational awareness. Adhering to best practices and standards that help the framework attain these design requirements is encouraged, such as the Apache TinkerPop Open Source Graph Computing Framework. When analyzing multi-source information, several issues can arise that inhibit the ability to produce an effectual analysis. Errors in the processing pipeline, data entry, and reporting as well as redundant information and deliberate attempts at misinformation will persist throughout the data. Identifying these knowledge conflicts is an important yet unresolved task. Current methods rely on linguistic cues with little regard to semantics. These constructs measure various features of the text, including parts of speech, tense, and voice. However, it turns out that these measures and constructs are not significant predictors of deceptive behavior and explain only a fraction of the variance. This necessitates a new approach that not only understands the syntactic features of text, but also the semantic features. Focus Area B2 - Social Media Analytics� Analytical capabilities that can be applied to social media communications have been limited in scope, scale and reach.� Identifying mission relevant information from billions of posts generated by millions of users is currently a labor-intensive and subjective process. Central concerns in the analysis of social media data are in the vast amount of data that is constantly being produced and the actual analytics that need to be performed. ETA must develop an improved method for noise reduction and analysis in order to extensively and effectively search the massive social media datasets. While noise reduction can be used to refine the search space, analysts will still be confronted with content that requires an automated capability that will be able to readily investigate individuals, uncover relationships, and follow connections to reveal networks of people and organizations. Analysts need to understand who the protagonists are, what they have done, and what they are planning. Automated analysis methods are needed that will help analysts rapidly decipher unfolding events and even attempt to predict future events. Where current social media analytics have been focused on analyzing the explicit relationships additional research under ETA is needed to uncover the implicit relationships within social media that are not directly evident. Areas of interest for this research of Social Media content and structure would develop the automated methods to identify embedded motivations and attitudes, expose non-explicit relationships between individuals and groups along with their dynamics in how they interact. C.� Text Analytics for Cyber Domain (TA4CD): This research area will develop adaptive technology enabling multiple analyst groups to more effectively find and exploit Essential Elements of Information (EEIs) relevant to their Mission Area with particular emphasis on the Cyber Domain. As previously stated, EEIs are pieces of information that answer questions deemed critical to mission accomplishment (see formal definition in Joint Publication 2-0, Joint Intelligence, dated 22 Oct 2013). The individual focus areas of this research area are Enhanced Search for EEI Information, Adaptive Multi-Source Processing and Cyber Entity Co-Reference. � Focus Area C1 - Enhanced Search for EEI Information The goal of this Focus Area is to research and develop technology that significantly improves analysts� ability to find information relevant to their EEIs.� Areas of interest include, but are not limited to, Web-Scale Multi-Lingual Document Search and EEI Modeling and Search. Web-Scale Multi-Lingual Document Search: Analysts need to be able to perform fast, effective web-scale search (100M documents+) of multi-lingual data sources, to find documents containing information potentially relevant to their EEIs.�� Achieving state-of-the-art performance in terms of the relevancy and completeness of results is key.�� The technology must be portable to new languages and genres, for both high and low-resource languages, preferably by a user vs technology expert.� EEI Modeling & Search: There is a need for innovative technology to effectively search large volumes of potentially relevant documents for information pertinent to answering an EEI.� EEIs can range from very specific queries, to very high level questions, such as �How stable is Country X?�� Developing search technology that can provide answers to such high-level questions is a challenge, because it really requires answering a number of sub-questions whose answers all contribute to answering the EEI.� So, for example, in order to answer the EEI �How stable is Country X?�, one might need to answer a number of sub-questions whose answers all contribute to addressing the EEI, such as �How stable is the current Government in Country X?�, �Are banks open for normal business in Country X?�, �Is food readily available in Country X?�, and �Have the number of violent crimes increased in Country X?�� In other words, answering high-level EEIs requires the ability to perform �meta-searches� to define/manage/refine the sub-questions (sub-queries) that must be answered to determine the answer to the high-level EEI.� Technology is needed to model EEIs and their sub-queries as sets of Structured Natural Language Queries; manage and refine the EEI query model over time; use the query models to search for relevant information, as well as expand/refine search results based on feedback from the user. The technology must achieve high relevancy of search results, and provide a means to improve system results over timebased on user feedback.� Alternate approaches will be considered.� Technical approaches must also be user-adaptable to different Mission Areas.� The primary language of interest is English; being adaptable to other languages is a plus but not a requirement. Focus Area C2 - Adaptive Multi-Source Processing Adaptive Multi-Source Processing will research and develop user-adaptive text exploitation technology that will make it possible to customize text exploitation capabilities to support multiple groups of analysts with different mission areas, different document types, and different information requirements.� Areas of interest include, but are not limited to, User-Driven Domain Customization (described above under the GTD-ID Focus Area); TE Platform for MSA (described above under the GTD-ID Focus Area); Adaptive Text Zoning; and Document Structure Analysis. Adaptive Text Zoning.� Currently, text zoning is a manual process that requires a technology expert to analyze new and diverse document types, determine their structure, and then manually create text zoning rules to identify and process the various structured and unstructured portions of the document.� The intent of this area is to research and develop an automated capability for Text Zoning that can be adapted to new document types/structures with either no intervention, or very minimal intervention by a user who is not a technology expert.�� Document Structure Analysis. Very large documents may consist of multiple coherent discourse units that are more effectively processed as individual sub-documents by a text exploitation platform.� The goal of this area is to research discourse parsing, or alternative methods for document structure analysis, in order to identify coherent discourse units within large documents for text processing.� � Focus Area C3 - Cyber Entity Co-Reference Cyber Entity Co-Reference will provide the analyst with capabilities to automate the consolidation of Entities extracted from text that are relevant to the Cyber Domain, both within a document and across multiple documents.�� This includes, but is not limited to, Equipment Entity Co-Reference. Equipment Entity Co-Reference (within-document and cross-document).� Within and cross-document entity co-reference capabilities already exist for named entities like People and Organizations, enabling automated consolidation, analysis and visualization of this information.� Analysts in the Cyber Domain need a comparable capability: within and cross-document Equipment Entity Co-Reference algorithms that will enable automated consolidation of equipment information extracted from text, both within and across many documents.� However, the way in which Equipment Entities are discussed in text introduce a number of unique challenges that would make equipment entity co-reference more of challenge than the co-reference for named entities like People, Organizations and Geo-Political Entities. For example, Equipment Entities are typically referred to by a class name (e.g., �the XYZ Router) vs by a uniquely identifiable name (e.g., �the XYZ router with serial #123�), which increases ambiguity.� �Research in this area will result in a proof-of-concept for within and cross-document Equipment Entity Co-Reference, enabling consolidation of equipment information within and across documents.� IMPORTANT NOTES REGARDING: � FUNDAMENTAL RESEARCH.� It is DoD policy that the publication of products of fundamental research will remain unrestricted to the maximum extent possible. National Security Decision Directive (NSDD) 189 defines fundamental research as follows: �Fundamental research� means basic and applied research in science and engineering, the results of which ordinarily are published and shared broadly within the scientific community, as distinguished from proprietary research and from industrial development, design, production, and product utilization, the results of which ordinarily are restricted for proprietary or national security reasons. As of the date of publication of this BAA, the Government cannot identify whether work proposed under this BAA may be considered fundamental research and may award both fundamental and non-fundamental research.� Proposers should indicate in their proposal whether they believe the scope of the research included in their proposal is fundamental or not. While proposers should clearly explain the intended results of their research, the Government shall have sole discretion to select award instrument type and to negotiate all instrument terms and conditions with selectees. Appropriate clauses will be included in resultant awards for non-fundamental research to prescribe publication requirements and other restrictions, as appropriate. For certain research projects, it may be possible that although the research being performed by the awardee is restricted research, a sub-awardee may be conducting fundamental research.� In those cases, it is the awardee�s responsibility to explain in their proposal why its sub-awardee�s effort is fundamental research. CLOUD COMPUTING.� In accordance with DFARS Clause 252.239-7010, if the development proposed requires storage of Government, or Government-related data on the cloud, offerors need to ensure that the cloud service provider proposed has been granted Provisional Authorization by the Defense Information Systems Agency (DISA) at the level appropriate to the requirement. II. AWARD INFORMATION: 1. FUNDING:� Total funding for this BAA is approximately $24.9M.� The anticipated funding to be obligated under this BAA is broken out by fiscal year as follows: � FY18 - $2.70M FY19 - $5.05M FY20 - $5.25M FY21 - $5.75M FY22 - $6.15M � The breakout of funding by fiscal year is a projection.� The total awards values in any given year will vary. � Individual awards will not normally exceed 36 months with dollar values ranging from $100K to $900K.� There is also the potential to make awards up to any dollar value as long as the value does not exceed the available BAA ceiling amount.� �� The Government reserves the right to select all, part, or none of the proposals received, subject to the availability of funds.� All potential Offerors should be aware that due to unanticipated budget fluctuations, funding in any or all areas may change with little or no notice. � 2.� FORM.� Awards of efforts as a result of this announcement will be in the form of contracts, grants, cooperative agreements or other transactions depending upon the nature of the work proposed.� � 3.� BAA TYPE:� This is a two-step open broad agency announcement.� This announcement constitutes the only solicitation.� � As STEP ONE � The Government is only soliciting white papers at this time.� DO NOT SUBMIT A FORMAL PROPOSAL.� Those white papers found to be consistent with the intent of this BAA may be invited to submit a technical and cost proposal.� See Section VI of this announcement for further details regarding the proposal.�� III. ELIGIBILITY INFORMATION: � 1.� ELIGIBILITY:� All qualified offerors who meet the requirements of this BAA may apply.� � 2.� FOREIGN PARTICIPATION/ACCESS: � This BAA is closed to foreign participation. Exceptions.� Fundamental Research.� If the work to be performed is unclassified, fundamental research, this must be clearly identified in the white paper and/or proposal.� See Part II, Section I for more details regarding Fundamental Research.� Offerors should still identify any performance by foreign nationals at any level (prime contractor or subcontractor) in their proposals.� Please specify the nationals� country of origin, the type of visa or work permit under which they are performing and an explanation of their anticipated level of involvement.� You may be asked to provide additional information during negotiations in order to verify the foreign citizen�s eligibility to participate on any contract or assistance agreement issued as a result of this announcement Foreign Ownership, Control or Influence (FOCI) companies who have mitigation plans/paperwork in place.� Proof of approved mitigation documentation must be provided to the contracting office focal point, Amber Buckley, Contracting Officer, telephone (315) 330-3605, or e-mail Amber.Buckley@us.af.mil prior to submitting a white paper and/or a proposal.� For information on FOCI mitigation, contact the contact the Defense Counterintelligence and Security Agency (DCSA).�� Additional details can be found at: https://www.dcsa.mil/mc/ctp/foci/ Foreign Nationals as Employees or Subcontractors. Applicable to any effort not considered Fundamental Research.� Offerors are responsible for ensuring that all employees and/or subcontractors who will work on a resulting contract are eligible to do so.� Any employee who ...
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A -- Adaptive Multi Source Exploitation of Documents (AMUSED)