This RFI seeks capability statements relating to the collection of research data from multi-camera video networks in support of computer vision research. Over the past five years, there have been notable advances in computer vision approaches to facilitate tracking and re-identification of persons in security camera networks. However, the primary datasets available to the research community for algorithm training and performance evaluations, while incredibly valuable, are somewhat limited in subject count, camera network scope, and environmental factors, resulting in a disconnect between the data being leveraged by researchers and the types of video data that would exist in actual video networks utilized by public safety and law enforcement entities. Further research in the area of computer vision within multi-camera video networks may support post-event crime scene reconstruction, protection of critical infrastructure and transportation facilities, military force protection, and in the operations of National Special Security Events.

This RFI seeks approaches, capabilities, and previous experience related to the ability to carry out a potential video research data collection with the following characteristics:

· Data from a large camera network containing a minimum of twenty (20) video feeds of varying positions, views, resolutions, and frame rates with both overlapping and non-overlapping fields of view;

· Data captured over a large region (~10,000 sq. meters) with multiple intersections, paths for pedestrian foot traffic, and buildings entrances/exits;

· Data collected in an urban or semi-urban environment with distractors and occlusions representative of real-world conditions, such as signs, vehicles (parked and moving), trees, and other obstructions;

· Data collected over multiple days with varying illumination and weather conditions;

· Data involving a minimum of 5,000 pedestrians;

· Data involving a minimum of 200 subject volunteers injected into the camera network view and given instructions on how to behave and/or where to go in the camera network;

· Annotations for all video footage detailing ground-truth bounding boxes of each person in each frame of each camera view to facilitate research, development, test, and evaluation of person tracking and re-identification algorithms;

· Minimum video footage for the entire dataset totaling an estimated 960 hours (20 cameras x 12 hrs/day x 4 days);

· A dataset that is permissible to be released to the general research community under a privacy, legal, and policy approved data release process.

Full information is available here.

Source: FedBizOpps

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The Intelligence Advanced Research Projects Activity (IARPA) is seeking information on research efforts in the area of machine learning with a particular focus on deep learning. This request for information (RFI) is issued solely for information gathering and planning purposes; this RFI does not constitute a formal solicitation for proposals. The following sections of this announcement contain details of the scope of technical efforts of interest, along with instructions for the submission of responses.

Background & Scope

This RFI seeks capability statements relating to machine and deep learning. Responses to this RFI should address the following points:

1. Respondent’s capabilities in the realm of machine and deep learning. Of specific interest is the respondent’s knowledge of, and experience implementing, current, cutting-edge machine learning techniques.
2. Respondent’s ability to perform research and development at the TOP SECRET//SCI level. This includes the presence of qualified, cleared personnel as well as appropriate network access.

Full information is available here.

Source: FedBizOpps

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The Intelligence Advanced Research Projects Activity (IARPA) seeks information regarding highly innovative approaches to securing Sensitive Compartmented Information Facilities (SCIFs). One area of interest is advanced materials or techniques to fully shield SCIFs from unintended radio frequency (RF), optical, magnetic, or acoustic transmissions. Second, we seek novel approaches or technologies enabling more effective and reliable technical surveillance countermeasures (TSCM) to detect and monitor surveillance attacks against existing and future SCIFs. Finally, we must both detect and secure the operation of wireless devices and networks near and within sensitive areas and prevent the unauthorized entry and operation of a variety of portable electronic devices.

Our adversaries continue to develop advanced tools and methods to enable technical surveillance attacks against sensitive U.S. facilities and personnel. These attacks are increasingly sophisticated and may exploit a variety of different sensors and data exfiltration paths. It is imperative that we are able to detect the operation and location of any adversarial sensors, whether concealed or hidden in plain sight, and eliminate all possible transmission paths for exfiltration of data. This requires that all structural components of the SCIF have highly effective shielding to a broad spectrum of energy and a variety of waveforms; it also requires that we have an advanced understanding of the ways in which adversarial sensors can be concealed and detected whether they are active or passive.

We anticipate that modern SCIFs and their surrounding facilities are likely to be constructed with smart-building management systems having both wired and wireless sensors; we must prevent such systems from leading to additional means for data exfiltration. We also anticipate that greater use of wireless networks and portable electronic devices in and near secure areas will demand greater protection of SCIFs from a variety of wireless exploitations. In addition, mobile communications devices and portable memory storage devices have become so ubiquitous that we seek improved means to detect the entry, presence and use of these devices in sensitive areas where such means are not harmful to human health. Finally, given the vulnerability of computer information technology (IT) to a variety of malware and other attacks, we anticipate blended attacks where cyber-attacks of implants on IT equipment will work in conjunction with other sensors or data exfiltration paths.

SCIFs, now and in the future, must be fully protected from RF transmission. Radio transmitters, both standalone and embedded within wireless networks, can create opportunities for surveillance when RF transmissions can pass into and out of secure areas; therefore, robust attenuation of RF and other electro-magnetic (EM) carriers is desired using techniques that may go beyond the capabilities of our current standard, certified shielded enclosures.

The types of RF threats that should be considered include signals associated with wireless networking, commercial communications, extremely low power signals, spread spectrum, burst and frequency hopping transmission, various modulation formats with and without encryption, etc. In addition, we must attenuate both RF/EM and optical propagation in the windows of our secure facilities. This can present an even greater challenge; current RF window technologies sometimes over-attenuate visible light transmission. Future windows need designs that both match the shielding performance of surrounding materials and still allow at least 90% of the visible light to be transmitted. Window concepts for new construction and retrofitting proposals for windows in existing buildings, such as a window films, are both of interest.

Full information is available here.

Source: FedBizOpps

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The Intelligence Advanced Research Projects Activity (IARPA) is seeking information on research efforts in the area of innovative, new computer hardware and software architectures with intelligent computer environments. This request for information (RFI) is issued solely for information gathering and planning purposes; it does not constitute a formal solicitation for proposals. The following sections of this announcement contain details on the scope of technical efforts of interest, along with instructions for the submission of responses.

Background

Future computing systems (FCS) should be a revolutionary class of advanced computers with both a new highly-capable architecture and an intelligent computational environment that “understands” its own state (as well as natural input commands), learns new concepts (which are then codified into knowledge), and uses this knowledge to skillfully deliver directed goals.  FCS should enable users to focus on (rather than be distracted from) their applications, and to assist (rather than hinder) them in achieving their goals.

Over the past 60 years, computers have become orders-of-magnitude faster and both more complex and more diverse, but the computational model (i.e., the model for how algorithms/computations are executed) has not substantially changed.  Consequently, the demands on users for system expertise have escalated to prohibitive levels.  Concurrently, the need for real-time (or near-real-time) analysis of massive amounts of heterogeneous data in this new era of explosive data growth has dramatically broadened the application space for advanced computers beyond conventional HPC applications and has compounded the need for ever-increasing computer capacity, capability, response times, and agility.  The current volume and variety of data are already beginning to exceed the ability of today’s most advanced classical systems to deliver optimal solutions.

The time is long overdue for redesigning computers to be smarter and more self-sufficient, while continuing to deliver increasing performance to meet escalating demands. Computers need to be “intelligent systems” capable of assisting humans and other computers in executing extremely complex and data-intensive tasks, as well as of monitoring and maintaining their own operation.  Such systems must be able to assist users in solving not only problems critical to national security and economic prosperity but also equally those that characterize all aspects of modern life.

To accomplish these goals, FCS should have three important new aspects: 1) a knowledge base/inference engine of cognitive and computer system management functions that is integral to system operation at all levels, 2) a machine learning capability that is integrated with the knowledge base to provide a unified “informed learning system” (i.e., the knowledge base guides learning and the newly learned concepts are then codified and incorporated into the knowledge base, and 3) a system design that enables efficient execution of these capabilities while minimizing “time to solution.”  To ensure that the learned knowledge is correct and appropriate, the system for generating and growing the knowledge base system should include a monitoring subsystem that involves human oversight.  The cognitive/management component is a key new feature that must provide comprehensive system-wide capabilities intrinsic and integral to system operation at every level to preclude the system from being blind-sided by partial knowledge or oversights.

Full information is available here.

Source: FedBizOpps

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The Intelligence Advanced Research Projects Activity (IARPA) is seeking information on research efforts into components and capabilities for ultra-low-power chemical sensing devices: collection systems, separation systems, detection/identification, vacuum systems, and power systems. This request for information (RFI) is issued solely for information gathering and planning purposes; this RFI does not constitute a formal solicitation for proposals. The following sections of this announcement contain details of the scope of technical efforts of interest, along with instructions for the submission of responses.

The goal of this RFI is to gather information about component level technology that has been developed inside and outside of the MAEGLIN Phase 1 efforts and to share that information with those interested in submitting proposals for MAEGLIN Phase 2 to facilitate teaming and overall creativity of system approaches.

The information gathered from this RFI will be used in a data package that will be made available to those who 1.) respond to this RFI or 2.) register on IDEAS for the MAEGLIN Phase 2 BAA. Additionally, RFI information will be shared with IARPA’s MAEGLIN mission partners to help them evaluate the current state of the art in components for compact, ultra-low-power chemical sensing systems. Please do not submit any responses that include proprietary or classified information.

Full information is available here.

Source: FedBizOpps

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The Intelligence Advanced Research Projects Activity (IARPA) is seeking information on detection approaches related to marine and coastal biosecurity. This request for information (RFI) is issued solely for information gathering and planning purposes; this RFI does not constitute a formal solicitation for proposals. The following sections of this announcement contain details of the scope of technical efforts of interest, along with instructions for the submission of responses.

Background and Scope

The biosecurity of U.S. marine and coastal areas is a key aspect of the United States National Security Strategy with potential impacts to critical infrastructure, commerce, health, and maritime resources. Increasing global commerce, changing climate conditions, and emerging biological technologies create vulnerabilities that the U.S. needs to be prepared to defend against. New capabilities are necessary to ensure that the U.S. can effectively protect critical economic and national resources.

One key aspect of biosecurity is the purposeful or unintentional introduction of aquatic organisms to non-native areas. Aquatic invasive species (AIS) can be transported by a variety of mechanisms, including ship hulls and ballast water, which provide pathways for microbes, eggs, and small invertebrates to travel and disperse. Control measures and monitoring approaches such as offshore ballast water exchange and ballast water management systems have been developed to reduce the risk of AIS introduction, but significant risk remains.

Methods of monitoring for AIS in coastal waterways and ballast water as well as on ship hulls exist, but current approaches are limited and time consuming. In addition, interpretation of complex biological data in ways that provide actionable information to federal, state, and local agencies can be difficult. Challenges with monitoring are compounded by geographic scale, the diversity of life found within these systems, the limited extent of marine reference data, and changes resulting from environmental conditions such as storms, microclimates, and seasonal changes. Development and advancement of capabilities for the detection of aquatic invasive species is needed to enable early detection of risks to food, water, and economic resources.

IARPA is seeking information regarding robust, cost-effective, and standardizable tools for early detection of AIS in both marine and fresh-water environments, ports and marinas, ballast water, and on ship hulls. Challenges that can be addressed in response to this RFI include the ability to detect/identify a broad number of biological taxa, sample collection and extraction, throughput, and data interpretation.

Full information is available here.

Source: FedBizOpps

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The Intelligence Advanced Research Projects Activity (IARPA) is seeking information on data sources for evaluation of credibility assessment techniques and technologies. This request for information (RFI) is issued solely for information gathering and planning purposes; this RFI does not constitute a formal solicitation for proposals. The following sections of this announcement contain details of the scope of technical efforts of interest, along with instructions for the submission of responses.

Background & Scope

The assessment of a source’s credibility, or their information, is a core challenge for a broad range of intelligence, defense, homeland security, and law enforcement applications. There are a variety of techniques and technologies that are purported to be useful for establishing the credibility of a source, such as an individual, and/or their information, but it is difficult to evaluate and compare the accuracy, sensitivity, specificity, and practical utility of such solutions.  Effective evaluations and comparisons of different credibility assessment methods are hampered by a diversity of testing and demonstration environments, the use of experimental methods that lack construct validity, and other testing conditions that impact the relevance to national security applications, such as poor ecological validity, low stakes or low consequence conditions, and unrepresentative participant cohorts. These concerns are further exacerbated by the potential financial gain for developers of such approaches, which can introduce biased methods and potentially limited reporting about the performance of such systems. Together, these limitations highlight the need for an independent body to motivate and evaluate different credibility assessment techniques and technologies.

To enable such an open and fair evaluation, IARPA seeks data sources that could be compiled and used to independently evaluate and verify the state of the art in credibility assessment techniques and technologies.  For this RFI, credibility refers to the veracity of information and/or the person or source providing that information. Assessments of credibility are often complex and may involve an evaluation of many factors of a source and/or the information, to include, but not limited to, veracity, trustworthiness, motivation, considerations about what may be withheld or concealed, and if credibility of information and/or a source has evolved across environments or over time, such as through transmission (e.g. second or third hand information). This RFI seeks data sources that include an evaluation of one, some, or all of these factors, as well as others that are not listed here, as long as they can be theoretically or empirically linked to an evaluation of source and/or information credibility.

Of critical interest are datasets that can be made publically accessible, or accessible via a data use agreement, and which have objective ground truth about the credibility of the source and/or the information. Data sources of interest include, but are not limited to:

• Experimental data from real or mock/simulated scenarios;

• Data from real world events;

• Information that is exchanged in physical or virtual spaces, either face to face or via other channels (e.g. exclusively text based);

• Information that is delivered directly by the individual whose credibility is being evaluated, or through a proxy or intermediate (e.g. messenger, translator, avatar);

• The credibility of the information and/or source may evolve over time, through transmission, and/or across environments/contexts;

• Video, audio, behavioral, text, physiological, or other data types.

Full information is available here.

Source: FedBizOpps

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Requests for Information are issued to solicit input from the research community on a particular topic.  IARPA is interested in novel, innovative, state-of-the-art capabilities in compact, highly sensitive detectors of special nuclear material for a range of applications. The Detection and Characterization of Special Nuclear Material RFI is issued solely for information gathering and planning purposes and does not constitute a formal solicitation for proposals.  IARPA’s latest RFI closes on December 18, 2017.

IARPA also provides an opportunity to submit proposals not aligned with current programs, based on new ideas from the research community.

Source: IARPA

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The Intelligence Advanced Research Projects Activity (IARPA) is seeking information on research efforts in the area of detection of special nuclear material (SNM). This request for information (RFI) is issued solely for information gathering and planning purposes; this RFI does not constitute a formal solicitation for proposals. The following sections of this announcement contain details of the scope of technical efforts of interest, along with instructions for the submission of responses.

 Background & Scope

Radiation emission serves as a signature for the possible presence of SNM. At present, land-based detection of SNM, and other radioactive sources in commerce, is primarily performed by radiation portal monitors (RPMs). These RPMs are positioned at chokepoints such as border crossings, entrances to facilities, or other areas of interest. For roadside applications, RPMs are generally positioned close to traffic lanes so that individual vehicles can be measured while moving at slow speeds in order to maximize detection sensitivity. In some circumstances, there is the need to perform radiation measurements of vehicles along open roadways without impeding the flow of traffic. In these cases, it is necessary to position detectors farther from traffic lanes and to have sufficient sensitivity to measure vehicles moving at highway speeds.

IARPA is interested in novel, innovative, state-of-the-art (SOTA) capabilities in compact, highly sensitive detectors of SNM for a range of applications. Responses to this RFI can describe both existing detectors and concepts for future detectors. Existing applications or areas of interest to IARPA include, but are not limited to:

• Radiation detection along open roads with vehicles moving at highway speeds

• Cargo inspection at ports of entry

• Detection of SNM radiation sources in urban environments

Full information is available here.

Source: FedBizOpps

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The Intelligence Advanced Research Projects Activity (IARPA) is seeking information on research efforts in the area of sensitive, compact magnetometers. This request for information (RFI) is issued solely for information gathering and planning purposes; this RFI does not constitute a formal solicitation for proposals. The following sections of this announcement contain details of the scope of technical efforts of interest, along with instructions for the submission of responses.

Background & Scope

Compact, sensitive magnetometers can enable or enhance the capabilities of a number of critical needs ranging from nuclear magnetic resonance imaging (NMRI) to precision navigation without GPS. Superconducting Quantum Interferences Devices (SQUIDs) can detect single flux quanta but the cooling requirements for the superconducting materials in the Josephson junctions at the heart of SQUIDs limit their usefulness for many applications. Alternatively, Optically Detected Magnetic Resonance (ODMR) devices do not require cooling and can therefore be extremely compact with sensitivities approaching those of SQUIDs. In addition, some ODMR devices can provide a high-bandwidth and direct measurement of the vector field (as opposed to forming vectors from multiple independent scalar measurements).

IARPA is interested in current state-of-the-art (SOTA) capabilities in compact, highly sensitive magnetometry for a range of applications. Responses to this RFI can describe both existing and planned detector concepts. Existing applications or areas of interest to IARPA include, but are not limited to:
• NMRI
• Nanometer-scale NMRI
• Detection of biological magnetic signals including, for instance, from the brain and heart
• Magnetic field gradiometry for navigation and forensics

This RFI seeks approaches to magnetometry that are:
• Highly sensitive (approaching the single-flux quanta level)
• Highly compact (suitable for manufacturing in arrays)
• Inherently ruggedizable (robust against vibration, temperature shifts, shock, etc.)
• Low power

Full information is available here.

Source: FedBizOpps

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