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GraphChallenge.org triangle counting performance [e-print]

Summary

The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems. GraphChallenge.org provides a wide range of preparsed graph data sets, graph generators, mathematically defined graph algorithms, example serial implementations in a variety of languages, and specific metrics for measuring performance. The triangle counting component of GraphChallenge.org tests the performance of graph processing systems to count all the triangles in a graph and exercises key graph operations found in many graph algorithms. In 2017, 2018, and 2019 many triangle counting submissions were received from a wide range of authors and organizations. This paper presents a performance analysis of the best performers of these submissions. These submissions show that their state-of-the-art triangle counting execution time, Ttri, is a strong function of the number of edges in the graph, Ne, which improved significantly from 2017 (Ttri \approx (Ne/10^8)^4=3) to 2018 (Ttri \approx Ne/10^9) and remained comparable from 2018 to 2019. Graph Challenge provides a clear picture of current graph analysis systems and underscores the need for new innovations to achieve high performance on very large graphs
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Summary

The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems...

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AI enabling technologies: a survey

Summary

Artificial Intelligence (AI) has the opportunity to revolutionize the way the United States Department of Defense (DoD) and Intelligence Community (IC) address the challenges of evolving threats, data deluge, and rapid courses of action. Developing an end-to-end artificial intelligence system involves parallel development of different pieces that must work together in order to provide capabilities that can be used by decision makers, warfighters and analysts. These pieces include data collection, data conditioning, algorithms, computing, robust artificial intelligence, and human-machine teaming. While much of the popular press today surrounds advances in algorithms and computing, most modern AI systems leverage advances across numerous different fields. Further, while certain components may not be as visible to end-users as others, our experience has shown that each of these interrelated components play a major role in the success or failure of an AI system. This article is meant to highlight many of these technologies that are involved in an end-to-end AI system. The goal of this article is to provide readers with an overview of terminology, technical details and recent highlights from academia, industry and government. Where possible, we indicate relevant resources that can be used for further reading and understanding.
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Summary

Artificial Intelligence (AI) has the opportunity to revolutionize the way the United States Department of Defense (DoD) and Intelligence Community (IC) address the challenges of evolving threats, data deluge, and rapid courses of action. Developing an end-to-end artificial intelligence system involves parallel development of different pieces that must work together...

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Survey of data fusion in IoT

Published in:
MIT Lincoln Laboratory Report TR-1239

Summary

With the advent of the Internet--and, in particular, the World Wide Web--came a massive increase in the amount of data we as a society create on a daily basis. The more recent proliferation of mobile devices and online social networking has increased the amount of user-generated content to an enormous level. The current spread of Internet of Things (IoT) devices is causing an even greater acceleration, with the number of IoT devices online already outnumbering the number of people in the world. Within all of this data is information of potential value to many missions of the U.S. government. While IoT devices are plentiful, they tend to be inexpensive and are sometimes unreliable in their measurements. A prudent consumer of IoT data would be skeptical of any information gleaned from a single IoT sensor. This, however, is where the sheer volume of IoT devices in the world can provide a substantial benefit. The low cost of IoT sensors may yield relatively low performance, but also makes it feasible to collect large amounts of data over a sea of devices. While, for example, a single traffic sensor may frequently malfunction, fusing data across a large number of sensors improves robustness to unreliable measurements from any given device. A diverse array of sensor types, application areas, and data fusion methodologies form a large body of work in the academic literature. This report provides a survey of the recent literature on fusion techniques for IoT data, with an eye toward methods that may be interesting for U.S. government analysts, enabling them to augment their data most effectively and provide the highest possible force multiplier for their analysis products. The remainder of this report is organized as follows. Section 2 provides a brief background on the Internet of Things. Section 3 formally states the objective of the current study. The findings from several literature surveys are summarized in Section 4. Section 5 highlights recent results in relevant application areas. Finally, in order to get a set of findings that can be compared to each other, Section 6 outlines results in a particular application: indoor positioning. Section 7 provides a discussion of the implications of the current literature on future research and development. Section 8 summarizes and concludes the report.
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Summary

With the advent of the Internet--and, in particular, the World Wide Web--came a massive increase in the amount of data we as a society create on a daily basis. The more recent proliferation of mobile devices and online social networking has increased the amount of user-generated content to an enormous...

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Artificial intelligence: short history, present developments, and future outlook, final report

Summary

The Director's Office at MIT Lincoln Laboratory (MIT LL) requested a comprehensive study on artificial intelligence (AI) focusing on present applications and future science and technology (S&T) opportunities in the Cyber Security and Information Sciences Division (Division 5). This report elaborates on the main results from the study. Since the AI field is evolving so rapidly, the study scope was to look at the recent past and ongoing developments to lead to a set of findings and recommendations. It was important to begin with a short AI history and a lay-of-the-land on representative developments across the Department of Defense (DoD), intelligence communities (IC), and Homeland Security. These areas are addressed in more detail within the report. A main deliverable from the study was to formulate an end-to-end AI canonical architecture that was suitable for a range of applications. The AI canonical architecture, formulated in the study, serves as the guiding framework for all the sections in this report. Even though the study primarily focused on cyber security and information sciences, the enabling technologies are broadly applicable to many other areas. Therefore, we dedicate a full section on enabling technologies in Section 3. The discussion on enabling technologies helps the reader clarify the distinction among AI, machine learning algorithms, and specific techniques to make an end-to-end AI system viable. In order to understand what is the lay-of-the-land in AI, study participants performed a fairly wide reach within MIT LL and external to the Laboratory (government, commercial companies, defense industrial base, peers, academia, and AI centers). In addition to the study participants (shown in the next section under acknowledgements), we also assembled an internal review team (IRT). The IRT was extremely helpful in providing feedback and in helping with the formulation of the study briefings, as we transitioned from datagathering mode to the study synthesis. The format followed throughout the study was to highlight relevant content that substantiates the study findings, and identify a set of recommendations. An important finding is the significant AI investment by the so-called "big 6" commercial companies. These major commercial companies are Google, Amazon, Facebook, Microsoft, Apple, and IBM. They dominate in the AI ecosystem research and development (R&D) investments within the U.S. According to a recent McKinsey Global Institute report, cumulative R&D investment in AI amounts to about $30 billion per year. This amount is substantially higher than the R&D investment within the DoD, IC, and Homeland Security. Therefore, the DoD will need to be very strategic about investing where needed, while at the same time leveraging the technologies already developed and available from a wide range of commercial applications. As we will discuss in Section 1 as part of the AI history, MIT LL has been instrumental in developing advanced AI capabilities. For example, MIT LL has a long history in the development of human language technologies (HLT) by successfully applying machine learning algorithms to difficult problems in speech recognition, machine translation, and speech understanding. Section 4 elaborates on prior applications of these technologies, as well as newer applications in the context of multi-modalities (e.g., speech, text, images, and video). An end-to-end AI system is very well suited to enhancing the capabilities of human language analysis. Section 5 discusses AI's nascent role in cyber security. There have been cases where AI has already provided important benefits. However, much more research is needed in both the application of AI to cyber security and the associated vulnerability to the so-called adversarial AI. Adversarial AI is an area very critical to the DoD, IC, and Homeland Security, where malicious adversaries can disrupt AI systems and make them untrusted in operational environments. This report concludes with specific recommendations by formulating the way forward for Division 5 and a discussion of S&T challenges and opportunities. The S&T challenges and opportunities are centered on the key elements of the AI canonical architecture to strengthen the AI capabilities across the DoD, IC, and Homeland Security in support of national security.
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Summary

The Director's Office at MIT Lincoln Laboratory (MIT LL) requested a comprehensive study on artificial intelligence (AI) focusing on present applications and future science and technology (S&T) opportunities in the Cyber Security and Information Sciences Division (Division 5). This report elaborates on the main results from the study. Since the...

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Simulation approach to sensor placement using Unity3D

Summary

3D game simulation engines have demonstrated utility in the areas of training, scientific analysis, and knowledge solicitation. This paper will make the case for the use of 3D game simulation engines in the field of sensor placement optimization. Our study used a series of parallel simulations in the Unity3D simulation framework to answer the questions: how many sensors of various modalities are required and where they should be placed to meet a desired threat detection threshold? The result is a framework that not only answers this sensor placement question, but can be easily expanded to differing optimization criteria as well as answer how a particular configuration responds to differing crowd flows or informed/non-informed adversaries. Additionally, we demonstrate the scalability of this framework by running parallel instances on a supercomputing grid and illustrate the processing speed gained.
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Summary

3D game simulation engines have demonstrated utility in the areas of training, scientific analysis, and knowledge solicitation. This paper will make the case for the use of 3D game simulation engines in the field of sensor placement optimization. Our study used a series of parallel simulations in the Unity3D simulation...

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Cloud computing in tactical environments

Summary

Ground personnel at the tactical edge often lack data and analytics that would increase their effectiveness. To address this problem, this work investigates methods to deploy cloud computing capabilities in tactical environments. Our approach is to identify representative applications and to design a system that spans the software/hardware stack to support such applications while optimizing the use of scarce resources. This paper presents our high-level design and the results of initial experiments that indicate the validity of our approach.
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Summary

Ground personnel at the tactical edge often lack data and analytics that would increase their effectiveness. To address this problem, this work investigates methods to deploy cloud computing capabilities in tactical environments. Our approach is to identify representative applications and to design a system that spans the software/hardware stack to...

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SIAM data mining "brings it" to annual meeting

Summary

The Data Mining Activity Group is one of SIAM's most vibrant and dynamic activity groups. To better share our enthusiasm for data mining with the broader SIAM community, our activity group organized six minisymposia at the 2016 Annual Meeting. These minisymposia included 48 talks organized by 11 SIAM members.
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Summary

The Data Mining Activity Group is one of SIAM's most vibrant and dynamic activity groups. To better share our enthusiasm for data mining with the broader SIAM community, our activity group organized six minisymposia at the 2016 Annual Meeting. These minisymposia included 48 talks organized by 11 SIAM members.

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Very large graphs for information extraction (VLG) - detection and inference in the presence of uncertainty

Summary

In numerous application domains relevant to the Department of Defense and the Intelligence Community, data of interest take the form of entities and the relationships between them, and these data are commonly represented as graphs. Under the Very Large Graphs for Information Extraction effort--a one year proof-of-concept study--MIT LL developed novel techniques for anomalous subgraph detection, building on tools in the signal processing research literature. This report documents the technical results of this effort. Two datasets--a snapshot of Thompson Reuters' Web of Science database and a stream of web proxy logs--were parsed, and graphs were constructed from the raw data. From the phenomena in these datasets, several algorithms were developed to model the dynamic graph behavior, including a preferential attachment mechanism with memory, a streaming filter to model a graph as a weighted average of its past connections, and a generalized linear model for graphs where connection probabilities are determined by additional side information or metadata. A set of metrics was also constructed to facilitate comparison of techniques. The study culminated in a demonstration of the algorithms on the datasets of interest, in addition to simulated data. Performance in terms of detection, estimation, and computational burden was measured according to the metrics. Among the highlights of this demonstration were the detection of emerging coauthor clusters in the Web of Science data, detection of botnet activity in the web proxy data after 15 minutes (which took 10 days to detect using state-of-the-practice techniques), and demonstration of the core algorithm on a simulated 1-billion-vertex graph using a commodity computing cluster.
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Summary

In numerous application domains relevant to the Department of Defense and the Intelligence Community, data of interest take the form of entities and the relationships between them, and these data are commonly represented as graphs. Under the Very Large Graphs for Information Extraction effort--a one year proof-of-concept study--MIT LL developed...

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Time delay integration and in-pixel spatiotemporal filtering using a nanoscale digital CMOS focal plane readout

Summary

A digital focal plane array (DFPA) architecture has been developed that incorporates per-pixel full-dynamic-range analog-to-digital conversion and orthogonal-transfer-based realtime digital signal processing capability. Several long-wave infrared-optimized pixel processing focal plane readout integrated circuit (ROIC) designs have been implemented, each accommodating a 256 x 256 30-um-pitch detector array. Demonstrated in this paper is the application of this DFPA ROIC architecture to problems of background pedestal mitigation, wide-field imaging, image stabilization, edge detection, and velocimetry. The DFPA architecture is reviewed, and pixel performance metrics are discussed in the context of the application examples. The measured data reported here are for DFPA ROICs implemented in 90-nm CMOS technology and hybridized to HgxCd1-xTe (MCT) detector arrays with cutoff wavelengths ranging from 7 to 14.5 m and a specified operating temperature of 60 K-80 K.
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Summary

A digital focal plane array (DFPA) architecture has been developed that incorporates per-pixel full-dynamic-range analog-to-digital conversion and orthogonal-transfer-based realtime digital signal processing capability. Several long-wave infrared-optimized pixel processing focal plane readout integrated circuit (ROIC) designs have been implemented, each accommodating a 256 x 256 30-um-pitch detector array. Demonstrated in this...

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