Lost in Translation? Data Mining, National Security and the Adverse Inference Problem

To the extent that we permit data mining programs to proceed, they must provide adequate due process and redress mechanisms that permit individuals to clear their names. A crucial criteria for such a mechanism is to allow access to information that was used to make adverse assessments so that errors may be corrected. While some information may have to be kept secret for national security purposes, a degree of transparency is needed when individuals are trying to protect their right to travel or access government services free from suspicion. Part II of this essay briefly outlines the government\u27s ability to gain access to private sector data held by commercial entities or third parties. Part III of this essay examines data mining and some of the problems inherent in using data analysis as a predictive tool for terrorism prevention. Part IV of this paper focuses on the specific problem of adverse inferences. This section examines the recent efforts of the federal Transportation Security Administration (TSA) to use data mining in airline passenger profiling. The Computer Assisted Passenger Profiling and Prescreening System II (CAPPS II) as mapped out by the TSA, and the most recent initiative, Secure Flight, illustrate some of the perceived risks inherent in the use of data mining to try and predict whether individuals are a security risk. Part V of this paper explores what efforts Congress and policy makers can make to address the risk of false positives and adverse influences,and the rise of commercial data mining as a favored tool for combating terrorism

Grounding for a computational model of place

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Text printed 2 columns per page.Includes bibliographical references (leaves 66-70).Places are spatial locations that have been given meaning by human experience. The sense of a place is it's support for experiences and the emotional responses associated with them. This sense provides direction and focus for our daily lives. Physical maps and their electronic decedents deconstruct places into discrete data and require user interpretation to reconstruct the original sense of place. Is it possible to create maps that preserve this sense of place and successfully communicate it to the user? This thesis presents a model, and an application upon that model, that captures sense of place for translation, rather then requires the user to recreate it from disparate data. By grounding a human place-sense for machine interpretation, new presentations of space can be presented that more accurately mirror human cognitive conceptions. By using measures of semantic distance a user can observe the proximity of place not only in distance but also by context or association. Applications built upon this model can then construct representations that show places that are similar in feeling or reasonable destinations given the user's current location.(cont.) To accomplish this, the model attempts to understand place in the context a human might by using commonsense reasoning to analyze textual descriptions of place, and implicit statements of support for the role of these places in natural activity. It produces a semantic description of a place in terms of human action and emotion. Representations built upon these descriptions can offer powerful changes in the cognitive processing of space.Matthew Curtis Hockenberry.S.M

Benefits of using marginal opportunistic wildlife behavior data: Constraints and applications across taxa – a dominance hierarchy example relevant for wildlife management

This study is a new approach on collecting, handling and examining wildlife behavior data across mammal species in order to provide new and unique conclusions from efficient data collection schemes. Sophisticated dominance hierarchy patterns and the ability of individual recognition are well described in many large mammals such as monkeys and cetaceans through the effort of detailed long-term studies. Their implications are well known as important topics regarding management strategies, especially for endangered species. However worldwide, for other large mammals, e.g. bears, detailed long-term wildlife behavior studies are virtually not available. This is due to the inaccessibility and inefficient observation abilities for many animal species in the wild, especially long-term studies. Up to now, it is believed that long-term studies are necessary to describe the existence of social structures like dominance hierarchies and individual perception abilities reliably and to present results in a sophisticated ‘significant’ manner. To accomplish more detailed behavior investigations on species where we lack such long-term data, here a new approach to this discipline ‘behavior modeling’ is presented, concentrating on the use of marginal opportunistic samples. This statistical approach has never been conducted to behavior analysis so far. Marginal behavior data for six species were investigated and cSUMMARY.................................................................................................................................. I ZUSAMMENFASSUNG ............................................................................................................ III 1 INTRODUCTION................................................................................................................... 1 1.1 Social Structure - Why it Matters........................................................................................ 1 1.2 Social Structure and Dominance Hierarchy in Higher Mammals ........................................ 4 1.2.1 Overview ................................................................................................................ 4 1.2.2 Howling Monkeys (Alouatta palliata)...................................................................... 5 1.2.3 Humpback Whales (Megaptera novaeangliae)....................................................... 7 1.2.4 Brown Bears (Ursus arctos) ................................................................................... 9 1.2.5 Polar Bears (Ursus maritimus) ............................................................................. 11 1.2.6 Spotted Seals (Phoca largha)............................................................................... 13 1.2.7 Muskoxen (Ovibos moschatus) ............................................................................ 15 1.3 Review of Using Opportunistic and Marginal Data ........................................................... 16 1.4 Data Mining in Behavior Sciences ................................................................................... 18 1.4.1 Overview .............................................................................................................. 18 1.4.2 Why TreeNet........................................................................................................ 20 1.5 Meta-analysis – Overview................................................................................................ 22 1.6 Overall Logic for Approach .............................................................................................. 23 1.7 Justification of Approach.................................................................................................. 24 2 METHODS .......................................................................................................................... 26 2.1 Field work ........................................................................................................................ 29 2.1.1 Howling Monkeys ................................................................................................. 29 2.1.2 Humpback Whales ............................................................................................... 30 2.1.3 Brown Bears ........................................................................................................ 31 2.1.4 Polar Bears .......................................................................................................... 31 2.1.5 Spotted Seals....................................................................................................... 33 2.1.6 Muskoxen............................................................................................................. 33 2.2 Statistic Programs............................................................................................................ 34 2.2.1 Modeling with TreeNet ......................................................................................... 35 2.2.2 Prediction Accuracy of the TreeNet Model ........................................................... 37 2.2.3 Distance Histograms ............................................................................................ 382.2.4 Interaction Diagrams ............................................................................................ 38 2.2.5 Meta-analysis....................................................................................................... 38 3 RESULTS ........................................................................................................................... 39 3.1 Preface ............................................................................................................................ 39 3.1.1 Preface to Modeling with TreeNet ........................................................................ 39 3.1.2 Preface to Prediction Accuracy of the TreeNet Model .......................................... 39 3.1.3 Preface to Distance Histograms ........................................................................... 40 3.1.4 Preface to Interaction Diagrams ........................................................................... 40 3.1.5 Preface to Meta-analysis...................................................................................... 40 3.2 Howling Monkeys............................................................................................................. 41 3.2.1 Modeling with TreeNet ......................................................................................... 41 3.2.2 Prediction Accuracy of the TreeNet Model ........................................................... 43 3.2.3 Distance Histograms ............................................................................................ 44 3.2.4 Interaction Diagrams ............................................................................................ 46 3.2.5 Meta-analysis....................................................................................................... 47 3.3 Humpback Whales........................................................................................................... 49 3.3.1 Modeling with TreeNet ......................................................................................... 49 3.3.2 Prediction Accuracy of the TreeNet Model ........................................................... 51 3.3.3 Distance Histograms ............................................................................................ 52 3.3.4 Interaction Diagrams ............................................................................................ 54 3.3.5 Meta-analysis....................................................................................................... 55 3.4 Brown Bears .................................................................................................................... 57 3.4.1 Modeling with TreeNet ......................................................................................... 57 3.4.2 Prediction Accuracy of the TreeNet Model ........................................................... 59 3.4.3 Distance Histograms ............................................................................................ 59 3.4.4 Interaction Diagrams ............................................................................................ 62 3.4.5 Meta-analysis....................................................................................................... 63 3.5 Polar Bears...................................................................................................................... 65 3.5.1 Modeling with TreeNet ......................................................................................... 65 3.5.2 Prediction Accuracy of the TreeNet Model ........................................................... 67 3.5.3 Distance Histograms ............................................................................................ 67 3.5.4 Interaction Diagrams ............................................................................................ 70 3.5.5 Meta-analysis....................................................................................................... 71 3.6 Spotted Seals .................................................................................................................. 73 3.6.1 Modeling with TreeNet ......................................................................................... 733.6.2 Prediction Accuracy of the TreeNet Model ........................................................... 76 3.6.3 Interaction Diagrams ............................................................................................ 76 3.6.4 Meta-analysis....................................................................................................... 77 3.7 Muskoxen ........................................................................................................................ 79 3.7.1 Modeling with TreeNet ......................................................................................... 79 3.7.2 Prediction Accuracy of the TreeNet Model ........................................................... 81 3.7.3 Distance Histograms ............................................................................................ 82 3.7.4 Interaction Diagrams ............................................................................................ 83 3.7.5 Meta-analysis....................................................................................................... 84 4 DISCUSSION...................................................................................................................... 86 4.1 Social Structures in studied Species................................................................................ 86 4.1.1 Howling Monkeys ................................................................................................. 86 4.1.2 Humpback Whales ............................................................................................... 87 4.1.3 Brown Bears ........................................................................................................ 88 4.1.4 Polar Bears .......................................................................................................... 90 4.1.5 Spotted Seals....................................................................................................... 91 4.1.6 Muskoxen............................................................................................................. 92 4.2 Use of Opportunistic and Marginal Datasets for Evidence and in Behavior Studies ......... 93 4.3 Modeling with TreeNet..................................................................................................... 93 4.4 Meta-analysis .................................................................................................................. 95 4.5 Meaning and Context of Key Findings ............................................................................. 96 4.6 Strength and Weaknesses of Approach........................................................................... 97 4.7 Individual Perception in Bears.......................................................................................... 98 5 OVERALL CONCLUSIONS AND STUDY SUGGESTIONS ............................................... 99 6 ACKNOWLEDGEMENTS ................................................................................................. 100 7 REFERENCES.................................................................................................................. 101 8 APPENDICES................................................................................................................... 112 8.1 Appendix: General Definitions ....................................................................................... 113 8.2 Appendix: Observed Activities ....................................................................................... 115 8.3 Appendix: Interaction Categories ................................................................................... 116 8.3.1 General Interaction Definitions ........................................................................... 116 8.3.2 Species Categorisation ...................................................................................... 1208.4 Appendix: Ethograms .................................................................................................... 124 8.5 Appendix: TreeNet Model Setup .................................................................................... 131 8.6 Appendix: Additonal Result Figures ............................................................................... 132 8.6.1 Howling Monkeys ............................................................................................... 132 8.6.2 Humpback Whales ............................................................................................. 139 8.6.3 Brown Bears ...................................................................................................... 144 8.6.4 Polar Bears ........................................................................................................ 148 8.6.5 Spotted Seals..................................................................................................... 153 8.6.6 Muskoxen – females .......................................................................................... 154 8.6.7 Muskoxen – males ............................................................................................. 157 8.7 Appendix: Example Distance Histograms as expected in non-social Species ................ 158 8.8 Appendix: CD ................................................................................................................ 15