Support Vector Machine for Behavior-Based Driver Identification System

We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security application is more effective. In this paper, we first describe our experimental platform for collecting and modeling human driving behaviors. Then we compare fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) for data preprocessing. Using machine learning method of support vector machine (SVM), we derive the individual driving behavior model and we then demonstrate the procedure for recognizing different drivers by analyzing the corresponding models. The experimental results of learning algorithms and evaluation are described

MACHINE LEARNING APPROACH FOR AVOIDING RAPE

Effective self-protective behaviors, such as victim's physical resistance for avoiding sexual victimization have been studied. However, effective self-protective behavioral sequences, such as offender's physical violence followed by victim's physical resistance, have not been studied often. Our study aims to clarify these sequences through supervised machine learning approach. The samples consisted of 88 official documents on sexual crimes regarding women committed by male offenders incarcerated in a Japanese local prison. The crimes were classified as completed or attempted cases based on judges’ evaluation. All phrases in each crime description were also partitioned and coded according to the Japanese Penal Code. The Support Vector Machine learned the most likely sequences of behaviors to predict completed and attempted cases. Around 90% of cases were correctly predicted through the identification of sequences of behaviors. The sequence involving the offender’s violence followed by victim’s physical resistance predicted attempted sexual crime. However, the sequence involving victim’s general resistance followed by the offender’s violence predicted completed sexual crime. Timing of victim’s resistance and offender’s violence could affect potential avoidance of sexual victimization

MARITIME DOMAIN AWARENESS THROUGH THE CHARACTERIZATION OF SHIP BEHAVIOR WITH AIS DATA

Maritime Domain Awareness (MDA), as defined in the 2005 National Strategy for Maritime Security, is the “effective understanding of anything associated with the global maritime domain that could impact the security, safety, economy, or environment of the United States.” Thus, it is imperative for the U.S. Navy to develop approaches that enhance understanding of the maritime domain in order to maintain operational effectiveness. One such way to enhance this understanding is to develop approaches that automate the analysis of Automatic Identification System (AIS) data to characterize the behavior of ships in the maritime domain. By the sheer amount of AIS data available, it quickly becomes challenging for a human operator to identify ship behaviors throughout the world. When timeliness is important for decision makers, it becomes even more important that the characterization of ship behavior is done quickly and accurately to identify potential issues or threats. Thus, a major contribution of this thesis is the development of an autonomous machine learning system that characterizes ship behavior quickly and accurately in order to achieve MDA in a particular environment. This includes an autonomous system for the identification of ship tracks in a region. Two major contributions of this work are the development of a taxonomy of ship behaviors, which is currently lacking in the literature, and a report on the characterization of such behaviors through machine learning methods.Ensign, United States NavyApproved for public release. Distribution is unlimited

Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks

The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured. In this work, we look into how private information can be leaked from network traffic generated in the smart home network. Although researchers have proposed techniques to infer IoT device types or user behaviors under clean experiment setup, the effectiveness of such approaches become questionable in the complex but realistic network environment, where common techniques like Network Address and Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic analysis using traditional methods (e.g., through classical machine-learning models) is much less effective under those settings, as the features picked manually are not distinctive any more. In this work, we propose a traffic analysis framework based on sequence-learning techniques like LSTM and leveraged the temporal relations between packets for the attack of device identification. We evaluated it under different environment settings (e.g., pure-IoT and noisy environment with multiple non-IoT devices). The results showed our framework was able to differentiate device types with a high accuracy. This result suggests IoT network communications pose prominent challenges to users' privacy, even when they are protected by encryption and morphed by the network gateway. As such, new privacy protection methods on IoT traffic need to be developed towards mitigating this new issue

A descriptive study of two small peer-directed mathematics groups in an elementary classroom.

The purpose of this study was to describe the behavior of children engaged in two different Peer Work Group (PWG) tasks and to search for patterns of behavior that relate to learning. The study was exploratory in nature and was designed to investigate the processes children use under different PWG task-structure conditions. Two groups of children in a 1st-2nd grade classroom were studied; each group worked for one week on each task and all interaction was videotaped. Detailed information about requests and responses was recorded onto a checklist. Pretests and posttests were administered for each task to assess gains and to search for relationships among tasks, behaviors, and learning. Results include identification of eleven task-related behaviors with differences across tasks in level of engagement for the following: Independent Seatwork, Group Discussion, Time Off-Task, Waiting for Peers, Cooperative Problem Solving, Approaching the Teacher, and Requesting Help. Patterns in the data for request-response behaviors agree with sociolinguistic theory regarding effective speakers . Significant differences were not found within or between groups and tasks on achievement measures. Implications are drawn regarding the influence of task structure on group process and children\u27s use of requesting behavior for obtaining elaborated responses from peers

Building the Infrastructure: The Effects of Role Identification Behaviors on Team Cognition Development and Performance

The primary purpose of this study was to extend theory and research regarding the emergence of mental models and transactive memory in teams. Utilizing Kozlowski et al.’s (1999) model of team compilation, we examine the effect of role identification behaviors and argue that such behaviors represent the initial building blocks of team cognition during the role compilation phase of team development. We then hypothesized that team mental models and transactive memory would convey the effects of these behaviors onto team performance in the team compilation phase of development. Results from 60 teams working on a command and control simulation supported our hypotheses

Designing Adaptive Instruction for Teams: a Meta-Analysis

The goal of this research was the development of a practical architecture for the computer-based tutoring of teams. This article examines the relationship of team behaviors as antecedents to successful team performance and learning during adaptive instruction guided by Intelligent Tutoring Systems (ITSs). Adaptive instruction is a training or educational experience tailored by artificially-intelligent, computer-based tutors with the goal of optimizing learner outcomes (e.g., knowledge and skill acquisition, performance, enhanced retention, accelerated learning, or transfer of skills from instructional environments to work environments). The core contribution of this research was the identification of behavioral markers associated with the antecedents of team performance and learning thus enabling the development and refinement of teamwork models in ITS architectures. Teamwork focuses on the coordination, cooperation, and communication among individuals to achieve a shared goal. For ITSs to optimally tailor team instruction, tutors must have key insights about both the team and the learners on that team. To aid the modeling of teams, we examined the literature to evaluate the relationship of teamwork behaviors (e.g., communication, cooperation, coordination, cognition, leadership/coaching, and conflict) with team outcomes (learning, performance, satisfaction, and viability) as part of a large-scale meta-analysis of the ITS, team training, and team performance literature. While ITSs have been used infrequently to instruct teams, the goal of this meta-analysis make team tutoring more ubiquitous by: identifying significant relationships between team behaviors and effective performance and learning outcomes; developing instructional guidelines for team tutoring based on these relationships; and applying these team tutoring guidelines to the Generalized Intelligent Framework for Tutoring (GIFT), an open source architecture for authoring, delivering, managing, and evaluating adaptive instructional tools and methods. In doing this, we have designed a domain-independent framework for the adaptive instruction of teams

Childhood anxiety: how schools identify, assess, provide resources to and refer students with anxiety

Includes bibliographical references