In the United States, there are more than 35, 000 reported suicides with approximately 1, 800 of them being psychiatric inpatients. Staff perform intermittent or continuous observations in order to prevent such tragedies, but a study of 98 articles over time showed that 20% to 62% of suicides happened while inpatients were on an observation schedule. Reducing the instances of suicides of inpatients is a problem of critical importance to both patients and healthcare providers. In this paper, we introduce SHARE - A Self-Harm Activity Recognition Engine, which attempts to infer self-harming activities from sensing accelerometer data using smart devices worn on a subject's wrist. Preliminary classification accuracy of 80% was achieved using data acquired from 4 subjects performing a series of activities (both self-harming and not). The results, application, and proposed technology platform are discussed in-depth.

In this paper, we investigate the feasibility of leveraging the accelerometer in modern smartphones to detect the triggering of explosion events. By emplacing a static smartphone and a state-of-the-art seismometer in the vicinity of real explosion blasts (conducted at an Explosives Research Lab in a university setting), and comparing their detected event readings, we make several insightful contributions. We find that readings from events in the smartphone and the seismometer are highly correlated in the temporal and frequency domain. We then demonstrate the feasibility of designing an algorithm in the smartphone (executing as an app) to detect the triggering of an explosion based on comparing short term sudden spikes in vibrations due to an explosion event, and long-term dormancy in vibration readings (in the absence of an explosion). To the best of our knowledge, ours is the first work that demonstrates the feasibility of leveraging smartphones for detecting explosion events.

Modeling and characterizing Internet traffic has been a widely studied problem since the conception of the Internet. The self-similar, bursty nature of the traffic has led to a number of conventional statistical models that unfortunately provide relatively weak modeling power. Recently, fractal analysis techniques have emerged to better characterize and model Internet traffic data. However, past research studies have focused on describing and quantifying the fractal nature of Internet traffic on user groups, instead of a single user. In this paper the authors investigate the issue of individual users exhibiting fractal (self-similar behavior) behavior across multiple application types. Using real Internet traffic traces (collected via Net-Flow logs) collected at a college campus for 30 days, our investigations reveal that in a number of application categories (http, chatting, p2p, email etc.) at least one user exhibits long-range correlations typical of fractal behavior. Of the 10 application groups, 7 had over 80% of users demonstrating self-similar behavior with 3 of those groups having > 98%. Potential benefits of our study in the realm of smart health and network security, by reducing the dimensionality of large Internet traffic datasets, are discussed.

Abstract A Sybil attack consists of an adversary assuming multiple identities to defeat the trust of an existing reputation system. When Sybil attacks are launched in vehicular networks, the mobility of vehicles increases the difficulty of identifying the malicious vehicle location. In this paper, a novel protocol for Sybil detection in vehicular networks is presented. Considering that vehicular networks are cyber-physical systems, the technique exploits well grounded results in the physical (i.e., transportation) domain to detect the Sybil attacks in the cyber domain. Compared to existing works that rely on additional cyber hardware support, or complex cryptographic primitives for Sybil detection, the protocol leverages the theory of platoon dispersion that models the physics of naturally occurring vehicle dispersion. Specifically, the proposed technique employs a certain number of roadside units that periodically collect reports from vehicles regarding their physical neighborhood. Leveraging from existing models of platoon dispersion, a protocol was designed to detect anomalously close neighborhoods that are reflective of Sybil attacks. To the best of the authors’ knowledge, this paper is unique in integrating a well established theory in transportation engineering for detecting cyber space attacks in vehicular networks. The resulting protocol is simple, efficient, and robust in diverse attack environments.

Software design and development often presents a high-risk element during the execution of engineering projects due to the difficulty of identifying defects during late stages of development. Many defects identified during the late stages of small spacecraft development can be avoided by constructing interactive, dynamic models. This process is often followed for hardware fabrication/testing but often not to the same extent for software. An alternative to the typical software development process is needed that enables modeling and simulation feedback at early design stages. Petri nets allow for software visualization, simulation, and verification in a cost-effective way. An alternative software modeling approach using Petri nets is presented to rapidly design, develop, and verify/validate small spacecraft software. Using the presented techniques, the Missouri University of Science and Technology Satellite Research Team successfully demonstrated core functionality of their software system with their Missouri-Rolla satellite microsatellite at the final concept review of the U.S. Air Force Research Laboratory's University Nanosat Program's Nanosat-7 Competition.

Stress and Anxiety negatively affect mental health and can lead a number of debilitating impacts to overall health and well being. In the recent past, adolescents are becoming increasingly afflicted with Stress and Anxiety. In this paper, we report our findings on a six-week study of 70 students at a college campus on associations between Stress and Anxiety with respect to Internet usage of students. Using Cisco NetFlow records, on-campus Internet usage of students was collected continuously and unobtrusively in a privacy-preserving manner. Using the Depression Anxiety and Stress Scale (DASS), students were separated based on normal scores and high scores separately for both Anxiety and Stress. Mann-Whitney U-tests revealed that there exists statistically significant differences in the mean values between the groups from the perspective of several Internet usage features. Students with high stress scores exhibit decreased chat (octets, packets, duration), total duration, and streaming duration compared to the students with normal stress scores. Students with high anxiety scores showed an increased mail duration and decreased peer-to-peer duration compared with students with normal anxiety scores. The methods and results of this paper provide a framework for conducting similar studies at universities with the goal of aiding student mental health.

Understanding negative consequences of heavy Internet use on mental health is a topic that is gaining significant traction recently. A number of studies have investigated heavy Internet usage, especially among young adults in relation to online games, social media and email. While such studies do provide valuable insights, Internet usage so far has been characterized by means of self-reported surveys only that may suffer from errors and biases. In this paper, we report the findings of a two month empirical study on heavy Internet usage among students conducted at a college campus. The novelty of the study is that it is believed to be the first to use real Internet data that is collected continuously, passively and preserving privacy. A total of 69 Computer Science freshman students were surveyed for symptoms of heavy Internet usage, using the Internet Related Problem Scale, and their campus Internet usage was monitored (after appropriate anonymization procedures to maintain subject privacy). Statistical analysis revealed that several Internet usage features, such as instant messaging, entropy, gaming, web browsing, peer-to-peer usage, remote usage, and email usage exhibit significant correlations with symptoms of Internet addiction like introversion, craving, loss of control and tolerance. Although the study found that Facebook and Twitter usage did not show significant statistical correlations with symptoms of heavier Internet usage, it was found that students tending towards heavier Internet usage used those websites less. We believe that this study provides critical new insights into symptoms of heavier (possibly addictive) Internet usage among young adults, which is now a topic of significant concern to the mental health community today.

A Sybil attack is one where an adversary assumes multiple identities with the purpose of defeating the trust of an existing reputation system. When Sybil attacks are launched in vehicular networks, an added challenge in detecting malicious nodes is mobility that makes it increasingly difficult to tie a node to the location of attacks. In this paper, we present an innovative protocol for Sybil detection in vehicular networks. Considering that vehicular networks are cyber-physical systems integrating cyber and physical components, our technique exploits well grounded results in the physical (i.e., transportation) domain to tackle the Sybil problem in the cyber domain. Compared to existing works that rely on additional cyber hardware support, or complex cryptographic primitives for Sybil detection, the key innovation in our protocol is leverage the theory of platoon dispersion that models the physics of naturally occurring dispersion in roads. Specifically, our technique employs a certain number of roadside units that periodically collect reports from vehicles regarding their physical neighborhood as they move in roads. Leveraging from existing models of platoon dispersion, we design a protocol to detect anomalously close neighborhoods that are reflective of Sybil attacks. To the best of our knowledge, this is the first work integrating a well established theory in transportation engineering for detecting cyber space attacks in vehicular networks. The resulting protocol is naturally simple, efficient and performs very well.