A disaster usually severely harms human health and property. After a disaster, great amount of information of a disaster area is needed urgently. The information not only indicates the severity of the disaster, but also is crucial for an efficient search and rescue process. In order to quickly and accurately collect real time information in a disaster scenario, a mobile platform is developed for an outdoor scenario and a localization and navigation system for responders is introduced for an indoor scenario. The mobile platform has been integrated to the DIORAMA system. It is built with a 6-wheel robot chassis along with an Arduino microcontroller. Controlled by a mounted Android smartphone, the mobile platform can receive commands from incident commanders and quickly respond to the commands. While patrolling in a disaster area, a constant RFID signal is collected to improve the localization accuracy of victims. Pictures and videos are also captured in order to enhance the situational awareness of rescuers. The design of the indoor information collection is focused on the responder side. During a disaster scenario, it is hard to track responders’ locations in an indoor environment. In this thesis, an indoor localization and navigation system based on Bluetooth low energy and Android is developed for helping responders report current location and quickly find the right path in the environment. Different localization algorithms are investigated and implemented. A navigation system based on A­* is also proposed.

For historical reasons, implementers of genetic algorithms often use a haploid binary primitive type for chromosome encoding. I will demonstrate that one can reduce development effort and achieve higher fitness by designing a genetic algorithm with an encoding scheme that closely matches the problem space. I will show that implicit parallelism does not result in binary encoded chromosomes obtaining higher fitness scores than other encodings. I will also show that Hamming distances should be understood as part of the relationship between the closeness of an encoding to the problem instead of assuming they should always be held constant. Closeness to the problem includes leveraging structures that are intended to model a specific aspect of the environment. I will show that diploid chromosomes leverage abeyance to benefit their adaptability in dynamic environments. Finally, I will show that if not all of the parts of the GA are close to the problem, the benefits of the parts that are can be negated by the parts that are not

IEEE-CS is pursuing licensing for software engineers, but ACM believes that software engineering is too immature, and regulating the profession would be premature. In 1996, Norman Gibbs and Gary Ford from Carnegie Mellon University performed a qualitative survey of the maturity of the software engineering profession. I apply this model to the present state of the art in software engineering and analyze the results for 2010. I analyze the maturity of software engineering to determine that the profession is not yet ready for licensure. This is not because the infrastructure of software engineering is too immature, but because we have failed to establish an appropriate body of knowledge for software engineers. I also show that once an appropriate body of knowledge is established, licensure will be an appropriate next step and will open the way for the profession to fully mature.

Code clones are duplicate fragments of code that perform the same task. As software code bases increase in size, the number of code clones also tends to increase. These code clones, possibly created through copy-and-paste methods or unintentional duplication of effort, increase maintenance cost over the lifespan of the software. Code clone detection tools exist to identify clones where a human search would prove unfeasible, however the quality of the clones found may vary. I demonstrate that the performance of such tools can be improved by normalizing the source code before usage. I developed Normalizer, a tool to transform C source code to normalized source code where the code is written as consistently as possible. By maintaining the code's function while enforcing a strict format, the variability of the programmer's style will be taken out. Thus, code clones may be easier to detect by tools regardless of how it was written. Reordering statements, removing useless code, and renaming identifiers are used to achieve normalized code. Normalizer was used to show that more clones can be found in Introduction to Computer Networks assignments by normalizing the source code versus the original source code using a small variety of code clone detection tools.

Data Analytics and technology have changed baseball as we know it. From the increase in defensive shifts to teams using cameras in the outfield to steal signs, teams will try anything to win. One way to gain an edge in baseball is to figure out what pitches a pitcher will pitch. Pitch prediction is a popular task to try to accomplish with all the data that baseball provides. Most methods involve using situational data like the ball and strike count. In this paper, we try a different method of predicting pitch type by only looking at the pitcher's pose in the set position. We do this to find a pitcher's tell or "tip". In baseball, if a pitcher is tipping their pitches, they are doing something that gives away what they will pitch. This could be because the pitcher changes the grip on the ball only for some pitches or something as small as a different flex in their wrist. Professional baseball players will study pitchers before they pitch the ball to try to pick up on these tips. If a tip is found, the batters have a significant advantage over the pitcher. Our paper uses pose estimation and object detection to predict the pitch type based on the pitcher's set position before throwing the ball. Given a successful model, we can extract the important features or the potential tip from the data. Then, we can try to predict the pitches ourselves like a batter. We tested this method on three pitchers: Tyler Glasnow, Yu Darvish, and Stephen Strasburg. Our results demonstrate that when we predict pitch type at a 70\% accuracy, we can reasonably extract useful features. However, finding a useful tip from these features still requires manual observation.

Studies show that smartphone thefts are a significant problem in the United States. [30] With many upcoming proposals to decrease the theft-rate of such devices, investigating new techniques for preventing smartphone theft is an important area of research. The prevalence of new biometric identification techniques for smartphones has led some researchers to propose biometric anti-theft measures for such devices, similar to the current fingerprint authentication system for iOS. Gait identification, a relatively recent field of study, seems to be a good fit for anti-theft because of the non-intrusive nature of passive pattern recognition in walking. In this paper, we reproduce and extend a modern gait recognition technique proposed in Cell Phone-Based Biometrics by testing the technique outside of the laboratory on real users under everyday conditions. We propose how this technique can be applied to create an anti-theft system, and we discuss future developments that will be necessary before such research is ready to be implemented in a release-quality product. Because previous studies have also centered around the ability to differentiate between individual users from a group, we will examine the accuracy of identifying whether or not a specific user is currently using a system. The system proposed in this paper shows results as high as 91% for cross-fold accuracy for some users; however, the predictive accuracy for a single day’s results ranged from 0.8% accuracy to 92.9% accuracy, showing an unreliability that makes such a system unlikely to be useful under the pressure of real-world conditions.

User input has taken many forms since the conception of computers. In the past ten years, Tablet PCs have provided a natural writing experience for users with the advent of active pen input. Unfortunately, pen based input has yet to be adopted as an input method by any modern mobile operating system. This thesis investigates the addition of active pen based input to the Android mobile operating system. The Android input framework was evaluated and modified to allow for active pen input events. Since active pens allow for their position to be detected without making contact with the screen, an on-screen pointer was implemented to provide a digital representation of the pen's position. Extensions to the Android Software Development Kit (SDK) were made to expose the additional functionality provided by active pen input to developers. Pen capable hardware was used to test the Android framework and SDK additions and show that active pen input is a viable input method for Android powered devices. Android was chosen because it is open source and therefore available to modify and test on physical hardware. Gingerbread (Android 2.3) was used as the code base for this thesis. All modifications to the Android framework that are detailed in this thesis will be made available online. The goal of this thesis is to explore methods of integrating and exposing active pen input in Android and encourage the implementation and adoption of active pen input by Google as a standard input method in Android.

The growth of web-based malware and phishing attacks has catalyzed significant advances in the research and use of interstitial warning pages and modals by a browser prior to loading the content of a suspect site. These warnings commonly use visual cues to attract users' attention, including specialized iconography, color, and an absence of buttons to communicate the importance of the scenario. While the efficacy of visual techniques has improved safety for sighted users, these techniques are unsuitable for blind and visually impaired users. This is likely not due to a lack of interest or technical capability by browser manufactures, where universal design is a core tenet of their engineering practices, but instead a reflection of the very real dearth of research literature to inform best practices, exacerbated by a deficit of clear methodologies for conducting studies with this population. Indeed, the challenges are manifold. In this paper, we present the results of our study analyzing the experiences of the visually impaired with browser security warnings, detail the development and advancement of the methodological best practices when conducting a study of this kind, and ultimately identify some initial approaches that could improve the security for this population.

In general an ontology is created by following a top-down, or so called genus-species approach, where the species are differentiated from the genus and from each other by means of differentiae [8]. The superconcept is the genus, every subconcept is a species, and the differentiae correspond to roles. To complete it a user organizes data into a proper structure, accompanied with the instances in that domain in order to complete the construction of an ontology. It is a concept learning procedure in a school, for example. Students first learn the general knowledge and apply it to their exercise and homework for practice. After they are more familiar with the knowledge, they can use what they have learned to solve the problems in their daily life. The deductive learning approach is based on the fundamental knowledge that a student has acquired already. By contrast, a more intuitive way of learning is the bottom-up approach, which is based on atomism. That is also a frequently used way for humans to acquire knowledge. From sensing the world by vision, hearing, and touching, people learn information about actual objects, i.e., instances, in the world. After an instance has been collected, a relationship between it and existing knowledge will be created and an ontology will be formed automatically. The primary goal of this thesis is to make a better instance input interface for the ontology development tool Protégé to simplify the procedure of ontology construction. The second goal is to show the feasibility of a bottom-up approach for the building of an ontology. Without setting up the organization of classes and properties (slots) first, a user simply inputs all the information from an instance and the program will form an ontology automatically. It means after an instance has been entered, the system will find a proper location inside of the ontology to store it.

<p>Many algorithms, particularly in the area of image processing, are expensive to develop and computationally resource intensive. We illustrate the advantages of symbolic code generation using an example - closed-loop visual target recognition and tracking in extreme lighting conditions. We quantify the effect of symbolic code generation methods on code efficiency, and explain how these methods allowed us to reduce the development time as well as improve reliability. Working directly with symbolic models improves software quality by reducing transcription errors, and enabled us to rapidly prototype different models for the visual tracking application, where the need to evaluate trackers in their real-time context precludes the effective use of scripting languages. We describe the model in detail, including formulations as an optimization problem; explain the challenges in solving the model; present our method of building the solvers; and summarize the impact on the performance of our methods.</p> / Master of Applied Science (MASc)