Selected Computing Research Papers Volume 2 June 2013

An Evaluation of Current Innovations for Solving Hard Disk Drive Vibration Problems (Isiaq Adeola) ........................................................................................................ 1 A Critical Evaluation of the Current User Interface Systems Used By the Blind and Visually Impaired (Amneet Ahluwalia) ................................................................................ 7 Current Research Aimed At Improving Bot Detection In Massive Multiplayer Online Games (Jamie Burnip) ........................................................................................................ 13 Evaluation Of Methods For Improving Network Security Against SIP Based DoS Attacks On VoIP Network Infrastructures (David Carney) ................................................ 21 An Evaluation of Current Database Encryption Security Research (Ohale Chidiebere) .... 29 A Critical Appreciation of Current SQL Injection Detection Methods (Lee David Glynn) .............................................................................................................. 37 An Analysis of Current Research into Music Piracy Prevention (Steven Hodgson) .......... 43 Real Time On-line Analytical Processing: Applicability Of Parallel Processing Techniques (Kushatha Kelebeng) ....................................................................................... 49 Evaluating Authentication And Authorisation Method Implementations To Create A More Secure System Within Cloud Computing Technologies (Josh Mallery) ................... 55 A Detailed Analysis Of Current Computing Research Aimed At Improving Facial Recognition Systems (Gary Adam Morrissey) ................................................................... 61 A Critical Analysis Of Current Research Into Stock Market Forecasting Using Artificial Neural Networks (Chris Olsen) ........................................................................... 69 Evaluation of User Authentication Schemes (Sukhdev Singh) .......................................... 77 An Evaluation of Biometric Security Methods for Use on Mobile Devices (Joe van de Bilt) .................................................................................................................. 8

The Show Must Go On: New York DIY as Cultural Practice in The Changing City

The New York DIY scene is situated in a long history of social, political, and musical movements across the nation. Coming from this tradition of “do it yourself” politics, this community has adapted over the years to combat the forces of gentrification and capitalism which constantly threaten DIY music scenes. Over the last two years, this community has faced one of the most unexpected challenges to the continuation of the scene; The COVID-19 pandemic. In this ethnographically inflected analysis of the New York City DIY scene, I will be looking at the ways in which this musical community has been affected by the pandemic and gentrification. Furthermore, through an ethnographic investigation of how DIY has persisted throughout the pandemic, I will be looking at what this might mean for the creation and continuation of this youth subculture. Theoretical and historical conceptualizations of gentrification are applied to these encounters to analyze the sonic qualities of gentrification, as well as what it means to people in the scene to be from New York. In applying a historical framework, this study also looks at the cyclical nature of youth reactionary politics, as well as consumer capitalism’s ability to co-opt aesthetics of youth counterculture. A transgender studies lens is also applied to DIY, as a way to understand how this community defines what DIY is, and locates the act of “doing” as the practice which counters the sounds of gentrification in the scene. Through this hybrid of ethnographic, theoretical and historical analysis, I aim to situate the New York DIY scene in traditions of DIY and punk aesthetics in order to think about what is possible for the future of this community in the face of adversity

Who wrote this scientific text?

The IEEE bibliographic database contains a number of proven duplications with indication of the original paper(s) copied. This corpus is used to test a method for the detection of hidden intertextuality (commonly named "plagiarism"). The intertextual distance, combined with the sliding window and with various classification techniques, identifies these duplications with a very low risk of error. These experiments also show that several factors blur the identity of the scientific author, including variable group authorship and the high levels of intertextuality accepted, and sometimes desired, in scientific papers on the same topic

Electronic Dance Music: From Deviant Subculture to Culture Industry

Utilizing a mixed qualitative research methods design, including interviews, ethnographic field work, and analyzing historical documents, I examine the Electronic Dance Music (EDM) subculture, since its inception in the late 80s as a deviant subculture. Previous studies of raves and other music subcultures focus almost exclusively on the role of popularity in transforming the subculture. In this research, I found, in the case of EDM at least, a more complicated process in which structural factors such as mass media, public officials (politicians and law enforcement), and major music corporations played a prominent role in the transformation. Media coverage focused on sensationalized cases of widespread drug use, while public officials responded by passing and enforcing legislation that forced EDM organizers into more legitimate venues. This change in venue brought them to the attention of the music industry, who saw a new opportunity to make money. By “rationalizing” the production, distribution, and consumption (especially via corporate advertising) of electronic dance music for profit, the original subcultural, even countercultural, values of PLUR (peace, love, unity, respect), solidarity, and authenticity were undermined. These changes resulted in the group being transformed into what Horkheimer and Adorno ([1944] 1972) called a culture industry. Electronic dance music is today dominated by large-scale entertainment corporations who employ a formulaic marketing strategy in the production of EDM events for a mass audience

Human-AI complex task planning

The process of complex task planning is ubiquitous and arises in a variety of compelling applications. A few leading examples include designing a personalized course plan or trip plan, designing music playlists/work sessions in web applications, or even planning routes of naval assets to collaboratively discover an unknown destination. For all of these aforementioned applications, creating a plan requires satisfying a basic construct, i.e., composing a sequence of sub-tasks (or items) that optimizes several criteria and satisfies constraints. For instance, in course planning, sub-tasks or items are core and elective courses, and degree requirements capture their complex dependencies as constraints. In trip planning, sub-tasks are points of interest (POIs) and constraints represent time and monetary budget, or user-specified requirements. Needless to say, task plans are to be individualized and designed considering uncertainty. When done manually, the process is human-intensive and tedious, and unlikely to scale. The goal of this dissertation is to present computational frameworks that synthesize the capabilities of human and AI algorithms to enable task planning at scale while satisfying multiple objectives and complex constraints. This dissertation makes significant contributions in four main areas, (i) proposing novel models, (ii) designing principled scalable algorithms, (iii) conducting rigorous experimental analysis, and (iv) deploying designed solutions in the real-world. A suite of constrained and multi-objective optimization problems has been formalized, with a focus on their applicability across diverse domains. From an algorithmic perspective, the dissertation proposes principled algorithms with theoretical guarantees adapted from discrete optimization techniques, as well as Reinforcement Learning based solutions. The memory and computational efficiency of these algorithms have been studied, and optimization opportunities have been proposed. The designed solutions are extensively evaluated on various large-scale real-world and synthetic datasets and compared against multiple baseline solutions after appropriate adaptation. This dissertation also presents user study results involving human subjects to validate the effectiveness of the proposed models. Lastly, a notable outcome of this dissertation is the deployment of one of the developed solutions at the Naval Postgraduate School. This deployment enables simultaneous route planning for multiple assets that are robust to uncertainty under multiple contexts