Algorithmic Jim Crow

This Article contends that current immigration- and security-related vetting protocols risk promulgating an algorithmically driven form of Jim Crow. Under the “separate but equal” discrimination of a historic Jim Crow regime, state laws required mandatory separation and discrimination on the front end, while purportedly establishing equality on the back end. In contrast, an Algorithmic Jim Crow regime allows for “equal but separate” discrimination. Under Algorithmic Jim Crow, equal vetting and database screening of all citizens and noncitizens will make it appear that fairness and equality principles are preserved on the front end. Algorithmic Jim Crow, however, will enable discrimination on the back end in the form of designing, interpreting, and acting upon vetting and screening systems in ways that result in a disparate impact

Authentication of Students and Students’ Work in E-Learning : Report for the Development Bid of Academic Year 2010/11

Global e-learning market is projected to reach $107.3 billion by 2015 according to a new report by The Global Industry Analyst (Analyst 2010). The popularity and growth of the online programmes within the School of Computer Science obviously is in line with this projection. However, also on the rise are students’ dishonesty and cheating in the open and virtual environment of e-learning courses (Shepherd 2008). Institutions offering e-learning programmes are facing the challenges of deterring and detecting these misbehaviours by introducing security mechanisms to the current e-learning platforms. In particular, authenticating that a registered student indeed takes an online assessment, e.g., an exam or a coursework, is essential for the institutions to give the credit to the correct candidate. Authenticating a student is to ensure that a student is indeed who he says he is. Authenticating a student’s work goes one step further to ensure that an authenticated student indeed does the submitted work himself. This report is to investigate and compare current possible techniques and solutions for authenticating distance learning student and/or their work remotely for the elearning programmes. The report also aims to recommend some solutions that fit with UH StudyNet platform.Submitted Versio

Defining Biometrics: Toward a Transnational Ethic of Personal Information

Innovations in biotechnology, computer science, and engineering throughout the late 20th and early 21st centuries dramatically expanded possible modes of data-based surveillance and personal identification. More specifically, new technologies facilitated enormous growth in the biometrics sector. The response to the explosion of biometric technologies was two-fold. While intelligence agencies, militaries, and multinational corporations embraced new opportunities to fortify and expand security measures, many individuals objected to what they perceived as serious threats to privacy and bodily autonomy. These reactions spurred both further technological innovation, and a simultaneous proliferation of hastily drafted policies, laws, and regulations governing the collection, use, and sharing of biometric data. In this paper, I argue that these policies are predicated on a fundamental misunderstanding of the nature of biometric information. Definitions of biometrics presume that “biologicalness” is binary. These definitions also imply, for a number of reasons, that biometric information is more dangerous than other kinds of personal information, therefore requiring stricter regulation. I propose an alternative explanation of biometrics, situating biometric information on a larger spectrum of personal information, rather than in a discrete category of its own. This revised definition of biometrics is necessary to effectively regulate personal information, particularly as the trend of rapid technological growth and change continues. I focus, in particular, on the implications of these findings in a transnational context, where transmission of personal information is largely unregulated, and has significant impact on international relations, security, and individual privacy

TERASENSE: THz device technology laboratory: final summary

The use of THz frequencies, particularly W and G band allows reaching higher resolution and deeper penetration in emerging applications like imaging, sensing, etc. The development of those new applications lays on reliable technologies, background of expertise and know-how. The CDS2008-00068 TERASENSE CONSOLIDER project has given the opportunity to extent upwards in frequency the previous background of the microwaves research group partners. This article summarizes the developments of the TERASENSE work package “THz Device Technology Laboratory”.This work was supported by the Spanish Ministerio de Ciencia e Innovación through the CONSOLIDER-INGENIO 2010 program reference CSD2008-00068 TERASENSE

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure