166 research outputs found
Enhancing Online Security with Image-based Captchas
Given the data loss, productivity, and financial risks posed by security breaches, there is a great need to protect online systems from automated attacks. Completely Automated Public Turing Tests to Tell Computers and Humans Apart, known as CAPTCHAs, are commonly used as one layer in providing online security. These tests are intended to be easily solvable by legitimate human users while being challenging for automated attackers to successfully complete. Traditionally, CAPTCHAs have asked users to perform tasks based on text recognition or categorization of discrete images to prove whether or not they are legitimate human users. Over time, the efficacy of these CAPTCHAs has been eroded by improved optical character recognition, image classification, and machine learning techniques that can accurately solve many CAPTCHAs at rates approaching those of humans. These CAPTCHAs can also be difficult to complete using the touch-based input methods found on widely used tablets and smartphones.;This research proposes the design of CAPTCHAs that address the shortcomings of existing implementations. These CAPTCHAs require users to perform different image-based tasks including face detection, face recognition, multimodal biometrics recognition, and object recognition to prove they are human. These are tasks that humans excel at but which remain difficult for computers to complete successfully. They can also be readily performed using click- or touch-based input methods, facilitating their use on both traditional computers and mobile devices.;Several strategies are utilized by the CAPTCHAs developed in this research to enable high human success rates while ensuring negligible automated attack success rates. One such technique, used by fgCAPTCHA, employs image quality metrics and face detection algorithms to calculate a fitness value representing the simulated performance of human users and automated attackers, respectively, at solving each generated CAPTCHA image. A genetic learning algorithm uses these fitness values to determine customized generation parameters for each CAPTCHA image. Other approaches, including gradient descent learning, artificial immune systems, and multi-stage performance-based filtering processes, are also proposed in this research to optimize the generated CAPTCHA images.;An extensive RESTful web service-based evaluation platform was developed to facilitate the testing and analysis of the CAPTCHAs developed in this research. Users recorded over 180,000 attempts at solving these CAPTCHAs using a variety of devices. The results show the designs created in this research offer high human success rates, up to 94.6\% in the case of aiCAPTCHA, while ensuring resilience against automated attacks
RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques
Construction waste disposal is an urgent issue
for protecting our environment. This paper proposes a
waste management system and illustrates the work
process using plasterboard waste as an example, which
creates a hazardous gas when land filled with household
waste, and for which the recycling rate is less than 10%
in the UK. The proposed system integrates RFID
technology, Rule-Based Reasoning, Ant Colony
optimization and knowledge technology for auditing
and tracking plasterboard waste, guiding the operation
staff, arranging vehicles, schedule planning, and also
provides evidence to verify its disposal. It h relies on
RFID equipment for collecting logistical data and uses
digital imaging equipment to give further evidence; the
reasoning core in the third layer is responsible for
generating schedules and route plans and guidance, and
the last layer delivers the result to inform users. The
paper firstly introduces the current plasterboard
disposal situation and addresses the logistical problem
that is now the main barrier to a higher recycling rate,
followed by discussion of the proposed system in terms
of both system level structure and process structure.
And finally, an example scenario will be given to
illustrate the system’s utilization
Low-latency Networking: Where Latency Lurks and How to Tame It
While the current generation of mobile and fixed communication networks has
been standardized for mobile broadband services, the next generation is driven
by the vision of the Internet of Things and mission critical communication
services requiring latency in the order of milliseconds or sub-milliseconds.
However, these new stringent requirements have a large technical impact on the
design of all layers of the communication protocol stack. The cross layer
interactions are complex due to the multiple design principles and technologies
that contribute to the layers' design and fundamental performance limitations.
We will be able to develop low-latency networks only if we address the problem
of these complex interactions from the new point of view of sub-milliseconds
latency. In this article, we propose a holistic analysis and classification of
the main design principles and enabling technologies that will make it possible
to deploy low-latency wireless communication networks. We argue that these
design principles and enabling technologies must be carefully orchestrated to
meet the stringent requirements and to manage the inherent trade-offs between
low latency and traditional performance metrics. We also review currently
ongoing standardization activities in prominent standards associations, and
discuss open problems for future research
New Statistical Algorithms for the Analysis of Mass Spectrometry Time-Of-Flight Mass Data with Applications in Clinical Diagnostics
Mass spectrometry (MS) based techniques have emerged as a standard forlarge-scale protein analysis. The ongoing progress in terms of more sensitive
machines and improved data analysis algorithms led to a constant expansion of
its fields of applications. Recently, MS was introduced into clinical proteomics
with the prospect of early disease detection using proteomic pattern matching.
Analyzing biological samples (e.g. blood) by mass spectrometry generates
mass spectra that represent the components (molecules) contained in a
sample as masses and their respective relative concentrations.
In this work, we are interested in those components that are constant within a
group of individuals but differ much between individuals of two distinct groups.
These distinguishing components that dependent on a particular medical condition
are generally called biomarkers. Since not all biomarkers found by the
algorithms are of equal (discriminating) quality we are only interested in a
small biomarker subset that - as a combination - can be used as a
fingerprint for a disease. Once a fingerprint for a particular disease
(or medical condition) is identified, it can be used in clinical diagnostics to
classify unknown spectra.
In this thesis we have developed new algorithms for automatic extraction of
disease specific fingerprints from mass spectrometry data. Special emphasis has
been put on designing highly sensitive methods with respect to signal detection.
Thanks to our statistically based approach our methods are able to
detect signals even below the noise level inherent in data acquired by common MS
machines, such as hormones.
To provide access to these new classes of algorithms to collaborating groups
we have created a web-based analysis platform that provides all necessary
interfaces for data transfer, data analysis and result inspection.
To prove the platform's practical relevance it has been utilized in several
clinical studies two of which are presented in this thesis. In these studies it
could be shown that our platform is superior to commercial systems with respect
to fingerprint identification. As an outcome of these studies several
fingerprints for different cancer types (bladder, kidney, testicle, pancreas,
colon and thyroid) have been detected and validated. The clinical partners in
fact emphasize that these results would be impossible with a less sensitive
analysis tool (such as the currently available systems).
In addition to the issue of reliably finding and handling signals in noise we
faced the problem to handle very large amounts of data, since an average dataset
of an individual is about 2.5 Gigabytes in size and we have data of hundreds to
thousands of persons. To cope with these large datasets, we developed a new
framework for a heterogeneous (quasi) ad-hoc Grid - an infrastructure that
allows to integrate thousands of computing resources (e.g. Desktop Computers,
Computing Clusters or specialized hardware, such as IBM's Cell Processor in a
Playstation 3)
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