37,679 research outputs found
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
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
Offline Handwritten Signature Verification - Literature Review
The area of Handwritten Signature Verification has been broadly researched in
the last decades, but remains an open research problem. The objective of
signature verification systems is to discriminate if a given signature is
genuine (produced by the claimed individual), or a forgery (produced by an
impostor). This has demonstrated to be a challenging task, in particular in the
offline (static) scenario, that uses images of scanned signatures, where the
dynamic information about the signing process is not available. Many
advancements have been proposed in the literature in the last 5-10 years, most
notably the application of Deep Learning methods to learn feature
representations from signature images. In this paper, we present how the
problem has been handled in the past few decades, analyze the recent
advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory,
Tools and Applications (IPTA 2017
Fairness Testing: Testing Software for Discrimination
This paper defines software fairness and discrimination and develops a
testing-based method for measuring if and how much software discriminates,
focusing on causality in discriminatory behavior. Evidence of software
discrimination has been found in modern software systems that recommend
criminal sentences, grant access to financial products, and determine who is
allowed to participate in promotions. Our approach, Themis, generates efficient
test suites to measure discrimination. Given a schema describing valid system
inputs, Themis generates discrimination tests automatically and does not
require an oracle. We evaluate Themis on 20 software systems, 12 of which come
from prior work with explicit focus on avoiding discrimination. We find that
(1) Themis is effective at discovering software discrimination, (2)
state-of-the-art techniques for removing discrimination from algorithms fail in
many situations, at times discriminating against as much as 98% of an input
subdomain, (3) Themis optimizations are effective at producing efficient test
suites for measuring discrimination, and (4) Themis is more efficient on
systems that exhibit more discrimination. We thus demonstrate that fairness
testing is a critical aspect of the software development cycle in domains with
possible discrimination and provide initial tools for measuring software
discrimination.Comment: Sainyam Galhotra, Yuriy Brun, and Alexandra Meliou. 2017. Fairness
Testing: Testing Software for Discrimination. In Proceedings of 2017 11th
Joint Meeting of the European Software Engineering Conference and the ACM
SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE),
Paderborn, Germany, September 4-8, 2017 (ESEC/FSE'17).
https://doi.org/10.1145/3106237.3106277, ESEC/FSE, 201
- …