4,345 research outputs found
Novel Survey on Email Spam Filtering Methods
Spam emails are causing major resource wastage by unnecessarily flooding the network links.The cost of spam is borne mostly by the recipient, so it is a form of postage due advertising. This paper describes how different methods can be used for spam filtering.To protect against unsolicited e-mails there are number of techniques presented with goal of efficient, accurate spam filtering. Few previous spam filters can meet the requirements of being user-friendly, attack-resilient, and personalized. This paper presents a literature survey into the state of research on spam filtering methods and how it is useful for user’s lives
Utilizing Machine Learning for Signal Classification and Noise Reduction in Amateur Radio
In the realm of amateur radio, the effective classification of signals and
the mitigation of noise play crucial roles in ensuring reliable communication.
Traditional methods for signal classification and noise reduction often rely on
manual intervention and predefined thresholds, which can be labor-intensive and
less adaptable to dynamic radio environments. In this paper, we explore the
application of machine learning techniques for signal classification and noise
reduction in amateur radio operations. We investigate the feasibility and
effectiveness of employing supervised and unsupervised learning algorithms to
automatically differentiate between desired signals and unwanted interference,
as well as to reduce the impact of noise on received transmissions.
Experimental results demonstrate the potential of machine learning approaches
to enhance the efficiency and robustness of amateur radio communication
systems, paving the way for more intelligent and adaptive radio solutions in
the amateur radio community
Invisible Pixels Are Dead, Long Live Invisible Pixels!
Privacy has deteriorated in the world wide web ever since the 1990s. The
tracking of browsing habits by different third-parties has been at the center
of this deterioration. Web cookies and so-called web beacons have been the
classical ways to implement third-party tracking. Due to the introduction of
more sophisticated technical tracking solutions and other fundamental
transformations, the use of classical image-based web beacons might be expected
to have lost their appeal. According to a sample of over thirty thousand images
collected from popular websites, this paper shows that such an assumption is a
fallacy: classical 1 x 1 images are still commonly used for third-party
tracking in the contemporary world wide web. While it seems that ad-blockers
are unable to fully block these classical image-based tracking beacons, the
paper further demonstrates that even limited information can be used to
accurately classify the third-party 1 x 1 images from other images. An average
classification accuracy of 0.956 is reached in the empirical experiment. With
these results the paper contributes to the ongoing attempts to better
understand the lack of privacy in the world wide web, and the means by which
the situation might be eventually improved.Comment: Forthcoming in the 17th Workshop on Privacy in the Electronic Society
(WPES 2018), Toronto, AC
Can Network Analysis Techniques help to Predict Design Dependencies? An Initial Study
The degree of dependencies among the modules of a software system is a key
attribute to characterize its design structure and its ability to evolve over
time. Several design problems are often correlated with undesired dependencies
among modules. Being able to anticipate those problems is important for
developers, so they can plan early for maintenance and refactoring efforts.
However, existing tools are limited to detecting undesired dependencies once
they appeared in the system. In this work, we investigate whether module
dependencies can be predicted (before they actually appear). Since the module
structure can be regarded as a network, i.e, a dependency graph, we leverage on
network features to analyze the dynamics of such a structure. In particular, we
apply link prediction techniques for this task. We conducted an evaluation on
two Java projects across several versions, using link prediction and machine
learning techniques, and assessed their performance for identifying new
dependencies from a project version to the next one. The results, although
preliminary, show that the link prediction approach is feasible for package
dependencies. Also, this work opens opportunities for further development of
software-specific strategies for dependency prediction.Comment: Accepted at ICSA 201
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