501 research outputs found
Inference and Evaluation of the Multinomial Mixture Model for Text Clustering
In this article, we investigate the use of a probabilistic model for
unsupervised clustering in text collections. Unsupervised clustering has become
a basic module for many intelligent text processing applications, such as
information retrieval, text classification or information extraction. The model
considered in this contribution consists of a mixture of multinomial
distributions over the word counts, each component corresponding to a different
theme. We present and contrast various estimation procedures, which apply both
in supervised and unsupervised contexts. In supervised learning, this work
suggests a criterion for evaluating the posterior odds of new documents which
is more statistically sound than the "naive Bayes" approach. In an unsupervised
context, we propose measures to set up a systematic evaluation framework and
start with examining the Expectation-Maximization (EM) algorithm as the basic
tool for inference. We discuss the importance of initialization and the
influence of other features such as the smoothing strategy or the size of the
vocabulary, thereby illustrating the difficulties incurred by the high
dimensionality of the parameter space. We also propose a heuristic algorithm
based on iterative EM with vocabulary reduction to solve this problem. Using
the fact that the latent variables can be analytically integrated out, we
finally show that Gibbs sampling algorithm is tractable and compares favorably
to the basic expectation maximization approach
Reversibility and switching options values in the geological disposal of radioactive waste.
This article offers some economic insights for the debate on the reversible geological disposal of radioactive waste. Irreversibility due to large sunk costs, an important degree of flexibility and several sources of uncertainty are taken into account in the decision process relative to the radioactive waste disposal. We draw up a stochastic model in a continuous time framework to study the decision problem of a reversible repository project for the radioactive waste, with multiple disposal stages. We consider that the value of reversibility, related to the radioactive waste packages, is jointly affected by economic and technological uncertainty. These uncertainties are modeled, first, by a 2-Dimensional Geometric Brown- ian Motion, and, second, by a Geometric Brownian Motion with a Poisson jump process. A numerical analysis and a sensitivity study of various parameters are also proposed.radioactive waste, reversibility, switching, real option theory.
The Automation of the Extraction of Evidence masked by Steganographic Techniques in WAV and MP3 Audio Files
Antiforensics techniques and particularly steganography and cryptography have
become increasingly pressing issues that affect the current digital forensics
practice, both techniques are widely researched and developed as considered in
the heart of the modern digital era but remain double edged swords standing
between the privacy conscious and the criminally malicious, dependent on the
severity of the methods deployed. This paper advances the automation of hidden
evidence extraction in the context of audio files enabling the correlation
between unprocessed evidence artefacts and extreme Steganographic and
Cryptographic techniques using the Least Significant Bits extraction method
(LSB). The research generates an in-depth review of current digital forensic
toolkit and systems and formally address their capabilities in handling
steganography-related cases, we opted for experimental research methodology in
the form of quantitative analysis of the efficiency of detecting and extraction
of hidden artefacts in WAV and MP3 audio files by comparing standard industry
software. This work establishes an environment for the practical implementation
and testing of the proposed approach and the new toolkit for extracting
evidence hidden by Cryptographic and Steganographic techniques during forensics
investigations. The proposed multi-approach automation demonstrated a huge
positive impact in terms of efficiency and accuracy and notably on large audio
files (MP3 and WAV) which the forensics analysis is time-consuming and requires
significant computational resources and memory. However, the proposed
automation may occasionally produce false positives (detecting steganography
where none exists) or false negatives (failing to detect steganography that is
present) but overall achieve a balance between detecting hidden data accurately
along with minimising the false alarms.Comment: Wires Forensics Sciences Under Revie
Nonhuman citizens on trial: The ecological politics of a beaver reintroduction
This is the author accepted manuscript. The final version is available from SAGE Publications via the DOI in this recordWildlife reintroductions can unsettle social and ecological norms, and are often controversial. In this paper, we examine the recent (re)introduction of Eurasian beavers to England, to analyse responses to an unauthorised release of a formerly resident species. Although the statutory response to the introduction was to attempt to reassert ecological and political order by recapturing the beavers, this action was strongly opposed by a diverse collective, united and made powerful by a common goal: to protect Englandâs ânewâ nonhuman residents. We show how this clash of state resolve and public dissent produced an uneasy compromise in the form of a formal, licensed âbeaver reintroduction trialâ, in which the new beaver residents have been allowed to remain, but under surveillance. We propose that although the trial is unorthodox and risky, there is an opportunity for it to be treated as a âwild experimentâ through which a more open-ended, experimental approach to co-inhabiting with wildlife might be attempted.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: SLC was supported by a scholarship from the University of Exeter
A novel hybrid method for effective identification and extraction of digital evidence masked by steganographic techniques in WAV and MP3 files
Anti-forensics techniques, particularly steganography and cryptography, have become increasingly pressing issues affecting current digital forensics practices. This paper advances the automation of hidden evidence extraction in audio files by proposing a novel multi-approach method. This method facilitates the correlation between unprocessed artefacts, indexed and live forensics analysis, and traditional steganographic and cryp- tographic detection techniques. In this work, we opted for experimental research methodology in the form of a quantitative analysis of the efficiency of the proposed automation in detecting and extracting hidden artefacts in WAV and MP3 audio files. This comparison is made against standard industry systems. This work advances the current automation in extracting evidence hidden by cryptographic and steganographic techniques during forensic investigations. The proposed multi-approach demonstrates a clear enhancement in terms of cover- age and accuracy, notably on large audio files (MP3 and WAV), where manual forensic analysis is complex, time-consuming and requires significant expertise. Nonetheless, the proposed multi-approach automation may occasionally produce false positives (detecting steganography where none exists) or false negatives (failing to detect steganography that is present). However, it strikes a good balance between efficiently and effectively detecting hidden evidence, minimising false negatives and validating its reliability
Sound environment analysis in smart home
International audienceThis study aims at providing audio-based interaction technology that lets the users have full control over their home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. The paper presents the sound and speech analysis system evaluated thanks to a corpus of data acquired in a real smart home environment. The 4 steps of analysis are signal detection, speech/sound discrimination, sound classification and speech recognition. The results are presented for each step and globally. The very first experiments show promising results be it for the modules evaluated independently or for the whole system
Multibiometric security in wireless communication systems
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and
WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition.
First is the enrolment phase by which the database of watermarked fingerprints with
memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel.
Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present oneâs fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user.
The following three steps then involve speaker recognition including the user
responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user.
In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint
image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and
sliding neighborhood) have been followed with further two steps for embedding, and
extracting the watermark into the enhanced fingerprint image utilising Discrete
Wavelet Transform (DWT).
In the speaker recognition stage, the limitations of this technique in wireless
communication have been addressed by sending voice feature (cepstral coefficients)
instead of raw sample. This scheme is to reap the advantages of reducing the
transmission time and dependency of the data on communication channel, together
with no loss of packet. Finally, the obtained results have verified the claims
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