Real-time Detection of AI-Generated Speech for DeepFake Voice Conversion

There are growing implications surrounding generative AI in the speech domain that enable voice cloning and real-time voice conversion from one individual to another. This technology poses a significant ethical threat and could lead to breaches of privacy and misrepresentation, thus there is an urgent need for real-time detection of AI-generated speech for DeepFake Voice Conversion. To address the above emerging issues, the DEEP-VOICE dataset is generated in this study, comprised of real human speech from eight well-known figures and their speech converted to one another using Retrieval-based Voice Conversion. Presenting as a binary classification problem of whether the speech is real or AI-generated, statistical analysis of temporal audio features through t-testing reveals that there are significantly different distributions. Hyperparameter optimisation is implemented for machine learning models to identify the source of speech. Following the training of 208 individual machine learning models over 10-fold cross validation, it is found that the Extreme Gradient Boosting model can achieve an average classification accuracy of 99.3% and can classify speech in real-time, at around 0.004 milliseconds given one second of speech. All data generated for this study is released publicly for future research on AI speech detection

Exoplanet HD209458b: inflated hydrogen atmosphere but no sign of evaporation

Many extrasolar planets orbit closely to their parent star. Their existence raises the fundamental problem of loss and gain in their mass. For exoplanet HD209458b, reports on an unusually extended hydrogen corona and a hot layer in the lower atmosphere seem to support the scenario of atmospheric inflation by the strong stellar irradiation. However, difficulties in reconciling evaporation models with observations call for a reassessment of the problem. Here, we use HST archive data to report a new absorption rate of ~8.9% +/- 2.1% by atomic hydrogen during the HD209458b transit, and show that no sign of evaporation could be detected for the exoplanet. We also report evidence of time variability in the HD209458 Lyman-a flux, a variability that was not accounted for in previous studies, which corrupted their diagnostics. Mass loss rates thus far proposed in the literature in the range 5x(10^{10}-10^{11} g s^{-1}) must induce a spectral signature in the Lyman-a line profile of HD209458 that cannot be found in the present analysis. Either an unknown compensation effect is hiding the expected spectral feature or else the mass loss rate of neutrals from HD209458 is modest.Comment: corrected for typos. Published 2007 December 10 in Apj

Using trust to detect denial of service attacks in the internet of things over MANETs

The rapid growth of employing devices as tools in daily life and the technological revolution have led to the invention of a novel paradigm; the Internet of Things (IoT). It includes a group of ubiquitous devices that communicate and share data with each other. These devices use the Internet Protocol (IP) to manage network nodes through mobile ad hoc networks (MANET). IoT is beneficial to MANET as the nodes are self-organising and the information reach can be expanded according to the network range. Due to the nature of MANET, such as dynamic topology, a number of challenges are inherent, such as Denial of Service (DoS) attacks. DoS attacks prohibit legitimate users from accessing their authorised services. In addition, because of the high mobility of MANET, the network can merge with other networks. In this situation, two or more networks of untrusted nodes may join one another leaving each of the networks open to attack. This paper proposes a novel method to detect DoS attacks immediately prior to the merger of two MANETs. To demonstrate the applicability of the proposed approach, a Grayhole attack is used in this study to evaluate the performance of the proposed method in detecting attacks

Adaptive thermal sensor array placement for human segmentation and occupancy estimation

Thermal sensor array (TSA) offers privacy-preserving, low-cost, and non-invasive features, which makes it suitable for various indoor applications such as anomaly detection, health monitoring, home security, and monitoring energy efficiency applications. Previous approaches to human-centred applications using the TSA usually relied on the use of a fixed sensor location to make the human-sensor distance and the human presence shape fixed. However, placing this sensor in different locations and new indoor environments can pose a significant challenge. In this paper, a novel framework based on a deep convolutional encoder-decoder network is proposed to address this challenge in real-life deployment. The framework presents a semantic segmentation of the human presence and estimates the occupancy in indoor-environment. It is also capable to segment the human presence and counts the number of people from different sensor locations, indoor environments, and human to sensor distance. Furthermore, the impact of the distance on the human presence using the TSA is investigated. The framework is evaluated to estimate the occupancy in different sensor locations, the number of occupants, environments, and human distance with classification and regression machine learning approaches. This paper shows that the classification approach using the adaptive boosting algorithm is an accurate approach which has achieves an accuracy of 98.43% and 100% from vertical and overhead sensor locations respectively

A minimal HIV-AIDS infection model with general incidence rate and application to Morocco data

We study the global dynamics of a SICA infection model with general incidence rate. The proposed model is calibrated with cumulative cases of infection by HIV-AIDS in Morocco from 1986 to 2015. We first prove that our model is biologically and mathematically well-posed. Stability analysis of different steady states is performed and threshold parameters are identified where the model exhibits clearance of infection or maintenance of a chronic infection. Furthermore, we examine the robustness of the model to some parameter values by examining the sensitivity of the basic reproduction number. Finally, using numerical simulations with real data from Morocco, we show that the model predicts well such reality.Comment: This is a preprint of a paper whose final and definite form is with 'Statistics Opt. Inform. Comput.', Vol. 7, No 2 (2019). See [http://www.IAPress.org]. Submitted 16/Sept/2018; Revised 10 & 15/Dec/2018; Accepted 15/Dec/201

A novel deep mining model for effective knowledge discovery from omics data

Knowledge discovery from omics data has become a common goal of current approaches to personalised cancer medicine and understanding cancer genotype and phenotype. However, high-throughput biomedical datasets are characterised by high dimensionality and relatively small sample sizes with small signal-to-noise ratios. Extracting and interpreting relevant knowledge from such complex datasets therefore remains a significant challenge for the fields of machine learning and data mining. In this paper, we exploit recent advances in deep learning to mitigate against these limitations on the basis of automatically capturing enough of the meaningful abstractions latent with the available biological samples. Our deep feature learning model is proposed based on a set of non-linear sparse Auto-Encoders that are deliberately constructed in an under-complete manner to detect a small proportion of molecules that can recover a large proportion of variations underlying the data. However, since multiple projections are applied to the input signals, it is hard to interpret which phenotypes were responsible for deriving such predictions. Therefore, we also introduce a novel weight interpretation technique that helps to deconstruct the internal state of such deep learning models to reveal key determinants underlying its latent representations. The outcomes of our experiment provide strong evidence that the proposed deep mining model is able to discover robust biomarkers that are positively and negatively associated with cancers of interest. Since our deep mining model is problem-independent and data-driven, it provides further potential for this research to extend beyond its cognate disciplines

Cold Positrons from Decaying Dark Matter

Many models of dark matter contain more than one new particle beyond those in the Standard Model. Often heavier particles decay into the lightest dark matter particle as the Universe evolves. Here we explore the possibilities that arise if one of the products in a (Heavy Particle) $\rightarrow$ (Dark Matter) decay is a positron, and the lifetime is shorter than the age of the Universe. The positrons cool down by scattering off the cosmic microwave background and eventually annihilate when they fall into Galactic potential wells. The resulting 511 keV flux not only places constraints on this class of models but might even be consistent with that observed by the INTEGRAL satellite.Comment: 20 pages, 7 figure

Investigation of pilot-scale 8040 FO membrane module under different operating conditions for brackish water desalination

© 2014, © 2014 Balaban Desalination Publications. All rights reserved. Two spiral wound forward osmosis membrane modules with different spacer designs (corrugated spacer [CS] and medium spacer [MS]) were investigated for the fertilizer-drawn forward osmosis (FO) desalination of brackish groundwater (BGW) at a pilot-scale level. This study mainly focused on examining the influence of various operating conditions such as feed flow rate, total dissolved solids (TDS) concentration of the BGW feed, and draw solution (DS) concentrations using ammonium sulfate ((NH4)2SO4, SOA) on the performance of two membrane modules. The feed flow rate played a positive role in the average water flux of the pilot-scale FO membrane module due to enhanced mass transfer coefficient across the membrane surface. Feed TDS and DS concentrations also played a significant role in both FO membrane modules because they are directly related to the osmotic driving force and membrane fouling tendency. CS module performed slightly better than MS module during all experiments due to probably enhanced mass transfer and lower fouling propensity associated with the CS. Besides, CS spacer provides larger channel space that can accommodate larger volumes of DS, and hence, could maintain higher DS concentration. However, the extent of dilution for the CS module is slightly lower