The central objective of this study is to build a speech recognition system for non-native children, predicated upon feature-space discriminative models, including feature-space maximum mutual information (fMMI) and its boosted counterpart, boosted feature-space maximum mutual information (fbMMI). The original children's speech corpora, enhanced via speed perturbation-based collaborative data augmentation, yield an effective performance outcome. To investigate the effect of non-native children's second language speaking proficiency on speech recognition systems, the corpus analyzes various speaking styles of children, including both read and spontaneous speech. Experiments revealed that traditional ASR baseline models were outperformed by feature-space MMI models, thanks to their steadily increasing speed perturbation factors.
The standardization of post-quantum cryptography has prompted a heightened focus on the side-channel security implications of lattice-based post-quantum cryptography. Based on the leakage mechanism in the decapsulation phase of LWE/LWR-based post-quantum cryptography, a message recovery method was developed that incorporates templates and cyclic message rotation strategies for the message decoding operation. Intermediate state templates were formulated using the Hamming weight model, with cyclic message rotation employed in the construction of unique ciphertexts. The process of recovering secret messages encrypted using LWE/LWR-based schemes capitalized on power leakage during system operation. The proposed method's accuracy and reliability were assessed using CRYSTAL-Kyber. The experiment's findings supported the successful recovery of the confidential messages used in the encapsulation phase, directly leading to the recovery of the shared key. A reduction in power traces was achieved for both template generation and attack compared to the existing methods. Performance under low signal-to-noise ratio (SNR) was markedly enhanced, as evidenced by the significant increase in success rate, thereby decreasing recovery costs. Provided adequate signal-to-noise ratio, the message recovery success rate may approach 99.6%.
Quantum key distribution, having its genesis in 1984, is a commercial secure communication methodology that allows two parties to create a shared, randomly generated, secret key using the principles of quantum mechanics. To enhance the QUIC transport protocol, we propose a QQUIC (Quantum-assisted Quick UDP Internet Connections) protocol, swapping out the original classical key exchange mechanisms with quantum key distribution techniques. MG132 ic50 The demonstrable security of quantum key distribution underpins the independence of QQUIC key security from computational suppositions. Remarkably, in some situations, QQUIC could conceivably reduce network latency below that of QUIC. For the generation of keys, the attached quantum connections act as the dedicated communication lines.
A quite promising digital watermarking technique serves both to protect image copyrights and to ensure secure transmissions. However, the presently used strategies often fail to meet expectations concerning robustness and capacity simultaneously. A robust semi-blind image watermarking scheme, characterized by high capacity, is proposed in this paper. As a first step, the discrete wavelet transform (DWT) is used on the carrier image. To conserve storage capacity, watermark images are compressed via a compressive sampling procedure. A combined one- and two-dimensional chaotic map, based on the Tent and Logistic functions (TL-COTDCM), is utilized to scramble the compressed watermark image, thereby bolstering security and dramatically lowering the rate of false positive occurrences. To finish the embedding process, a singular value decomposition (SVD) component is applied to embed within the decomposed carrier image. This scheme utilizes a 512×512 carrier image to perfectly embed eight 256×256 grayscale watermark images, thus significantly increasing the capacity to approximately eight times the average capacity of current watermarking techniques. The scheme's performance under the pressure of various common attacks on high strength was evaluated, and the experimental results exhibited our method's superiority through the prominent evaluation indicators of normalized correlation coefficient (NCC) and peak signal-to-noise ratio (PSNR). Our digital watermarking method stands out from existing state-of-the-art techniques in terms of robustness, security, and capacity, indicating substantial potential for immediate applications in the field of multimedia.
Bitcoin, the original cryptocurrency, is a decentralized network used for worldwide, private, peer-to-peer transactions. Its pricing, however, is subject to fluctuations based on arbitrary factors, leading to hesitation from businesses and households and thereby restricting its application. However, a considerable variety of machine learning techniques exists for the exact prediction of future prices. A recurring problem in earlier Bitcoin price prediction studies is their reliance on empirical evidence, without providing strong analytical support for their conclusions. Consequently, this study endeavors to address the prediction of Bitcoin's price, encompassing both macroeconomic and microeconomic frameworks, via the implementation of novel machine learning techniques. Prior work has produced mixed findings on the dominance of machine learning over statistical analysis and vice versa, thereby highlighting the requirement for more in-depth explorations. Using comparative approaches, including ordinary least squares (OLS), ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), this paper explores the predictive power of economic theories, represented by macroeconomic, microeconomic, technical, and blockchain indicators, on Bitcoin (BTC) price. The study's findings highlight the predictive power of certain technical indicators on short-term Bitcoin price fluctuations, thereby substantiating the soundness of technical analysis. Lastly, macroeconomic and blockchain indicators are identified as substantial long-term predictors of Bitcoin price fluctuations, suggesting that theories concerning supply, demand, and cost-based pricing are essential in such predictions. The results indicate that SVR surpasses other machine learning and traditional modeling approaches. The innovative element of this research is a theoretical analysis of Bitcoin price prediction. Analysis of the overall results demonstrates SVR's superiority compared to other machine learning and traditional models. Amongst the contributions of this paper are several important advancements. It can support international finance by establishing a reference framework for asset pricing and bolstering investment decisions. Furthermore, it enhances the economics of BTC price prediction by presenting its theoretical underpinnings. Additionally, the authors' hesitancy regarding machine learning's ability to surpass traditional approaches in forecasting Bitcoin prices motivates this study, focusing on machine learning configuration for developers to use as a reference point.
A concise overview of the models and results regarding flows in network channels is the subject of this review paper. Our preliminary investigation involves a thorough review of literature spanning multiple research areas intertwined with these flows. Thereafter, we examine fundamental mathematical models of network flows, which are based on differential equations. immune effect Models regarding substance flows in network conduits merit our sustained focus. Stationary cases of these flows are analyzed by presenting probability distributions for substances at the channel nodes, using two primary models. One model represents a channel with many branches, employing differential equations, while the second illustrates a basic channel, employing difference equations to describe substance flow. The probability distributions derived encompass, as particular instances, any probability distribution of a discrete random variable assuming values of 0, 1. We further elaborate on the applicability of the examined models, including their use in predicting migratory patterns. germline genetic variants A close examination of the theory of stationary flows in network channels and the theory of random network growth is given considerable attention.
How do groups advocating particular positions secure a dominant voice in the public arena, silencing those with contrasting views? Beyond this, what is the connection between social media and this issue? Building upon neuroscientific insights into the processing of social feedback, we develop a theoretical framework to explore these questions comprehensively. Individuals, through iterative interactions, gauge the public's reception of their perspectives, and choose to remain silent if their position faces social disapproval. In a social forum defined by varied viewpoints, an agent acquires a distorted perception of public sentiment, strengthened by the communicative actions across different ideological camps. A determined minority, acting in unison, can overcome the voices of a significant majority. Alternatively, the potent social structuring of viewpoints facilitated by online platforms encourages collective systems in which divergent voices are articulated and vie for ascendancy in the public domain. Massive computer-mediated interactions on opinions are examined in this paper, focusing on the role of basic social information processing mechanisms.
Classical hypothesis testing, when evaluating two models, is bound by two essential limitations: first, the models must be nested; and second, one model must completely embody the structure of the true model generating the data. Alternative model selection methods, using discrepancy measures, avoid the need for the previously mentioned assumptions. To assess the probability that the fitted null model more closely mirrors the underlying generative model than the fitted alternative model, we, in this paper, utilize a bootstrap approximation of the Kullback-Leibler divergence (BD). Our approach to rectify the bias present in the BD estimator involves either a bootstrap-based correction or the addition of the parameter count for the competing model.