Jornal de Teoria Generalizada de Mentiras e Aplicações

We argue in this paper that deep properties of discourse structure can be revealed by mapping discourse representations to networks and analyzing them using tools from network theory. Two talk clarified corpora, C58 and STAC, that have a place with various talk types and dialects were looked at and examined. The discourse representations of both corpora were based on a variety of key network indices, which demonstrate the distinct network profiles of the two kinds of discourse. In addition, strong tendencies in building or avoiding the construction of discourse relations between utterances for permissible three-node discourse subgraphs were illuminated by the discovery of both network motifs and antimotifs for the discourse networks in the two corpora. New discourse structure rules based on the properties of the networks that underpin discourse representation may emerge as a result of these findings.

The general case of a material belonging to an unknown class of constitutive behavior is the focus of our recently developed EUCLID method for unsupervised automated discovery of material laws. To this end, we influence the hypothesis of summed up standard materials, which envelops a plenty of significant constitutive classes including flexibility, thickness, versatility and erratic blends thereof. That's what we show, dependent just upon full-field kinematic estimations and net response powers, EUCLID can naturally find the two scalar thermodynamic possibilities, to be specific, the Helmholtz free energy and the dissemination potential, which totally characterize the way of behaving of summed up standard materials.

This paper investigates a Compressive Security (CP) philosophy for ideal tradeoff between utility increase and protection misfortune. CP addresses an aspect diminished subspace plan of ideally desensitized question that might be securely imparted to the general population. Based upon the data and assessment hypothesis, this paper proposes a "differential common data" (DMI) rule to defend the security insurance (PP). Algorithmically, DMI-ideal arrangements can be inferred by means of the Discriminant Part Investigation (DCA). In addition, DCA has two machine learning variants that are suitable for supervised learning applications—one in the kernel space and the other in the original space. CP unifies the conventional Information Bottleneck (IB) and Privacy Funnel (PF) and results in two constrained optimizers known as Generalized Information Bottleneck (GIB) and Generalized Privacy Funnel (GPF) by extending the concept of DMI to the utility gain and privacy loss. DCA can be further extended to a DUCA machine learning variant in supervised learning environments to achieve the best possible compromise between utility gain and privacy loss. Finally, a golden-section iterative method is developed specifically for the two constrained optimization problems in order to speed up convergence: GPF and GIB. Data bottleneck and security channel issues are critical challenges in data transmission and communication. Data bottleneck arises when the rate of data production or transfer exceeds the capacity of the communication channel or system. Security channel issues involve vulnerabilities that compromise the confidentiality, integrity, or availability of transmitted data. This article provides an overview of these challenges, their causes and implications. It discusses strategies for addressing data bottleneck, such as optimizing bandwidth, storage and processing capabilities. It also explores security measures, including authentication, encryption and intrusion detection, to mitigate security channel issues. A holistic approach integrating efficient data management and robust security practices is crucial for ensuring smooth and secure information flow.