A global variable-permutation based approach for estimating tree transformation parameters used in phylogenetically controlled multivariate regression v1

This protocol provides a method to realize phylogenetic control in multivariate regression modeling while estimating tree transformation parameters en route. The protocol requires compiling a list of all possible variable combinations (at multiple model lengths) and iterating through these while estimating the transformation parameters along side the regression. A combination of AIC and the coefficient of determination can be used, for example, to select the "best" model from numerous possible model lengths. The average of the tree transformation parameters can then be used on these "best" models to perform the final phylogenetically controlled multivariate regression.

Toward Adaptability of E-Evaluation: Transformation from Tree-Based to Graph-Based Structure

The COVID-19 pandemic and quarantine have forced students to use distance learning. Modern information technologies have enabled global e-learning usage but also revealed a lack of personalization and adaptation in the learning process when compared to face-to-face learning. While adaptive e-learning methods exist, their practical application is slow because of the additional time and resources needed to prepare learning material and its logical adaptation. To increase e-learning materials’ usability and decrease the design complexity of automated adaptive students’ work evaluation, we propose several transformations from a competence tree-based structure to a graph-based automated e-evaluation structure. Related works were summarized to highlight existing e-evaluation structures and the need for new transformations. Competence tree-based e-evaluation structure improvements were presented to support the implementation of top-to-bottom and bottom-to-top transformations. Validation of the proposed transformation was executed by analyzing different use-cases and comparing them to the existing graph-to-tree transformation. Research results revealed that the competence tree-based learning material storage is more reusable than graph-based solutions. Competence tree-based learning material can be transformed for different purposes in graph-based e-evaluation solutions. Meanwhile, graph-based learning material transformation to tree-based structure implies material redundancy, and the competence of the tree structure cannot be restored.

A Lightweight Blockchain Scheme for a Secure Smart Dust IoT Environment

Since a smart dust Internet of Things (IoT) system includes a very large number of devices sometimes deployed in hard-access areas, it is very difficult to prevent security attacks and to alleviate bottleneck phenomena. In this paper, we propose a lightweight blockchain scheme that helps device authentication and data security in a secure smart dust IoT environment. To achieve our goals, (1) we propose the structure of the lightweight blockchain and the algorithm of processing the blockchain. In addition, (2) we reorganize the linear block structure of the conventional blockchain into the binary tree structure in such a way that the proposed blockchain is more efficient in a secure smart dust IoT environment. Experiments show that the proposed binary tree-structured lightweight blockchain scheme can greatly reduce the time required for smart dust device authentication, even taking into account the tree transformation overhead. Compared with the conventional linear-structured blockchain scheme, the proposed binary tree-structured lightweight blockchain scheme achieves performance improvement by up to 40% (10% in average) with respect to the authentication time.

Assembly Sequence Planning by Probabilistic Tree Transformation

Various types of computer systems including CAD/CAM systems have been introduced in machine industry. Some of the systems can handle assembly sequence planning, however it requires long time for planning. This paper proposes a method of generating assembly sequences efficiently. This method extracts some parts and/or subassemblies whose possibilities of being removed from a product are strong, and tests whether they can be removed without any geometric interference. By performing these operations repeatedly, the method generates a disassembly sequence of the product, and obtains an assembly sequence by reversing it. The extraction of some parts and/or subassemblies is performed, based on probabilistic tree transformation. The authors present a calculation example by using a software tool integrated with a CAD system.

An alternative to synchronous tree substitution grammars

Synchronous tree substitution grammars (stsg) are a (formal) tree transformation model that is used in the area of syntax-based machine translation. A competitor that is at least as expressive as stsg is proposed and compared to stsg. The competitor is the extended multi bottom-up tree transducer (mbot), which is the bottom-up analogue with the additional feature that states have non-unary ranks. Unweighted mbot have already been investigated with respect to their basic properties, but the particular properties of the constructions that are required in the machine translation task are largely unknown. stsg and mbot are compared with respect to binarization, regular restriction, and application. Particular attention is paid to the complexity of the constructions.