DEVELOPMENT OF THE DIGITAL ENTERPRISES ARCHITECTURES

The article examines the impact of the use of digital technologies, such as new production technologies, industrial Internet, artificial intelligence, big data and predictive analytics on the construction of dynamic and flexible architectures of modern digital enterprises. As a tool that implements a single information space of production and business processes of the enterprise, the concept of Industry 4.0 and the architectural approach based on RAMI are proposed, which consider the use of the network structure of the connected world as the technological basis of the enterprise. To model the production and business processes of a digital enterprise, the concept of building enterprises whose activities are based on the use of models (Model-Based Enterprise) and an approach to modeling and managing digital flow (Digital Thread) is considered. To increase the semantic interoperability of interacting models of digital enterprises, it is proposed to use methods of ontological modeling, and to accelerate the flexible configuration of digital enterprises – methods of microservice organization of software.

Design of an Offline Handwriting Recognition System Tested on the Bangla and Korean Scripts

This dissertation presents a flexible and robust offline handwriting recognition system which is tested on the Bangla and Korean scripts. Offline handwriting recognition is one of the most challenging and yet to be solved problems in machine learning. While a few popular scripts (like Latin) have received a lot of attention, many other widely used scripts (like Bangla) have seen very little progress. Features such as connectedness and vowels structured as diacritics make it a challenging script to recognize. A simple and robust design for offline recognition is presented which not only works reliably, but also can be used for almost any alphabetic writing system. The framework has been rigorously tested for Bangla and demonstrated how it can be transformed to apply to other scripts through experiments on the Korean script whose two-dimensional arrangement of characters makes it a challenge to recognize. The base of this design is a character spotting network which detects the location of different script elements (such as characters, diacritics) from an unsegmented word image. A transcript is formed from the detected classes based on their corresponding location information. This is the first reported lexicon-free offline recognition system for Bangla and achieves a Character Recognition Accuracy (CRA) of 94.8%. This is also one of the most flexible architectures ever presented. Recognition of Korean was achieved with a 91.2% CRA. Also, a powerful technique of autonomous tagging was developed which can drastically reduce the effort of preparing a dataset for any script. The combination of the character spotting method and the autonomous tagging brings the entire offline recognition problem very close to a singular solution. Additionally, a database named the Boise State Bangla Handwriting Dataset was developed. This is one of the richest offline datasets currently available for Bangla and this has been made publicly accessible to accelerate the research progress. Many other tools were developed and experiments were conducted to more rigorously validate this framework by evaluating the method against external datasets (CMATERdb 1.1.1, Indic Word Dataset and REID2019: Early Indian Printed Documents). Offline handwriting recognition is an extremely promising technology and the outcome of this research moves the field significantly ahead.

2D black arsenic phosphorus and its application for anodes of lithium ion batteries

Two-dimensional (2D) structured black arsenic phosphorus (B-AsP) compound materials play a pivotal role in a wide range of applications due to their exotic physical properties and flexible architectures.

SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning

Machine learning applications, such as object detection and content recommendation, often require training a single model to predict multiple targets at the same time. Multi-task learning through neural networks became popular recently, because it not only helps improve the accuracy of many prediction tasks when they are related, but also saves computation cost by sharing model architectures and low-level representations. The latter is critical for real-time large-scale machine learning systems. However, classic multi-task neural networks may degenerate significantly in accuracy when tasks are less related. Previous works (Misra et al. 2016; Yang and Hospedales 2016; Ma et al. 2018) showed that having more flexible architectures in multi-task models, either manually-tuned or softparameter-sharing structures like gating networks, helps improve the prediction accuracy. However, manual tuning is not scalable, and the previous soft-parameter sharing models are either not flexible enough or computationally expensive. In this work, we propose a novel framework called SubNetwork Routing (SNR) to achieve more flexible parameter sharing while maintaining the computational advantage of the classic multi-task neural-network model. SNR modularizes the shared low-level hidden layers into multiple layers of subnetworks, and controls the connection of sub-networks with learnable latent variables to achieve flexible parameter sharing. We demonstrate the effectiveness of our approach on a large-scale dataset YouTube8M. We show that the proposed method improves the accuracy of multi-task models while maintaining their computation efficiency.

Electrostatic lateral interactions drive ESCRT-III heteropolymer assembly

Self-assembly of ESCRT-III complex is a critical step in all ESCRT-dependent events. ESCRT-III hetero-polymers adopt variable architectures, but the mechanisms of inter-subunit recognition in these hetero-polymers to create flexible architectures remain unclear. We demonstrate in vivo and in vitro that the Saccharomyces cerevisiae ESCRT-III subunit Snf7 uses a conserved acidic helix to recruit its partner Vps24. Charge-inversion mutations in this helix inhibit Snf7-Vps24 lateral interactions in the polymer, while rebalancing the charges rescues the functional defects. These data suggest that Snf7-Vps24 assembly occurs through electrostatic interactions on one surface, rather than through residue-to-residue specificity. We propose a model in which these cooperative electrostatic interactions in the polymer propagate to allow for specific inter-subunit recognition, while sliding of laterally interacting polymers enable changes in architecture at distinct stages of vesicle biogenesis. Our data suggest a mechanism by which interaction specificity and polymer flexibility can be coupled in membrane-remodeling heteropolymeric assemblies.

Flexible Architectures for Cryptographic Algorithms — A Systematic Literature Review

Symmetric and asymmetric cryptographic algorithms are used for a secure transmission of data over an unsecured public channel. In order to use these algorithms in real-time applications, many flexible hardware architectures have been proposed and implemented with multiple design constraints. Therefore, a systematic study is required to analyze various implementation approaches. This paper has focused on the identification and classification of recent research practices pertaining to the flexible hardware implementation of cryptographic algorithms. We have used Systematic Literature Review (SLR) process to identify 51 research articles, published during 2008–2017. The identified researches have been classified according to three design approaches: (1) crypto processor, (2) crypto coprocessor and (3) multicore crypto processor. Consequently, a comparative analysis of various cryptographic algorithms in terms of flexibility, throughput, area, power and implementation technology has been presented. A comprehensive investigation of flexible architectures for implementing cryptographic algorithms facilitates researchers and designers of the domain to select an appropriate design approach for a particular algorithm and/or application according to their needs.