Robust Data Structure Research Articles

Powerful graph neural network for node classification of the IoT network

Internet of Things (IoT) devices are increasingly used in various applications in our daily lives. The network structure for IoT is heterogeneous and can create a complex architecture depending on the application and geographical structure. To efficiently process the information within this diverse and complex relationship, a robust data structure is needed for network operations. Graph neural network (GNN) technology is emerging as a capable tool for predicting complex data structures, such as graphs. Graphs can be employed to mimic the structure of IoT network and process information from IoT nodes using GNN techniques. In this paper, our goal is to explore the effectiveness of GNN in performing the node classification task for a given IoT network. We have generated three different IoT networks with varying network sizes, number of nodes, and feature sizes. We then test 12 different GNN algorithms to evaluate their performance in IoT node classification. Each method is examined in detail to observe its training behavior, testing behavior, and resilience against noise. In addition, time complexity and generalization ability of each model have also been studied. The experimental results show that some methods exhibit high resilience against noisy data for IoT node classification accuracy.

A nodal-based Lagrange multiplier/cohesive zone approach for three-dimensional dynamic crack simulations of quasi-brittle materials

Accurate simulations of cracks remain a challenging task in the computational mechanics community. The main purpose of this work is to develop a nodal-based Lagrange multiplier/cohesive zone (LM/CZ) method for arbitrary three-dimensional (3D) crack simulations in the finite element (FE) models of quasi-brittle materials. The main idea of the developed method is to fulfill the displacement compatibility condition before crack onset via LMs enforced at nodes and describe crack behaviors with the aid of the cohesive zone model (CZM). The main appeal of our method lies in effectively addressing the so-called artificial compliance numerical issue arising in the conventional intrinsic CZMs, thereby yielding more accurate numerical results than intrinsic CZMs. Node-pair groups are generated via a robust data structure. In each group, the LM forces for all nodal pairs are calculated in a predictor–corrector form, which can achieve convergence within a limited number of iterations using the Gauss–Seidel method. Two loop types for nodal pairs are compared to find a more efficient scheme with fewer iterations. The nodal constraints are smoothly switched from LMs to cohesive forces after a defined fracture criterion is satisfied. The developed method is implemented in conjunction with tetrahedral elements. Several quasi-static numerical examples, including modes I and I/II cracks, are performed to demonstrate the numerical accuracy of our developed method. Finally, this work simulates a Kolthoff plate impact problem, where the computational accuracy and efficiency are further investigated and discussed by comparing our results with those calculated via the intrinsic CZMs.

Steel surface defect detection based on self-supervised contrastive representation learning with matching metric

Defect detection is crucial in the quality control of industrial applications. Existing supervised methods are heavily reliant on the large amounts of labeled data. However, labeled data in some specific fields are still scarce, and it requires professionals to do expensive manual annotations. In this paper, we construct a novel self-supervised steel surface defect detection model by learning better embedding feature representation of the defect on large amounts of unlabeled data, which can achieve excellent results in downstream detection tasks. Commonly used image embeddings strategies in self-supervised contrastive learning methods destroy the spatial structures of the image and are not suitable for pre-training of object detection. To address the aforementioned issue, we preserve convolutional feature maps to mine robust data structures and local features, which can enhance the representation capability of the upstream model and make it applicable for transfer to object detection tasks. Besides, in order to eliminate the effect of random augmentations of contrastive learning, which can introduce noise on multi-target coexistence datasets, the Earth Mover’s Distance (EMD) metric is employed to evaluate the contrastive matching similarity. Finally, a Self-supervised Contrastive Representation Learning framework with EMD (SCRL-EMD) is constructed through learning on large-scale unlabeled data and then transferred to Faster R-CNN and RetinaNet for detection performance validation on two public steel defect datasets. Comparative experimental results show that our method can achieve superior results than the state-of-the-art approaches. Compared to the baseline model, it achieves 4.1% and 6.8% mAP improvement on the two datasets, respectively. More importantly, a further improvement can be achieved on a smaller downstream dataset, revealing the meaningful potential of our method in exploiting more readily available unlabeled data.

A generic optimization framework for scheduling problems under machine deterioration and maintenance activities

This paper introduces a novel generic optimization framework for scheduling problems with machine deterioration and maintenance activities. The framework employs computationally efficient and robust data structures for schedules along with templates for implementing optimization algorithms making it applicable within Industry 4.0. The concept includes, but not limited to, the optimization objectives such as the maximum completion time, the maximum lateness (or tardiness), the total (weighted) number of late jobs, the total (weighted) completion times, the total (weighted) tardiness, the just-in-time. A set of example algorithms based on iterative local search, i.e. Nawaz–Enscore–Ham’s method (NEH), simulated annealing, genetic algorithm, scatter search algorithm, artificial bee colony were embedded within the framework to demonstrate its robustness. The theoretical and experimental analysis presented in the paper proved the high efficiency of the proposed approach; it traverses areas containing optimal solutions and at the same time it is characterized by a low computational complexity of related procedures to calculate criterion values or to generate new solutions. Furthermore, if the optimization methods are enhanced by the proposed data structures and procedures, their standard computational complexity is not increased even for complex scheduling problems including maintenance activities. The proposed framework modular architecture enables further integration with new algorithms aiming at improvement of neighbourhood search techniques. Its robustness, efficiency, and modularity can bring advantages in various decision support systems.

Collaborative Development of Polymer-Based Collection Survey Methodology and Relational Data Model

ABSTRACT Plastic-focused collection surveys are a fundamental starting point for locating, identifying and assessing the conservation needs of polymer-based objects, and are increasingly conducted internationally. As surveys are both time- and people-intensive, it is critical to ensure the optimal usefulness of the data for the institution and wider conservation community through consistent documentation, using flexible robust data structures, and making results accessible. However, only summaries of survey results have been published in the conservation literature, resulting in a lack of continuity and information dissemination. This is problematic for institutions without the necessary resources to conduct extensive surveys and to identify polymer types. This paper outlines the collaborative development of a plastic-focused collection survey methodology, highlighting the importance of developing and testing standardised procedures, engaging with expert collaborators, and ensuring results are accessible.

Scattering Centers to Point Clouds: A Review of mmWave Radars for Non-Radar-Engineers

Recently, mmWave radars have been gaining popularity, thanks to their low cost, ease of use and high-resolution sensing. In this paper, we provide a review of the mmWave radar data processing frameworks, starting from mathematical foundations to applications. Specifically, we focus on the mmWave radar point cloud as a robust data structure representing compressed signatures for target recognition and classification. We first focus on the generation of the radar point clouds, and the signal processing algorithms designed for their unique characteristics. Then, we illustrate how the radar point clouds are prepared for feature extraction and classification using machine learning and deep learning approaches. Finally, we summarize the state-of-the-art applications, open datasets, developments and the future research directions in this field.

Integer coordinates for intrinsic geometry processing

This paper describes a numerically robust data structure for encoding intrinsic triangulations of polyhedral surfaces. Many applications demand a correspondence between the intrinsic triangulation and the input surface, but existing data structures either rely on floating point values to encode correspondence, or do not support remeshing operations beyond basic edge flips. We instead provide an integer-based data structure that guarantees valid correspondence, even for meshes with near-degenerate elements. Our starting point is the framework of normal coordinates from geometric topology, which we extend to the broader set of operations needed for mesh processing (vertex insertion, edge splits, etc. ). The resulting data structure can be used as a drop-in replacement for earlier schemes, automatically improving reliability across a wide variety of applications. As a stress test, we successfully compute an intrinsic Delaunay refinement and associated subdivision for all manifold meshes in the Thingi10k dataset. In turn, we can compute reliable and highly accurate solutions to partial differential equations even on extremely low-quality meshes.

Category-Theoretic Formulation of the Model-Based Systems Architecting Cognitive-Computational Cycle

We introduce the Concept→Model→Graph→View Cycle (CMGVC). The CMGVC facilitates coherent architecture analysis, reasoning, insight, and decision making based on conceptual models that are transformed into a generic, robust graph data structure (GDS). The GDS is then transformed into multiple views of the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). The CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. We show that modeling languages are categories, drawing an analogy to programming languages. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a stakeholder-informing matrix that stimulates system architecture insight.

Conceptual State Analysis

Conceptual State Analysis (CSA) is the process of defining, exploring, and reasoning about state attributes and state spaces in complex system architectures. Model-Based State Analysis enhances CSA with formal modeling and analysis methods. We propose a process for generating the state vector of a given system architecture, based on a graph representation of the architecture’s model in Object-Process Methodology. A robust graph data structure that represents the model is queried to produce the architecture’s state space. The process facilitates analysis, reasoning, and model revision based on improved understanding of state vectors and state spaces. State attribute refinements within the model allow for model validity, viability, and reusability as the architecture evolves, with multiple agents, attributes, and attribute state values. The CSA approach advocates and facilitates careful, dynamic, and interactive state space exploration in lieu of exhaustive upfront enumeration of state space permutations. The results can be fed into other analysis, simulation, or visualization tools. We demonstrate CSA on driver assistance technology.

Reasons of the transformed toward NOSQL Databases and its data models

The relational database model is the main focus of the database for the previous time. It provides robust data storage and structure that supports transaction characteristics and data retrieval with structured query language- SQL. The emergence of web technologies and clustering technology in large servers called for the need to store unstructured data and move away from the pattern of tables and static fields and not topic to transaction conditions, especially in distributed systems. Instead, it used CAP theory. New data models are emergence including: Document Data model, Key-Value model, Column-Family model, and Graph model, And new programming languages that deal with these models. An asset inventory system is a model of systems that are not topic to a fixed structure. Each location in the organization has fairly different assets and assets in the same location have different specifications that are difficult to organize in the form of tables and columns. For this reason using of NOSQL CouchDB database system was adopted and the use of MAP-REDUCE technology to organize data display and Cloudant Query in Mango-view method to retrieve data from the inventory database.

Blockchain for Mobile Health Applications Acceleration with GPU Computing.

Blockchain is a linearly linked, distributed, and very robust data structure. Originally proposed as part of the Bitcoin distributed stack, it can be applied in a number of fields, most notably in smart contracts, social media, secure IoT, and cryptocurrency mining. It ensures data integrity by distributing strongly encrypted data in widely redundant segments. Each new insertion requires verification and approval by the majority of the users of the blockchain. Both encryption and verification are computationally intensive tasks which cannot be solved with ordinary off-the-shelf CPUs. This has resulted in a renewed scientific interest in secure distributed communication and coordination protocols. Mobile health applications are growing progressively popular and have the enormous advantage of timely diagnosis of certain conditions. However, privacy concerns have been raised as mobile health applications by default have access to highly sensitive personal data. This chapter presents concisely how blockchain can be applied to mobile health applications in order to enhance privacy.

Improving mine-to-mill by data warehousing and data mining

ABSTRACTMining is an interdisciplinary industry that utilises equipment and technology intensively in daily operations. Mine-to-Mill is considered as a key concept for metal mining recently. Impact of underperformed basic upstream operations such as drilling and blasting will sustain this inefficiency in downstream processes, such as mineral processing. Data provided for each of these operations from software and hardware utilised on field reached a level where advanced data analytics becomes applicable. Data warehousing and data mining are alternative tools that rely on a robust data structure. This study gives insight into a data-driven framework for modern mines and presents a data mining implementation on real-time mining-related data for prediction of blasting performance. Random forest and adaptive boosting algorithm were utilised on an integrated data warehouse to discover major operational parameters for efficient blasting. The implementation on site improved the performance of drilling and blasting. The variables highlighted as important by random forest and adaptive boosting algorithm directed the experts of mine-to-mill on site to focus on the close control and detailed analysis of certain drilling- and blasting-related parameters.

Adaptive parallel Delaunay triangulation construction with dynamic pruned binary tree model in Cloud

SummaryThe paper illustrates a parallel and distributed scheme for computing a planar Delaunay triangulation using a divide‐and‐conquer strategy in Cloud environment, which combines the incremental insertion algorithm and the divide‐and‐conquer method. The proposed hybrid algorithm for Delaunay triangulation construction is easy to be parallelized due to the dynamic pruned characteristic of the binary tree model used. Moreover, the Cloud platform decreases the communication overhead and improves data locality by making use of a data partitioning and integrating scheme offered by the map‐reduce architecture. The implementation of the parallel and distributed version of the algorithm relied on a robust data structure called quad‐edge, which implies the geometric relationship among the edges and vertexes adjacent. More importantly, the data are serialized easily and transmitted efficiently between different Cloud nodes; the algorithm is executed conveniently on PC clusters. We tested the parallel version of the algorithm on GeoKSCloud, a geographical knowledge service Cloud developed by our research team. Experimental results show that the proposed hybrid algorithm is efficient and competitive; it can be easily migrated and deployed in distributed and parallel computing environment, such as grid and Cloud. The parallel implementation of the hybrid algorithm has a good speed‐up, while data communication is the crucial factor for the efficiency of the parallel version. Overall, the parallel version outperforms both the sequential divide‐and‐conquer algorithm and the sequential incremental insertion algorithm.

Five-axis tool path generation in CNC machining of [formula omitted]-spline surfaces

Because of its flexible topology and robust data structure, the T-spline surface has become the trend of free-form surfaces representation in the realm of CAD design, animation and CAE. Yet its application in manufacturing has not been fully explored. In this work, the possibility of direct tool path generation on the T-spline surface has been discussed. An improved space-filling curve (ISFC) tool path planning algorithm has been proposed to exploit the advantage of T-spline as a mathematical representation of free-form surfaces in CAM process, as well as to overcome its disadvantages such as irregular boundaries and holes in the pre-image. The turning problem in traditional SFC has been tackled using Hermite compensation curves. Finally, a prototype system has been developed to implement the proposed algorithm and actual machining has been conducted. The result shows the feasibility as well as the efficiency of the proposed method for T-spline surfaces tool path generation compared to commercial CAM system.

Statistical algorithms for ontology-based annotation of scientific literature

BackgroundOntologies encode relationships within a domain in robust data structures that can be used to annotate data objects, including scientific papers, in ways that ease tasks such as search and meta-analysis. However, the annotation process requires significant time and effort when performed by humans. Text mining algorithms can facilitate this process, but they render an analysis mainly based upon keyword, synonym and semantic matching. They do not leverage information embedded in an ontology's structure.MethodsWe present a probabilistic framework that facilitates the automatic annotation of literature by indirectly modeling the restrictions among the different classes in the ontology. Our research focuses on annotating human functional neuroimaging literature within the Cognitive Paradigm Ontology (CogPO). We use an approach that combines the stochastic simplicity of naïve Bayes with the formal transparency of decision trees. Our data structure is easily modifiable to reflect changing domain knowledge.ResultsWe compare our results across naïve Bayes, Bayesian Decision Trees, and Constrained Decision Tree classifiers that keep a human expert in the loop, in terms of the quality measure of the F1-mirco score.ConclusionsUnlike traditional text mining algorithms, our framework can model the knowledge encoded by the dependencies in an ontology, albeit indirectly. We successfully exploit the fact that CogPO has explicitly stated restrictions, and implicit dependencies in the form of patterns in the expert curated annotations.

Greatest 'HITS': A new tool for tracking impacts at the National Institute of Environmental Health Sciences

Evaluators of scientific research programs have several tools to document and analyze products of scientific research, but few tools exist for exploring and capturing the impacts of such research. Understanding impacts is beneficial because it fosters a greater sense of accountability and stewardship for federal research dollars. This article presents the High Impacts Tracking System (HITS), a new approach to documenting research impacts that is in development at the National Institute of Environmental Health Sciences (NIEHS). HITS is designed to help identify scientific advances in the NIEHS research portfolio as they emerge, and provide a robust data structure to capture those advances. We have downloaded previously un-searchable data from the central NIH grants database and developed a robust coding schema to help us track research products (going beyond publication counts to the content of publications) as well as research impacts. We describe the coding schema and key system features as well as several development challenges, including data integration, development of a final data structure from three separate ontologies, and ways to develop consensus about codes among program staff.

The phenotype and genotype experiment object model (PaGE-OM): a robust data structure for information related to DNA variation

Torrents of genotype-phenotype data are being generated, all of which must be captured, processed, integrated, and exploited. To do this optimally requires the use of standard and interoperable "object models," providing a description of how to partition the total spectrum of information being dealt with into elemental "objects" (such as "alleles," "genotypes," "phenotype values," "methods") with precisely stated logical interrelationships (such as "A objects are made up from one or more B objects"). We herein propose the Phenotype and Genotype Experiment Object Model (PaGE-OM; www.pageom.org), which has been tested and implemented in conjunction with several major databases, and approved as a standard by the Object Management Group (OMG). PaGE-OM is open-source, ready for use by the wider community, and can be further developed as needs arise. It will help to improve information management, assist data integration, and simplify the task of informatics resource design and construction for genotype and phenotype data projects.

A general approach to the identification of compensated faults in robust data structures

In this paper we give an optimal algorithm for the identification of faulty attributes in a robust data structure. The algorithm does not use any fault syndrome table since the size of such a table could be large, particularly when faults can compensate one another arbitrarily. The data structure is viewed as a collection of data elements related via some “attributes.” The relationships are specified by a set of axioms in first order logic. Faults in attributes invalidate some of the axioms. The invalidated axioms are used to identify the faulty attributes. We show that the identification is possible in time proportional to the number of axioms even when faults compensate one another arbitrarily. This is optimal, since our method of axiom generation does not yield any redundant axioms.

Synthesizing robust data structures-an introduction

A formal approach is presented for the analysis and synthesis of robust data structures. The entire data structure is viewed as a collection of data elements related via some attributes. The relationships are specified by a set of axioms in first-order logic. Faults in attributes invalidate some of the axioms. The invalidated axioms are used to detect and correct the faulty attributes. The authors show how detection and correction can be localized to small portions of the data structure, thereby allowing concurrent repair in several disjoint portions. This property makes local correction attractive for B-trees and other structures used in databases. They then show how the ideas developed for attaining structural integrity can be applied to achieve data integrity as well.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Experimenting with data structures

AbstractResearch in robust data structures can be done both by theoretical analysis of properties of abstract implementations and by empirical study of real implementations. Empirical study requires a support environment for the actual implementation. In particular, if the response of the implementation to errors is being studied, a mechanism must exist for artificially injecting appropriate kinds of errors. This paper discusses techniques used in empirical investigations of data structure robustness, with particular reference to tools developed for this purpose at the University of Waterloo.