Designing a Secure and Scalable Data Sharing Mechanism Using Decentralized Identifiers(DID)

Centralized storage and identity identification methods pose many risks,including hacker attacks,data misuse,and single points of failure.Additionally,existing centralized identity management methods face interoperability issues and rely on a single identity provider,leaving users without control over their identities.Therefore,this paper proposes a mechanism for identity identification and data sharing based on decentralized identifiers.The scheme utilizes blockchain technology to store the identifiers and data hashed on the chain to ensure permanent identity recognition and data integrity.Data is stored on InterPlanetary File System(IPFS)to avoid the risk of single points of failure and to enhance data persistence and availability.At the same time,compliance with World Wide Web Consortium(W3C)standards for decentralized identifiers and verifiable credentials increases the mechanism’s scalability and interoperability.

IDentifier DNA分型盒(炎黄34)的法医学验证及应用评估

目的 测试IDentifier DNA分型盒(炎黄34)的技术性能指标,评估其法医学应用价值。方法 根据《法庭科学人类荧光标记STR复合扩增检测试剂质量基本要求》(GB/T 37226—2018),从种属特异性、分型准确性、灵敏度、适应性、耐受性、一致性、均衡性、反应条件验证、混合样本、稳定性、批间差11个方面对IDentifier DNA分型盒(炎黄34)进行测试。比较IDentifier DNA分型盒(炎黄34)与Power Plex~?Fusion 6C系统、Versa Plex~?27PY系统、Veri Filer~(TM) Plus PCR扩增试剂盒的系统效能。使用IDentifier DNA分型盒(炎黄34)检测日常案件中的拭子类生物检材,观察其STR检验结果 。结果 IDentifier DNA分型盒(炎黄34)具有良好的种属特异性、分型准确性、适应性、耐受性和均衡性,灵敏度可达0.062 5 ng,能检测案件中不同类型的检材、降解检材及混有抑制剂的检材,对混合比例为4∶1以内的样本均能获得完整分型。该试剂盒的系统效能非常高,TDP可达1-1.08×10~(-37),CPE_(trio)和CPE_(duo)分别可达1-5.47×10~(-14)和1-6.43×10~(-9)。针对案件中的接触类生物检材,其有效检出率可达21.05%。针对亲缘关系鉴定,单亲鉴定和全同胞鉴定的系统效能均得到了有效提高。结论 IDentifier DNA分型盒(炎黄34)的上述性能指标符合要求,可用于个体识别及亲权鉴定,适合在法庭科学领域应用。

DC中Identifier项的模式体系标准化探析

网络时代 ,元数据 DC是组织网络信息资源的重要手段 ,为了增强互操作性其元素项的限定是必要的。本文在分析了 DC中 Identifier项的模式体系的基础上 ,探讨了 URI、URL 和 URN三者之间的关系 ,认为 U RN将成为该项模式体系的国际标准。

Adaptive Control of a Two-Link Robot Using Batch Least-Square Identifier

We design a regulation-triggered adaptive controller for robot manipulators to efficiently estimate unknown parameters and to achieve asymptotic stability in the presence of coupled uncertainties.Robot manipulators are widely used in telemanipulation systems where they are subject to model and environmental uncertainties.Using conventional control algorithms on such systems can cause not only poor control performance,but also expensive computational costs and catastrophic instabilities.Therefore,system uncertainties need to be estimated through designing a computationally efficient adaptive control law.We focus on robot manipulators as an example of a highly nonlinear system.As a case study,a 2-DOF manipulator subject to four parametric uncertainties is investigated.First,the dynamic equations of the manipulator are derived,and the corresponding regressor matrix is constructed for the unknown parameters.For a general nonlinear system,a theorem is presented to guarantee the asymptotic stability of the system and the convergence of parameters'estimations.Finally,simulation results are discussed for a two-link manipulator,and the performance of the proposed scheme is thoroughly evaluated.

Analysis of identifiers in IoT platforms

The Internet of Things(IoT)provides new opportunities for different IoT platforms connecting various devices together.The need to identify those devices is the foremost important to perform any kind of operation.Many organizations and standard bodies that provide specifications and frameworks for the IoT currently have their own identification mechanisms.Some existing industrial identification mechanisms can also be used in the IoT.There is no common Identification Scheme(IS)for the IoT as yet,because of the political and commercial differences amongst the standard bodies.The unavailability of a unified IS method makes the inter-working among IoT platforms challenging.This paper analyses and compares ISs used by several selected IoT platforms.This work will help in understanding the need for a common identification mechanism to provide inter-working among different IoT platforms.

Sliding mode identifier for parameter uncertain nonlinear dynamic systems with nonlinear input

This paper presents a sliding mode(SM) based identifier to deal with the parameter idenfification problem for a class of parameter uncertain nonlinear dynamic systems with input nonlinearity. A sliding mode controller (SMC) is used to ensure the global reaching condition of the sliding mode for the nonlinear system;an identifier is designed to identify the uncertain parameter of the nonlinear system. A numerical example is studied to show the feasibility of the SM controller and the asymptotical convergence of the identifier.

Scalable and Identifier/Locator-Splitting Routing Protocol for Mobile Ad Hoc Networks

In the traditional Internet Protocol (IP) architecture, there is an overload of IP semantic problems. Existing solutions focused mainly on the infrastructure for the fixed network, and there is a lack of support for Mobile Ad Hoc Networks (MANETs). To improve scalability. a routing protocol for MANETs is presented based on a locator named Tree-structure Locator Distance Vector (TLDV). The hard core of this routing method is the identifier/locator split by the Distributed Hash Table (DHT) method, which provides a scalable routing service. The node locator indicates its relative location in the network and should be updated whenever topology changes. Locator space is organized as a tree-structure, and the basic routing operation of the TLDV protocol is presented. TLDV protocol is compared to some classical routing protocols for MANETs on the NS2 platform. Results show that TLDV has better scalability.

Ai2NU: A Cytoscape plugin for uniformization of biomolecular accession identifiers

Cytoscape is one of the most popular platforms for biomolecular networks research. However Cytoscape cannot display biomolecular names according to their accession identifiers in different databases. A plugin named Ai2NU is designed and implemented in this paper. It can make biomolecular names displayed automatically in biomolecular networks graphs in Cytoscape by constructing a local dictionary. It is convenient for researchers to recognize biomolecules and enhance the research efficiency.

FUZZY IDENTIFIER WITH EXPONENTIAL RATE OF CONVERGENCE FOR NONLINEAR DYNAMIC SYSTEMS

In this paper, fuzzy systems are used as identifiers for unknown nonlinear dynamic systems. The fuzzy identifier can incorporate linguistic knowledge of nonlinear dynamic systems with input-output pairs directly into the design. In the case where there is the modelling error, a new identification algorithm is proposed. It is proved that the fuzzy identifier is globally stable and the identification error converges to zero exponentially fast.

An Identifier-Based Network Access Control Mechanism Based on Locator/Identifier Split

Legacy IP address-based access control has met many challenges, because the network nodes cannot be identified accurately based on their variable IP addresses. “Locator/Identifier Split” has made it possible to build a network access control mechanism based on the permanent identifier. With the support of “Locator/Identifier Split” routing and addressing concept, the Identifier-based Access Control (IBAC) makes net-work access control more accurate and efficient, and fits for mobile nodes’ access control quite well. Moreover, Self-verifying Identifier makes it possible for the receiver to verify the packet sender’s identity without the third part authentication, which greatly reduces the probability of “Identifier Spoofing”.

Utilizing Geospatial Tools to Assign Unique Identifiers to Water Bodies in a Low-Income Country

Inland waters support the growth of several sectors including mining, agriculture, and health. This makes it crucial to have sustainable quantity and quality through conservation practices. Achieving sustainability requires information on the spatial distribution of water bodies. This requirement is particularly critical in low-income nations where dependence on natural resources is a key driver to economic growth. Unfortunately, these nations lack the resources to promote costly waterbody characterization. This study pre-sents a cost-effective approach in assigning Unique Identifiers (UIDs) that define locations and characteristics of rivers and streams. Our objective is to develop a scheme that can be used to identify and characterize rivers and streams in a nation. We utilized an open-source Digital Elevation Model (DEM) of NASA’s ASTER satellite and the hydrology tool in ArcGIS 10.7.1. The DEM was imported to ArcGIS followed by delineation of hydrologic regions, subregions, and stream orders. Each stream segment was given a UID based on its region and Strahler’s stream order system. We present a case study analysis for two regions within Sierra Leone using water quality data of selected rivers and streams. These will lay the foundation for a nationwide coding exercise and provide a useful reference for water resource practitioners.

Conditions of Non-Unique Identifiers in Record Linkage Using Japanese Cohort Dataset

The applications of unique identifiers such as name, home address and social security number to link different datasets have been commonly used and well-published. Also, the theoretical concepts of probabilistic algorithm in record linkage have been well-defined in the literature. However, few studies have reported the applications of its probabilistic algorithm using non-unique identifiers. In this paper, we investigate several variables (weight, height, waist, age, sex, smoking and alcohol habit) as non-unique identifiers using Japanese cohort dataset with three-year baseline of 1989-1991 to observe how effectively these identifiers can be used and what influence those may have on record linkage. Moreover, we modify the conditions of these identifiers and estimate the sensitivity, specificity and accuracy for comparison. We further investigate this by using extended ten-year baseline of 1989-1999 as well. As a result, we conclude that the combination of age, sex, weight and height predicts better estimation with regards to the sensitivity, specificity and accuracy than other combinations in both men and women in case of using three-year baseline, whereas the combination of age, sex and height predicts better in both men and women in case of using ten-year baseline.

Exponential Continuous Non-Parametric Neural Identifier With Predefined Convergence Velocity

This paper addresses the design of an exponential function-based learning law for artificial neural networks(ANNs)with continuous dynamics.The ANN structure is used to obtain a non-parametric model of systems with uncertainties,which are described by a set of nonlinear ordinary differential equations.Two novel adaptive algorithms with predefined exponential convergence rate adjust the weights of the ANN.The first algorithm includes an adaptive gain depending on the identification error which accelerated the convergence of the weights and promotes a faster convergence between the states of the uncertain system and the trajectories of the neural identifier.The second approach uses a time-dependent sigmoidal gain that forces the convergence of the identification error to an invariant set characterized by an ellipsoid.The generalized volume of this ellipsoid depends on the upper bounds of uncertainties,perturbations and modeling errors.The application of the invariant ellipsoid method yields to obtain an algorithm to reduce the volume of the convergence region for the identification error.Both adaptive algorithms are derived from the application of a non-standard exponential dependent function and an associated controlled Lyapunov function.Numerical examples demonstrate the improvements enforced by the algorithms introduced in this study by comparing the convergence settings concerning classical schemes with non-exponential continuous learning methods.The proposed identifiers overcome the results of the classical identifier achieving a faster convergence to an invariant set of smaller dimensions.

Identifier Migration for Identity Continuance in Single Sign-On

Single sign-on (SSO) is an identity management technique that provides the ability to use multiple Web services with one set of credentials. However, when the authentication server is down or unavailable, users cannot access these Web services, regardless of whether they are operating normally. Therefore, it is important to enable continuous use alongside SSO. In this paper, we present an identity continuance method for SSO. First, we explain four such continuance methods and identify their limitations and problems. Second, we propose a new solution based on an identifier migration approach that meets the requirement for identity continuance. Finally, we discuss these methods from the viewpoint of continuity, security, efficiency, and feasibility.

Identifier Management of Industrial Internet Based on Multi-Identifier Network Architecture

The industrial Internet realizes intelligent control and optimized operation of the industrial system through network interconnection.The industrial Internet identifier is the core element to accomplish this task.The traditional industrial Internet identifier resolution technologies depend excessively on IP networks,and cannot meet the requirements of ubiquitous resource-restraint Internet of Things(IoT)devices.An industrial Internet identifier resolution management strategy based on multi-identifier network architecture is proposed in this paper,which supports content names,identities,locations,apart from the traditional IP address.The application of multiple types of identifiers not only solves the problem of IP addresses exhaustion,but also enhances the security,credibility,and availability of the industrial Internet identification resolution system.An inter-translation scheme between multiple identifiers is designed to support multiple identifiers and the standard ones.We present an addressing and routing algorithm for identifier resolution to make it convenient to put our strategy into practice.

Overall-Transparent Dynamic Identifier-Mapping Mechanism Against Scanning and Worm Propagation in the SINET

Static assignment of IP addresses or identifiers can be exploited by an adversary to attack a network. However, existing dynamic IP address assignment approaches suffer from two limitations, namely: participation of terminals in the assignment and inadequate network server management. Thus, in this paper, we propose an Overall-transparent Dynamic Identifier-mapping Mechanism(ODIM) to manage the identifier of network nodes to defend against scanning and worm propagation in the Smart Identifier NETwork(SINET). We establish the selection and allocation constraints, and present selection and allocation algorithms to determine the constraints. The non-repetition probability and cover cycle allow us to evaluate the defense efficiency against scanning. We propose the probability for routing identifiers and derive the defense efficiency of ODIM against worm propagation. Simulation results and theoretical analysis show that the proposed method effectively reduces the detection probability of Routing IDentifiers(RIDs) and thus improves defense capabilities against worm propagation.

Application of Industrial Internet Identifier in Optical Fiber Industrial Chain

The industrial Internet has germinated with the integration of the traditional industry and information technologies.An identifier is the identification of an object in the industrial Internet.The identifier technology is a method to validate the identification of an object and trace it.The identifier is a bridge to connect information islands in the industry,as well as the data basis for building a technology application ecosystem based on identifier resolution.We propose three practical applications and application scenarios of the industrial Internet identifier in this paper.Future applications of identifier resolution in the industrial Internet field are also presented.

Construction and Application of Identifier Resolution in Automotive Industrial Internet

Identifier resolution system in the automotive industrial Internet is necessary for building a fully interconnected infrastructure with people,machines,factories,products and clients.The resolution system can not only ensure the comprehensive interconnection and efficiency of research and development,procurement,production,sales,and after-sales service in automotive industry,but also promote the integration of automotive industrial data,which facilitates the integrated development of traditional automotive manufacturing and the industrial Internet.This paper focuses on processes and methods of building identifier resolution system for the automotive industry and summarizes the construction and development of secondary node in the automotive industrial Internet in order to explore a suitable road to a rich and completed application ecosystem.

A Novel Method in Wood Identification Based on Anatomical Image Using Hybrid Model

Nowadays,wood identification is made by experts using hand lenses,wood atlases,and field manuals which take a lot of cost and time for the training process.The quantity and species must be strictly set up,and accurate identification of the wood species must be made during exploitation to monitor trade and enforce regulations to stop illegal logging.With the development of science,wood identification should be supported with technology to enhance the perception of fairness of trade.An automatic wood identification system and a dataset of 50 commercial wood species from Asia are established,namely,wood anatomical images collected and used to train for the proposed model.In the convolutional neural network(CNN),the last layers are usually soft-max functions with dense layers.These layers contain the most parameters that affect the speed model.To reduce the number of parameters in the last layers of the CNN model and enhance the accuracy,the structure of the model should be optimized and developed.Therefore,a hybrid of convolutional neural network and random forest model(CNN-RF model)is introduced to wood identification.The accuracy’s hybrid model is more than 98%,and the processing speed is 3 times higher than the CNN model.The highest accuracy is 1.00 in some species,and the lowest is 0.92.These results show the excellent adaptability of the hybrid model in wood identification based on anatomical images.It also facilitates further investigations of wood cells and has implications for wood science.