Related Schemes Research Articles

New implicit time integration schemes combining high frequency damping with high second order accuracy

It is shown that weighted linear combinations of the generalised-α method and certain related higher order schemes allow for the formulation of unconditionally stable single step time integration methods with improved second order accuracy and more targeted high-frequency damping. It is also shown that, if the user controlled high frequency damping parameter ρ∞ is set to zero, the new schemes can be expressed as linear multistep backward difference formulae and, in a particular case, recover Park's method. The performance of the proposed methods is illustrated in terms of mathematical analysis and a number of linear and nonlinear numerical examples including finite element based solutions of the incompressible Navier-Stokes equations.

How does the supervisor–subordinate relational schema shape employee voice behaviour in China?

Previous research has shown that the relationship between a supervisor and a subordinate is crucial in shaping employee voice behaviour. However, where these relationships originate needs to be clarified. Studies on relational schemas suggest that the quality of these relationships depends on the relational schemas held by both leaders and subordinates. Unfortunately, these two research areas have developed independently and have yet to intersect, which limits our understanding of how social interactions between leaders and subordinates impact employee voice behaviour. Based on research from both domains, the current study examines how the supervisor–subordinate relational schema affects employees' voice behaviour. Our proposal, grounded in complexity theory, suggests that the expectations of both leaders and subordinates about their relationship model determine whether employees choose to voice their opinions to their leaders. Analyses of data from a survey of 416 matched pairs of leaders and subordinates in China conducted using fsQCA and LPA largely support our view. The results indicate that the configuration of the supervisor–subordinate relational schema among leaders and subordinates can predict the level of employee voice behaviour. Additionally, this configuration is a natural subset of the population. These findings have important implications for improving employee voice behaviour in organizations.

FastFaceCLIP: A lightweight text‐driven high‐quality face image manipulation

AbstractAlthough many new methods have emerged in text‐driven images, the large computational power required for model training causes these methods to have a slow training process. Additionally, these methods consume a considerable amount of video random access memory (VRAM) resources during training. When generating high‐resolution images, the VRAM resources are often insufficient, which results in the inability to generate high‐resolution images. Nevertheless, recent Vision Transformers (ViTs) advancements have demonstrated their image classification and recognition capabilities. Unlike the traditional Convolutional Neural Networks based methods, ViTs have a Transformer‐based architecture, leverage attention mechanisms to capture comprehensive global information, moreover enabling enhanced global understanding of images through inherent long‐range dependencies, thus extracting more robust features and achieving comparable results with reduced computational load. The adaptability of ViTs to text‐driven image manipulation was investigated. Specifically, existing image generation methods were refined and the FastFaceCLIP method was proposed by combining the image‐text semantic alignment function of the pre‐trained CLIP model with the high‐resolution image generation function of the proposed FastFace. Additionally, the Multi‐Axis Nested Transformer module was incorporated for advanced feature extraction from the latent space, generating higher‐resolution images that are further enhanced using the Real‐ESRGAN algorithm. Eventually, extensive face manipulation‐related tests on the CelebA‐HQ dataset challenge the proposed method and other related schemes, demonstrating that FastFaceCLIP effectively generates semantically accurate, visually realistic, and clear images using fewer parameters and less time.

Blockchain-Based Lightweight Message Authentication for Edge-Assisted Cross-Domain Industrial Internet of Things

In edge-assisted cross-domain Industrial Internet of Things (IIoT), blockchain-based authentication is an effective way to build cross-domain trust and secure cross-domain data. However, existing authentication schemes still have serious challenges in terms of efficiency and security. In this paper, we propose a blockchain-based lightweight message authentication scheme. First, to address efficiency challenges, we build a blockchain-enabled edge-assisted lightweight authentication framework. This framework uses edge servers to assist smart devices in achieving cross-domain authentication and effectively reduce redundant interactions between entities. Second, to resolve the security challenges, we design a lightweight message authentication algorithm for cross-domain IIoT. The algorithm guarantees message security with low computational overhead and is suitable for multi-receiver cross-domain IIoT. The security proof and analysis demonstrate that the proposed scheme is secure under the random oracle model and can resist various attacks. The performance evaluation shows that our proposed scheme is superior in terms of computation and communication overhead when compared with other related schemes.

Secure data sharing scheme with privacy-preserving and certificateless integrity auditing in cloud storage

With cloud storage services becoming wildspread and low-cost, individuals and organizations select outsourcing large amounts of document to cloud. Cloud storage applications provide data sharing services with varying functionality. In some cloud storage systems that provide data sharing, such as e-health systems, hiding sensitive information fields is a necessity. Meanwhile, in sharing process, unauthorized entities may have access to these privacy fields. The resource-constrained computation capability for data owner (DO) is universal. To tackle above problems, we propose a sanitizer-based secure data sharing scheme where sanitizer performs most computations of encryption and signature after receiving the partially blinded data from DO. It also checks the legitimacy of visitors before CSP returns stored data file. Besides, adopting an attribute-based access policy guarantees further privacy and fine-grained access authorization. After verification, cloud will return the required data to the legitimate visitors. Moreover, integrity auditing to shared data file is based on certificateless authentication technique, which removes certificates issue of traditional cryptographic technique, avoids key-escrow attack risk caused by identity-based cryptography. Finally, the performance analysis and experimental results show that our proposed scheme is competitive in practical applications since it reduces the authenticator generation overhead by up to 21.6% and proof generation overhead by up to 12% compared with related schemes.

PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks

A secure and effective authentication and communication scheme between users and underwater sensors plays an important role in improving the detection and utilization of marine resources in underwater acoustic networks (UANs). However, due to the energy limitations and susceptibility to capture of underwater sensors and gateways, it is necessary to design a lightweight authentication protocol that can resist capture of sensors and gateways during attacks. In this paper, a lightweight authentication protocol for UANs based on the Physical Unclonable Function (PUF) and chaotic map is proposed. We used the advantages of PUF to resist sensors and gateways being captured in attacks and the chaotic map to achieve lightweight authentication because the computational cost of the chaotic map is almost one-third that of Elliptic Curve Cryptography (ECC). Additionally, we used the formal security proof in the random oracle model to prove the security of the proposed scheme. Our scheme was more secure and efficient compared with some other related schemes in terms of security and performance requirements, and the proposed scheme is suitable for UANs.

Comparison Analysis of Bio-Inspired Tendon-Driven Manipulators Based on Their Tension-Feasible Workspace

Abstract Bio-inspiration can be used in the development of novel dextrous and energy-efficient manipulators. This paper focuses on planar manipulators inspired by the bird neck, built upon stacking a series of tensegrity X-joints. The manipulators are actuated with four tendons and have different numbers of modules, with or without offsets. The objective of this work is to study the influence of offsets, number of modules, geometry of the joints, configuration at rest of the manipulators, and actuation scheme on the size of the tension-feasible workspace (TFW). The spring constants of the X-joints are determined so that the configuration at rest features a desired end-effector (EE) pose with minimal stiffness to ensure stability. Our study demonstrates that increasing the number of modules results in a larger TFW, although the number of active tendons is fixed. We find that the TFW can be maximized with appropriate modification of the joint geometry. Additionally, we explore the influence of bio-inspired approaches on the manipulator configuration at rest and actuation scheme in relation to the TFW. In addition, we conduct an analysis of the EE pose stiffness, revealing that offsets decrease this stiffness, while an optimal number of modules exists to achieve maximum stiffness. We observed that increasing the width of the manipulator generally enhances stiffness, while the configuration at rest and the actuation have little effects. Furthermore, experiments were conducted to validate the methodologies.

A Privacy-Preserving V2I Fast Authentication Scheme in VANETs

Due to the characteristics of high-speed movement of vehicles, authentication between vehicles and roadside units (RSUs) needs to be performed quickly. Vehicles can obtain the authentication information of the relevant RSUs from the certification authority (CA) in advance through route planning. Fast authentication can be performed when the vehicle enters the RSU range. However, in most of the current vehicle-to-infrastructure (V2I) fast authentication schemes, when the vehicle requests the authentication information of an RSU from the CA, the vehicle often needs to provide the CA with the travel path information, which leads to the CA easily obtaining the travel path of the vehicle. In addition, the CA encrypts the private keys of RSUs and sends them to vehicles as authentication keys, and the vehicles can obtain the private key information of RSUs directly after decryption. Once the private keys of RSUs are leaked, vehicular ad hoc networks (VANETs) can be attacked by malicious access. In order to protect the confidentiality of RSU private keys and the route privacy of vehicles, we propose a privacy-preserving V2I fast authentication scheme in VANETs. The scheme realizes the confidentiality of RSU private keys and the route privacy protection of vehicles by improving the oblivious transfer (OT) algorithm. Security analysis proves that our scheme has good privacy and attack resistance. Finally, performance evaluation shows that the proposed scheme performs better in terms of computational overhead and communication overhead compared to related schemes.

Empirical models and artificial intelligence for estimating hourly diffuse solar radiation in the state of Alagoas, Northeastern Brazil

Diffuse solar irradiation (HD) data are essential for the design and management of photovoltaic solar systems, biosphere-atmosphere modeling, and other applications. However, HD observations are scarce in several locations, especially in tropical regions. Employing hourly diffuse solar irradiation (HDh) and global solar irradiation (HGh) data collected between 2002─2003 and 2007─2008 in Alagoas State, Northeast Brazil, this study assesses various modeling techniques. Empirical models, including third-degree polynomial, logistic, sigmoidal, and rational functions, were compared with AI methods such as artificial neural networks (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Additionally, it explores how solarimetric and meteorological variables impact the performance of these models. The empirical models showed similar performance in estimating KDh(=HDh/HGh) (r2 > 0.726, modified Willmott – dm > 0.704, and RMSD < 0.103), with the third-degree polynomial model standing out. The empirical models had difficulty estimating KDh for hourly atmospheric transmittance (KTh) > 0.80, which indicated that they are not able to adequately simulate clear sky conditions, mostly due to surface reflections and clouds at the end of the day. ANN (r2 > 0.718, dm > 0.702, and RMSD < 0.105) showed better precision and accuracy of estimates for a greater number of schemes in relation to SVM and ANFIS (r2 > 0.704, dm > 0.699, RMSD < 0.108) and to empirical models. AI methods were able to represent the complexity of these conditions, with overall performance in estimating KDh superior or equivalent to empirical models. This study underscores the significance of exploring diverse methods for HD estimation, demonstrating promising potential for accurate and reliable estimation of hourly diffuse solar irradiation.

APPOINTMENT MAKER USING COMPUTERIZED VOICE

The Appointment Maker Using Computerized Voice is a novel system designed to streamline the process of scheduling appointments in healthcare settings through the integration of voice recognition and natural language processing technologies. By leveraging speech-to-text and text-to-speech capabilities, the system enables users to interact with the appointment booking interface using spoken commands, eliminating the need for manual input and enhancing accessibility for individuals with diverse technological proficiencies. This intuitive interface simplifies the appointment scheduling process, allowing users to book, reschedule, or cancel appointments with ease. Furthermore, the Appointment Maker employs machine learning models to understand and interpret user queries, providing accurate responses and assistance in real-time. By leveraging pre-trained language models and fine-tuning them on domain-specific data, the system demonstrates robust performance in understanding diverse user inputs and generating contextually relevant responses. This capability enhances user experience by providing personalized and timely assistance, akin to interacting with a human receptionist.The Appointment Maker features a backend database for storing and managing appointment and user information, ensuring data integrity, security, and efficient resource utilization. The relational database schema facilitates seamless coordination between users, healthcare providers, and administrative staff, enabling efficient scheduling and management of appointments. Overall, the Appointment Maker Using Computerized Voice represents a significant advancement in healthcare administration, offering a user-friendly and efficient solution for appointment scheduling while improving accessibility and patient experience. Key Words: Computerized Voice ,Natural Language Understanding (NLU), Speech to Text, Text to Speech.

Pillar data-acquisition strategies for cryo-electron tomography of beam-sensitive biological samples.

For cryo-electron tomography (cryo-ET) of beam-sensitive biological specimens, a planar sample geometry is typically used. As the sample is tilted, the effective thickness of the sample along the direction of the electron beam increases and the signal-to-noise ratio concomitantly decreases, limiting the transfer of information at high tilt angles. In addition, the tilt range where data can be collected is limited by a combination of various sample-environment constraints, including the limited space in the objective lens pole piece and the possible use of fixed conductive braids to cool the specimen. Consequently, most tilt series are limited to a maximum of ±70°, leading to the presence of a missing wedge in Fourier space. The acquisition of cryo-ET data without a missing wedge, for example using a cylindrical sample geometry, is hence attractive for volumetric analysis of low-symmetry structures such as organelles or vesicles, lysis events, pore formation or filaments for which the missing information cannot be compensated by averaging techniques. Irrespective of the geometry, electron-beam damage to the specimen is an issue and the first images acquired will transfer more high-resolution information than those acquired last. There isalso an inherent trade-off between higher sampling in Fourier space and avoiding beam damage to the sample. Finally, the necessity of using a sufficient electron fluence to align the tilt images means that this fluence needs to be fractionated across a small number of images; therefore, the order of data acquisition is also a factor to consider. Here, an n-helix tilt scheme is described and simulated which uses overlapping and interleaved tilt series to maximize the use of a pillar geometry, allowing the entire pillar volume to be reconstructed asa single unit. Three related tilt schemes are also evaluated that extend the continuous and classic dose-symmetric tilt schemes for cryo-ET to pillar samples to enable the collection of isotropic information across all spatial frequencies. Afourfold dose-symmetric scheme is proposed which provides a practical compromise between uniform information transfer and complexity of data acquisition.

Design of Secure and Privacy-Preserving Data Sharing Scheme Based on Key Aggregation and Private Set Intersection in Medical Information System

Medical data sharing is pivotal in enhancing accessibility and collaboration among healthcare providers, researchers, and institutions, ultimately leading to enhanced patient outcomes and more efficient healthcare delivery. However, due to the sensitive nature of medical information, ensuring both privacy and confidentiality is paramount. Access control-based data sharing methods have been explored to address these issues, but data privacy concerns still remain. Therefore, this paper proposes a secure and privacy-preserving data sharing scheme that achieves an equilibrium between data confidentiality and privacy. By leveraging key aggregate encryption and private set intersection techniques, our scheme ensures secure data sharing while protecting against the exposure of sensitive information related to data. We conduct informal and formal security analyses, including Burrow–Abadi–Needham logic and Scyther, to demonstrate its resilience against potential adversarial attacks. We also implement the execution time for cryptographic operations using multiprecision integer and a rational arithmetic cryptographic library and perform comparative analysis with existing related schemes in terms of security, computational cost, and time complexity. Our findings demonstrate a high level of security and efficiency, demonstrating that the proposed scheme contributes to the field by providing a solution that protects data privacy while enabling secure and flexible sharing of medical data.

Auditor's professional judgment and opinion

The paper is devoted to such an actual problem in the field of audit activity as professional judgment and an auditor's opinion. A proper clarification of the logical content of these concepts significantly affects the effectiveness of the audit according to requirements of International Standards on Auditing (ISA). To date, in scientific publications the various approaches to the interpretation of the given concepts are expressed. A significant part of the authors is limited to general definitions and only some of them make attempts to substantiate their research with the laws and categories of logic. At the same time, native authors in every possible way avoid a full-fledged logical definition of content of the concept "auditor's opinion". The main reason is that there is an unsuccessful translation from English terminology into Ukrainian. In some cases, the term "opinion" has been translated as "opinion", and in others it has been called "conclusion". Therefore, if the term "opinion" is preferred, it turns out that the audit begins with an auditor's opinion and ends with it. An auditor's opinion fully encompasses such forms of logical thinking as concepts, judgments and inferences. The paper not only shows the misinterpretation of the concepts of "professional judgment" and "auditor's opinion" in a number of scientific studies. Also it is proposed the own vision of the cognition process in the auditing field based on the laws of general and dialectical logic. A related scheme of an auditor's professional judgment is given. From this scheme it can be seen that the opinion of a professionally competent auditor from the very beginning of the audit is being transformed through rationale into the concepts and requirements of the ISA. Further, an auditor's rationale is being transformed into judgments and conclusions. Based on them, as a result of proving or proving and planning, the specific conclusions of an auditor are formed as a report. The paper states that an auditor's professional judgment, like the professional judgment of other specialists, is subordinated to the general laws of logic. Its specificity lies in the fact that it is carried out with professional skepticism in conditions of uncertainty and risk and is subject to the requirements of legislation and ISA.

Mapping method for complex mechatronic equipment relationship data to contextual semantic-constrained knowledge graphs

Complex mechatronic equipment’s complete life cycle information model includes multidimensional data on structure, qualities, behavior, and limitations. The data are diverse, derived from multiple sources, and include both streaming and non-streaming cyber-physical data. The product data system’s structured relational model, which is represented by the XBOM (X Bill of Material), is primarily where this data is stored. However, with the growing amount of equipment information, this relatively isolated and redundant architecture poses challenges. Within relational models, the growing intricacy of inner-outer key joins impedes effective data mining and retrieval for dynamic digital twin business applications. In response, this study proposes a knowledge graph transformation method that takes into account the semantic restrictions of the business application context to translate relational data to entities and semantic expressions. The method builds a knowledge context constraint meta-model by classifying data according to context semantics into one-hop entities, multi-hop entities, and abstract classes. Additionally, an ontology-based data extraction transformation method and an ontology-based mapping algorithm based on relational schema transformation are introduced in this article. This strategy significantly minimizes knowledge redundancy and enhances retrieval performance, as demonstrated by experimental verification through the development of a relational data mapping tool software.

A secure and efficient authentication key agreement scheme for industrial internet of things based on edge computing

Industrial Internet of Things (IIoT) is a pivotal driving force behind the intelligent transformation of the global industrial system, bringing about a gradual shift in production methods. However, due to users completing remote access and data transmission through open channels, ensuring user legitimacy and data security has become an important factor in IIoT. In addition, resource limited users or devices need to collect and publish corresponding messages while also timely processing instant messages obtained from the network. Leveraging edge computing technology effectively mitigates the transmission time of messages, addressing the challenges of high computing pressure and prolonged response time associated with cloud server computing. Therefore, investigating authentication key agreement based on edge computing in the IIoT environment holds substantial theoretical significance and practical importance. This article introduces an IIoT authentication that ensures both security and efficiency by integrating elliptic curve cryptography and three-factor authentication. Within this scheme, the user and the edge server accomplish security authentication. Through security analysis, the proposed scheme is proven to meet the essential security requirements. Performance analysis shows that it achieves higher security, supports more functions, and achieves lower computational and communication costs compared to related schemes.

RSDA: A Reliable and Secure Data Aggregation Scheme for Smart Grid

Smart grids are modern power distribution systems that utilize advanced technologies to optimize energy generation, distribution, and consumption. The huge amount of data generated by various sources, such as smart meters, sensors, and other devices, can be aggregated for efficient energy management, supporting demand response etc. However, this data can contain sensitive information about individual’s energy usage patterns, behaviour, and preferences, which raises several privacy concerns. Data reliability is another major issue, as the data send from the smart meters to the aggregators may get corrupted due to errors introduced by the communication channel. In this paper, a private-key additive homomorphic encryption scheme based on polar codes is proposed for privacy preserving data aggregation. The proposed scheme not only ensures data confidentiality but also provide data reliability. The security of this scheme relies on the difficulty in decoding the codeword and is achieved by embedding security in the generator matrix and error vector. Security analysis shows that the proposed scheme is resistant against all known attacks against private-key code-based cryptosystems. The performance analysis is evaluated in terms of error performance, communication overhead and computational complexity. The performance comparison with related additive homomorphic encryption scheme shows the efficiency of the proposed scheme.

Dual-complexities based on straightforward neighborhood pixel prediction in pixel-value-ordering framework for reversible data hiding

PVO-based schemes are the widely-used reversible data hiding (RDH) techniques. Benefiting from the good prediction performance, the stego-image can have a high quality. However, the complexity metric of PVO is still not good enough. The two main limitations are: the block-based context pixels are not highly correlated with the predicted pixel, and the fluctuation-based complexity calculation methods cannot comprehensively represent the real prediction result. Unlike the existing complexity metrics, we consider this problem from a novel viewpoint of neighborhood pixel prediction (NPP), i.e., using the prediction pixel to predict the unmodified neighborhood pixels of a predicted pixel. The neighborhood pixels are more reliable than the context pixels and the generated neighborhood prediction-errors (NPEs) are utilized to represent the real prediction-error (RPE). Two new features are extracted from NPEs as Dual-complexities to determine the embedding order. Experimental results indicate the quality of the stego-image can be improved significantly by using our proposed Dual-complexities in the related PVO-based schemes, and it can be directly extended to other schemes in PVO framework as well.

An empirical hybrid strategy for NoSQL database distribution

A case study of a pharmaceutical distribution company with a relational database that is migrated to a document-oriented NoSQL database for which a hybrid distribution strategy based on fragmentation and replication is implemented. The legacy relational database schema was thoroughly analyzed and evaluated to determine the collections and documents needed to move all the data and leverage the capabilities of the document-based database. In addition, a hybrid data distribution strategy was designed to effectively balance the performance, scalability, and fault tolerance of the system. Finally, integrated operational tests were performed on the distributed system nodes to verify that they function properly and ensure their availability for the company's operations.

On the divide-and-conquer attack of a plaintext related image chaotic encryption scheme

On the divide-and-conquer attack of a plaintext related image chaotic encryption scheme

Self-tuning Database Systems: A Systematic Literature Review of Automatic Database Schema Design and Tuning

Self-tuning is a feature of autonomic databases that includes the problem of automatic schema design. It aims at providing an optimized schema that increases the overall database performance. While in relational databases automatic schema design focuses on the automated design of the physical schema, in NoSQL databases all levels of representation are considered: conceptual, logical, and physical. This is mainly because the latter are mostly schema-less and lack a standard schema design procedure as is the case for SQL databases. In this work, we carry out a systematic literature survey on automatic schema design in both SQL and NoSQL databases. We identify the levels of representation and the methods that are used for the schema design problem, and we present a novel taxonomy to classify and compare different schema design solutions. Our comprehensive analysis demonstrates that, despite substantial progress that has been made, schema design is still a developing field and considerable challenges need to be addressed, notably for NoSQL databases. We highlight the most important findings from the results of our analysis and identify areas for future research work.