Consistency Verification Research Articles

Broad feature extraction and multi-directional imbalanced weighted broad learning system for the unsupervised stereo matching method

The practical application of supervised stereo matching methods is often limited by the requirement of obtaining ground-truth disparity maps in advance. To address this issue, we introduce a novel unsupervised stereo matching method based on the broad feature extraction and multi-directional imbalanced weighted broad learning system. In this study, an end-to-end unsupervised stereo matching method based on broad feature extraction was developed to generate an initial disparity map. Subsequently, a multi-directional imbalanced weighted broad learning system that considers the uneven distribution of disparity values was designed to effectively remove and redefine outlier disparities and enhance the accuracy of generating multiple candidate disparity maps. Multi-direction consistency verification was integrated into the system to further eliminate anomalous disparity values. This verification relies on uniqueness, which guarantees the consistency of the disparity truth values at the same location in various directions. Finally, the local gravity weight method based on a broad feature space was introduced to select a suitable disparity value from neighboring pixels to replace invalid pixel positions. Experiments were conducted under both indoor and outdoor scenarios to demonstrate the effectiveness and versatility of our approach, confirming that it outperforms state-of-the-art techniques, highlighting its superiority.

Multi-Source Image Matching Algorithms for UAV Positioning: Benchmarking, Innovation, and Combined Strategies

The accuracy and reliability of unmanned aerial vehicle (UAV) visual positioning systems are dependent on the performance of multi-source image matching algorithms. Despite many advancements, targeted performance evaluation frameworks and datasets for UAV positioning are still lacking. Moreover, existing consistency verification methods such as Random Sample Consensus (RANSAC) often fail to entirely eliminate mismatches, affecting the precision and stability of the matching process. The contributions of this research include the following: (1) the development of a benchmarking framework accompanied by a large evaluation dataset for assessing the efficacy of multi-source image matching algorithms; (2) the results of this benchmarking framework indicate that combinations of multiple algorithms significantly enhance the Match Success Rate (MSR); (3) the introduction of a novel Geographic Geometric Consistency (GGC) method that effectively identifies mismatches within RANSAC results and accommodates rotational and scale variations; and (4) the implementation of a distance threshold iteration (DTI) method that, according to experimental results, achieves an 87.29% MSR with a Root Mean Square Error (RMSE) of 1.11 m (2.22 pixels) while maintaining runtime at only 1.52 times that of a single execution, thus optimizing the trade-off between MSR, accuracy, and efficiency. Furthermore, when compared with existing studies on UAV positioning, the multi-source image matching algorithms demonstrated a sub-meter positioning error, significantly outperforming the comparative method. These advancements are poised to enhance the application of advanced multi-source image matching technologies in UAV visual positioning.

Terminal sequence consistency verification method for small diameter abreast optical fibers based on computer vision

In a kind of precision industrial equipment, small diameter abreast optical fibers are used for high-speed communication among functional nodes. The arrangement order at both terminals of the abreast optical fibers need to comply with communication protocols. In this paper, we propose an automatic terminal sequence consistency verification method based on computer vision. The Hue Saturation Value (HSV) color space is used for improving the image feature extraction capability. An abreast optical fiber sequence dictionary which converts the protocol logic into an input-output mapping table is provided to follow protocol confidentiality and improve inspecting speed. A light control baffle position adaptive algorithm is designed for improving the accuracy of optical fiber incident light control. The experimental results show that the method can achieve the conductivity inspection of 1 optical fiber every 50 seconds, and the inspection accuracy is over 96.5%, which generally improves the inspection efficiency by 45% compared with manual inspection.

Formal Verification of Consistency for Systems with Redundant Controllers

Formal Verification of Consistency for Systems with Redundant Controllers

VConMC: Enabling Consistency Verification for Distributed Systems Using Implementation-Level Model Checkers and Consistency Oracles

Many cloud services are relying on distributed key-value stores such as ZooKeeper, Cassandra, HBase, etc. However, distributed key-value stores are notoriously difficult to design and implement without any mistakes. Because data consistency is the contract for clients that defines what the correct values to read are for a given history of operations under a specific consistency model, consistency violations can confuse client applications by showing invalid values. As a result, serious consequences such as data loss, data corruption, and unexpected behavior of client applications can occur. Software bugs are one of main reasons why consistency violations may occur. Formal verification techniques may be used to make designs correct and minimize the risks of having bugs in the implementation. However, formal verification is not a panacea due to limitations such as the cost of verification, inability to verify existing implementations, and human errors involved. Implementation-level model checking has been heavily explored by researchers for the past decades to formally verify whether the underlying implementation of distributed systems have bugs or not. Nevertheless, previous proposals are limited because their invariant checking is not versatile enough to check for the wide spectrum of consistency models, from eventual consistency to strong consistency. In this work, consistency oracles are employed for consistency invariant checking that can be used by implementation-level model checkers to formally verify data consistency model implementations of distributed key-value stores. To integrate consistency oracles with implementation-level distributed system model checkers, the partial-order information obtained via API is leveraged to avoid the exhaustive search during consistency invariant checking. Our evaluation results show that, by using the proposed method for consistency invariant checking, our prototype model checker, VConMC, can detect consistency violations caused by several real-world software bugs in a well-known distributed key-value store, ZooKeeper.

A blockchain system supporting cross-border data protection and consistency verification in unified global carbon emissions trading framework

The Carbon Emission Trading (CET) system has been independently implemented in dozens of countries to protect the environment. Meanwhile, there are efforts underway to establish a Unified Global CET (UG-CET) system, which aims to better coordinate emission reduction efforts worldwide and protect the global environment. However, these efforts overlook the issue of data security, which may raise concerns for countries considering participation in such a system. In this paper, we propose a UG-CET system that addresses data security concerns. Firstly, it safeguards cross-border data by not requiring countries to share detailed data, but only providing generalized data. Secondly, it is capable of auditing data, ensuring data consistency even in the absence of shared original data. Therefore, it respects the interests of participating countries by not only protecting their own data but also ensuring the authenticity of data shared by other countries. Finally, the proposed scheme is validated using authentic CET data, and the results demonstrated that it can maintain a high efficiency while achieving a verification accuracy of 99.6%, effectively ensuring the security and consistency of the data flow in carbon emission trading.

Consistency Verification of Frequency Maintenance Criteria Based on Minimum Level of Inertia in South Korea

Consistency Verification of Frequency Maintenance Criteria Based on Minimum Level of Inertia in South Korea

A cluster-based disambiguation method using pose consistency verification for structure from motion

Structure from motion (SfM) recovers scene structures and camera poses based on feature matching, and faces challenges from ambiguous scenes. There are a large number of ambiguous scenes in real environment, which contain many duplicate structures and textures. The ambiguity leads to incorrect feature matches between images with similar appearance, and makes geometric misalignment in SfM. To address this problem, recent methods have focused on investigating the inconsistencies in feature topology among multi-view images. However, the feature topology is directly derived from 2D images. Thus, it is susceptible to feature occlusion caused by changes in perspective. Therefore, we propose a new method that disambiguates scenes using pose consistency rather than feature consistency. The pose consistency is conducted in 3D geometric space which is less sensitive to feature occlusion. Thus, the pose consistency is more robust than feature consistency. Our core motivation lies that the incorrect matches between ambiguous images will cause pose deviation from the global poses generated by correct matches. To detect this pose deviation, we first combine local and global information of the scene to generate the global reliable camera poses. The local information of each image is obtained by image clustering, and it strengthens the global information that is represented as the verified maximum spanning tree of clusters. Then, the global poses serve as the reference for further pose consistency verification. The global poses also enable us to perform both rotation and translation consistency verification for uncertain matches. During the pose consistency verification, the pose deviation calculated on image-level may be too small to be noticed. Thus, we propose to perform pose consistency verification at cluster-level instead of image-level to amplify the pose deviation. In the experiments, we compared our approach with several state-of-the-art methods, including COLMAP, Geodesic-SfM and TC-SfM, on both ambiguous and regular datasets. The results demonstrate that our approach achieves the best robustness, only our approach succeeds on all ambiguous image sequences (14/14). The quantitative evaluation results on image sequences with ground truth also show that our approach achieves the best accuracy (average RMSE of translation = 0.109, average RMSE of rotation = 0.827) among all methods. The source code of our approach is publicly available at https://github.com/gongyeted/MA-SfM.

Scenario-specific verification of system requirements consistency via time modeling

Traditional requirement verification methods often fail to fully consider the modeling of time-dependent behaviors during system execution, which may lead to inconsistent system requirements, thereby compromise software reliability and correctness. To address this limitation, this paper proposes the Scenario-Specific Verification Method for System Requirements Consistency via Time Modeling. The Scenario-Specific Verification Method converts use case descriptions and problem graphs into Clock Constraint Specification Language (CCSL) constraints, transformed into clock graphs for verifying requirement consistency. The resulting constraints are solved with the Z3 solver to validate correctness, thereby enhancing system reliability. The novel time modeling approach provides techniques for verifying temporal dependencies in requirements. A case study demonstrates the efficiency and accuracy of the Scenario-Specific Verification Method in detecting inconsistencies. The main contributions include that a technique is demonstrated to formalize use case descriptions as CCSL constraints, improving on natural language and a method is presented utilizing CCSL time modeling and clock graphs to analyze requirements for consistency. Overall, an innovative solution for requirement verification using time modeling is offered by this paper, contributing to the enhancement of system development process reliability and correctness.

A secure dynamic cross-chain decentralized data consistency verification model

The continuous increase in the number of transactions is exerting significant pressure on the storage capacity of blockchains. By storing data on multiple blockchains and interacting with each other with cross-chain technology, the storage pressure can be relieved to a large degree. Nevertheless, the feature of immutability may impede the further development of blockchains, e.g., some expired identity information cannot be updated due to immutability. The decentralized chameleon hash function can resolve the issue while ensuring the feature of decentralization. Unfortunately, how to ensure the consistency of dynamic data updating in cross-chain interaction is still a question worth considering. In this paper, we propose a dynamic cross-chain decentralized data consistency verification (DCCV) model, in which we adopt an audit chain and design a dynamic Merkle hash tree to guarantee the consistency of dynamic data updating between the source chain and the target chain. In addition, in order to enhance the relevance of calculations across different smart contracts and improve audit efficiency, we propose a way to develop cross-chain smart contracts collaboratively. Moreover, Cosi protocol and multi-signcryption are utilized to ensure the security and privacy of cross-chain data transmission. Finally, theoretical and experimental analysis demonstrates that DCCV can achieve dynamic data consistency verification in the process of cross-chain interaction.

A framework for neural network based constitutive modelling of inelastic materials

Given the significant recent advances in added layer manufacturing and materials engineering, new types of materials or new material micro-structures are becoming available at a fast rate. The finite element analysis of structures or structural components requires a constitutive model that describes the behaviour of the new materials. The formulation of accurate constitutive equations is generally complex and time consuming. Hence, suitable machine learning strategies may be used to render this process obsolete and bridge the gap between experimental data and finite element analysis. In this work, a generic stress update procedure is presented that is suitable for the modelling of rate-independent, elastic or inelastic, isotropic or anisotropic material behaviour. The proposed strategy is based on a recurrent neural network architecture and must be trained on stress and strain data sequences that represent physical or numerical experiments. A training strategy based on gradient-free optimisation is presented. It is shown that piecewise linear behaviour, such as uniaxial elasto-plasticity, can be represented exactly. Further numerical examples include uniaxial damage mechanics and elasto-plasticity under plane strain conditions. An efficient criterion for the verification of thermodynamic consistency is proposed and applied to the trained stress update models. The strategy is compared to GRU or LSTM based architectures and shown to offer advantages.

Hierarchical Supervisory Control Under Partial Observation: Normality

Conditions preserving observability of specifications between the plant and its abstraction are essential for hierarchical supervisory control of discrete-event systems under partial observation. Observation consistency and local observation consistency were identified as such conditions. To preserve normality, only observation consistency is required. Although observation consistency preserves normality between the levels for normal specifications, for specifications that are not normal, observation consistency is insufficient to guarantee that the supremal normal sublanguage computed on the low level and on the high level coincide. We define modified observation consistency, under which the supremal normal sublanguages of different levels coincide. We show that the verification of (modified) observation consistency is <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PSpace</small> -hard for finite automata and undecidable for slightly more expressive models than finite automata. Decidability of (modified) observation consistency is an open problem. Hence we further discuss two stronger conditions that are easy to verify. Finally, we illustrate the conditions on an example of a railroad controller and on a case study of a part of an MRI scanner.

Empowering Domain Experts With Formal Methods for Consistency Verification of Safety Requirements

Empowering Domain Experts With Formal Methods for Consistency Verification of Safety Requirements

CloudBrain-MRS: An intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectrum plots and metabolite quantification, the spread of clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: (1) Automatically statistical analysis to find biomarkers for diseases; (2) Consistency verification between the classic and artificial intelligence quantification algorithms; (3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, data of both healthy subjects and patients with mild cognitive impairment are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing at least two years of free access and service. If you are interested, please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.

Probabilistic consistency of stochastic multiplicative comparison matrices based on Monte Carlo simulation

Stochastic multiplicative comparison matrices (SMCMs) are widely accepted due to their flexibility in measuring various types of decision-makers' uncertain preferences by treating the judgment as a random variable. However, few existing consistency indices for SMCMs account for the influence of uncertainty of SMCMs on consistency. This may lead to some contradictions in consistency measurement for SMCMs. This paper proposes a probabilistic consistency index (PCI) considering the uncertainty of SMCMs from a statistical perspective. The index satisfies the property of invariance under the permutation of the labels of objects. The influence of uncertainty on the PCI is discussed in detail by numerical simulation. In addition, the perfect consistency and acceptable consistency of SMCMs based on the PCI are introduced, and a general threshold determination algorithm for acceptable consistency of SMCMs based on Monte Carlo simulation is designed considering the uncertainty, the distribution and the order of an SMCM. Subsequently, a consistency improvement method for SMCMs based on stochastic programming is proposed, which preserves the original preference of decision-makers to the greatest extent. Finally, examples are given to illustrate the significance of the consistency verification by using the PCI, and the most likely preference structure is used as the ranking for SMCMs.

Evaluation of Chatbot Technology: The Case of Greece

In recent years, the field of Artificial Intelligence has made significant strides, particularly in advancing chatbots through Natural Language Processing (NLP) technology. Recently, however, there has been intense competition in this industry, with major tech companies consistently introducing new and improved solutions. Nevertheless, the Greek context introduces several unique challenges and obstacles to the adoption of modern solutions, owing to the distinctiveness and relative rarity of the Greek language, as well as the limited financial resources of the Greek economy. The objective of this study is to assess the performance of chatbots in terms of the quality of their responses, including relevance, naturalness, coherence, accuracy, and vocabulary, and to gauge user experience and satisfaction. An additional objective is to acquire a comprehensive comparative overview of chatbot performance in Greece, both on a per-question basis and when comparing related questions. The chosen method for evaluation is a guided interview employing closed-type questions. The intention is to gather structured and quantified data in an area where the typical internet user may not possess extensive familiarity or prior experience with such evaluations. Conclusions were drawn for each question, enabling a focused and comparative assessment of solution quality, identification of potential trends, and verification of response consistency.

Noise insensitive volumetric fusion method for enhanced photoacoustic microscopy.

Photoacoustic imaging is an emerging imaging modality that combines the high contrast of optical imaging and the high penetration of acoustic imaging. However, the strong focusing of the laser beam in optical-resolution photoacoustic microscopy (OR-PAM) leads to a limited depth-of-field (DoF). Here, a volumetric photoacoustic information fusion method was proposed to achieve large volumetric photoacoustic imaging at low cost. First, the initial decision map was built through the focus detection based on the proposed three-dimensional Laplacian operator. Majority filter-based consistency verification and Gaussian filter-based map smoothing were then utilized to generate the final decision map for the construction of photoacoustic imaging with extended DoF. The performance of the proposed method was tested to show that our method can expand the limited DoF by a factor of 1.7 without the sacrifice of lateral resolution. Four sets of multi-focus vessel data at different noise levels were fused to verify the effectiveness and robustness of the proposed method. The proposed method can efficiently extend the DoF of OR-PAM under different noise levels.

Exploring and Analyzing ESG Activity Evaluation Factors of Sports Brands using Delphi and AHP Techniques

PURPOSE This study aimed to identify and establish ESG activity assessment factors tailored for sports brands, offering a foundational framework for fostering sustainability within the sports industry.METHODS A Delphi survey was conducted between December 1, 2022, and January 31, 2023, involving university professors, sports brand executives, ESG researchers, sports marketing majors , a nd sports brand influencers. Data were analyzed using SPSS WIN 24.0 and MS Excel, encompassing research subject classification, consistency verification, and empirical analysis.RESULTS The assessment of ESG activity evaluation factors of sports brands revealed that governance, social, and environmental dimensions ranked in descending order of relative importance. Among specific evaluation criteria, the adoption of employee ethics regulations was deemed paramount in the governance evaluation area, while support for stakeholder welfare and social well-being took precedence in the social evaluation category. In the environmental sector, the production and delivery of eco-friendly, durable products held the highest significance. Further analysis, considering the weighted priorities of all evaluation factors, emphasized the importance of factors, such as the adoption of employee ethics regulations, support for stakeholder welfare, social well-being, and the production and delivery of eco-friendly, durable products. Conversely, disclosure of company information and issues, support for the underprivileged, and general shareholders' meeting or agenda for the protection of shareholders' rights and interests ranked lower.CONCLUSIONS In summary, this study validates the role of governance (G) aspects within ESG activities for sports brands, alongside environmental and social dimensions. It highlights the importance of a balanced ESG framework, particularly in an era marked by heightened social responsibility awareness among consumers. Prioritizing governance in marketing strategies not only distinguishes sports brands but also necessitates impactful promotion and advertising efforts.

Заголовочный комплекс радиопрограммы как метатекстовый показатель (по данным опросов)

The aim of the study is to ascertain the extent to which the elements of the headline complex of a radio programme effectively perform the function of a meta-text indicator, that is, to determine whether the potential addressees can guess the range of themes presented in the programme after familiarising themselves with the elements of the headline complex. Evaluation of the success of a radio programme’s title, hashtags, annotation, illustrations posted on the radio station’s website and on its public page in a social network was determined on the basis of data obtained during surveys of potential listeners. The headline complexes in the programmes and podcasts of radio stations of different formats were used for the surveys: Radio Rossii, Radio Mayak and Dorozhnoye Radio. The scientific novelty of the study lies in developing and testing a survey methodology that offers a consistent verification of the role of verbal and visual elements of the headline complex. The conclusions about what causes unsuccessful radio communication at the stage of the listener’s perception of the header complex are also novel. The results showed that potential addressees do not always correctly guess the range of themes of a radio programme based on one or more of the presented elements of the headline complex. The function of a meta-text indicator in a radio programme is performed by the headline complex most effectively in the totality of its elements.

ECFuse: Edge-Consistent and Correlation-Driven Fusion Framework for Infrared and Visible Image Fusion.

Infrared and visible image fusion (IVIF) aims to render fused images that maintain the merits of both modalities. To tackle the challenge in fusing cross-modality information and avoiding texture loss in IVIF, we propose a novel edge-consistent and correlation-driven fusion framework (ECFuse). This framework leverages our proposed edge-consistency fusion module to maintain rich and coherent edges and textures, simultaneously introducing a correlation-driven deep learning network to fuse the cross-modality global features and modality-specific local features. Firstly, the framework employs a multi-scale transformation (MST) to decompose the source images into base and detail layers. Then, the edge-consistent fusion module fuses detail layers while maintaining the coherence of edges through consistency verification. A correlation-driven fusion network is proposed to fuse the base layers containing both modalities' main features in the transformation domain. Finally, the final fused spatial image is reconstructed by inverse MST. We conducted experiments to compare our ECFuse with both conventional and deep leaning approaches on TNO, LLVIP and M3FD datasets. The qualitative and quantitative evaluation results demonstrate the effectiveness of our framework. We also show that ECFuse can boost the performance in downstream infrared-visible object detection in a unified benchmark.