Consistency Verification Research Articles

Infrared and low-light visible image fusion based on hybrid multiscale decomposition and adaptive light adjustment

Most traditional infrared and visible image fusion methods often ignore weak texture details, especially for the low-light visible images in which the weak details are easily drowned due to noise or ill-illumination. To address this problem, we propose a novel infrared and low-light visible image fusion method from the perspective of low-light visible image enhancement, weak feature extraction strategy and detail preserved fusion rules. By combining both local and global contrast enhancements, an adaptive light adjustment algorithm is proposed to improve the brightness and texture details of low-light visible images. In addition, we design a hybrid multiscale decomposition model based on guided filters (GFs) and side window guided filters (SWGFs) to decompose the source images into the base layer, large-scale detail layers and small-scale detail layers. The three layers reflects the background, large edge structures and weak texture details of source images respectively. Subsequently, the visual saliency retention, normalized arctan function, and edge preservation-based consistency verification are applied to highlight salient targets and retain weak details for three layers fusion. Qualitative and quantitative experimental results on publicly available datasets prove the superiority of our method over state-of-the-art methods in terms of highlight salient targets, avoiding edge blurring, and retaining weak details.

Clinical implementation of the first Cherenkov imaging system in a community-based hospital.

PurposeTo document experiences with one year of clinical implementation of the first Cherenkov imaging system and share the methods that we developed to utilize Cherenkov imaging to improve treatment delivery accuracy in real-time.MethodsA Cherenkov imaging system was installed commissioned and calibrated for clinical use. The optimal room lighting conditions and imaging setup protocols were developed to optimize both image quality and patient experience. The Cherenkov images were analyzed for treatment setup and beam delivery verification.ResultsWe have successfully implemented a clinical Cherenkov imaging system in a community-based hospital. Several radiation therapy patient setup anomalies were found in 1) exit dose to the contralateral breast, 2) dose to the chin due to head rotation for a supraclavicular field, 3) intrafractional patient motion during beam delivery, and 4) large variability (0.5 cm to 5 cm) in arm position between fractions. The system was used to deliver deep inspiration breath hold (DIBH) treatment delivery of an electron treatment beam. Clinical process and procedures were improved to mitigate the identified issues to ensure treatment delivery safety and to improve treatment accuracy.ConclusionThe Cherenkov imaging system has proven to be a valuable clinical tool for the improvement of treatment delivery safety and accuracy at our hospital. With only minimal training the therapists were able to adjust or correct treatment positions during treatment delivery as needed. With future Cherenkov software developments Cherenkov imaging systems could provide daily surface guided radiotherapy (SGRT) and real time treatment delivery quality control for all 3D and clinical setup patients without adding additional radiation image dose as in standard kV, MV and CBCT image verifications. Cherenkov imaging can greatly improve clinical efficiency and accuracy, making real time dose delivery consistency verification and SGRT a reality.

A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services

Location-based services (LBSs) facilitate people’s lives; location-based service providers (LBSPs) usually outsource services to third parties to provide better services. However, the third party is a dishonest entity that might return incorrect or incomplete query results under the consideration of saving storage space and computation resources. In this paper, we propose a security-enhanced query result verification scheme (SEQRVS) for the outsourced data in a LBS. Specifically, while retaining fine-grained query result verification, we improve the construction process of verification objects to enhance the security of the outsourced data. To prevent the third party from deducing the knowledge of the outsourced data stored in itself (statistically), our scheme designs a novel storage structure to enhance the ability of privacy preservation for the outsourced data. Furthermore, based on the secure keyword search and query result verification mode proposed in our scheme, the user cannot only verify the correctness and completeness of the query result but also achieve consistency verification by the blockchain. Finally, the security analysis and extensive simulation results show the security and practicality of the proposed scheme.

Rotating Savings and Credit Associations, An Alternative Source of SME Financing: The Case of the Inhabitants of the Western Region of Cameroon

The relevance of SMEs to the economy is a globally accepted truth. These small actors generally constitute the largest category of businesses and contribute significantly to vital economic indicators. Despite their relevance, this category of businesses experiences considerable challenges obtaining financing: especially formal financing. This limited access to capital has been reported to constitute a major cause of high SME failure rates. This is mainly because these businesses lack the collateral necessary to access formal financing. Despite the above literature, it is reported that inhabitants of the Western Region of Cameroon, also referred to as the Bamelikes make extensive use of Rotating Savings and Credit Associations (ROSCA) for business financing and that this is partly responsible for their increased success in entrepreneurial activities. However, empirical studies establishing the impact of the usage of ROSCAs on SME access to capital in this region are almost inexistent. Given the above, it is the aim of this study to investigate and empirically establish whether the usage of ROSCAs significantly impacts SME access to capital in the Western Region of Cameroon. Using stratified random sampling, 500 participants from the major settlements of the Western Region of Cameroon were selected for this study. The questionnaire for the study was administered to these participants. 414 questionnaires were returned and after consistency verification, data from 401 respondents were retained for this study. The data was analyzed using the One-Way ANOVA analytical tool in the IBM SPSS software (version 21). The finding disclosed that ROSCAs usage has a statistically significant impact on SME’s access to capital in the Western Region of Cameroon. It is therefore recommended that SME owners/managers join such associations or come together to create such associations to improve their access to capital. In addition to that, since these organizations are widely spread across Africa and developing economies, they could be repurposed for business financing reasons instead of consumption.

Efficient Multi-focus Image Fusion Using Parameter Adaptive Pulse Coupled Neural Network Based Consistency Verification

Efficient Multi-focus Image Fusion Using Parameter Adaptive Pulse Coupled Neural Network Based Consistency Verification

Anchorage of shear reinforcement in beams and slabs

Anchorage of shear reinforcement, such as links or stirrups, can be performed by providing hooks, bends or heads at their ends, by welding transverse reinforcement or by closing open stirrups with pins. Hooks and bends, also used to enhance the anchorage of flexural reinforcement at the end regions of beams and slabs, have often been preferred because of their simple and cost-effective production. Such anchorages present nevertheless several peculiarities that shall be accounted for. They are relatively sensitive to their detailing (mandrel diameter, length of the tail segment between bar end and bent region) as well as to the cracking state of the surrounding concrete. Also, brittle failures can occur due to spalling of the concrete cover in case of bars near to a free surface.The anchorage with hooks and bends is currently still widely designed according to old detailing rules, based on the results of tests performed in many cases with materials whose properties are significantly different than those of nowadays. Also, no mechanical models are available for a consistent verification and detailing, acknowledging the different potential failure modes and their interaction with the surrounding concrete. In an effort to provide a consistent approach to its design and verification, this paper presents an investigation addressed at understanding the mechanical response and performance of bend and hook anchorages. To that aim, the results of an experimental programme performed with state-of-the-art instrumentation are introduced. By means of analysis of Digital Image Correlation and Fibre-Optic Measurements, the complete transfer of forces is analysed under different circumstances. On that basis, a consistent mechanical model is developed and validated, also reproducing a large variety of tests found in the literature. The paper introduces eventually several practical considerations on the activation of shear reinforcement in beams, and the level of performance required at its anchorages.

The initial Polish adaptation of the Mind Wandering Questionnaire: translation and verification of the scale internal consistency

Abstract Introduction: The aim of the current study was to present the results of the first step of the Mind Wandering Questionnaire (MWQ) adaptation to the Polish sample. The adaptation involved performing translation of the scale, verification of the translated items’ accessibility and the analysis of the internal consistency. Material and methods: MWQ in original form is a single-factor short self-assessing questionnaire composed of five items with a 6-point Likert-type answering scale. The translation of items was performed by a professional language expert according to psychometric rules. The research group included 70 healthy young adults (students), aged 21.53, an equal number of males and females. Results: The adapted questionnaire exhibited a single-factorial structure and satisfactory parameters of internal consistency: Cronbach’s alpha was 0.78, all individual items were significantly correlated with MWQ total score, and the mean value of correlation reached 0.74. The mean value of the adopted MWQ total score reached 15.84 (SD = 4.11). The study showing the initial step of the questionnaire adaptation revealed that the scale in the Polish version has an internal structure comparable with the original one, and its internal consistency exhibited good parameters. Conclusions: In the next steps of the scale full adaptation, a convergent validity assessment should be carried out together with correlations with other scales assessing psychological constructs and dimensions of individual differences associated with Mind Wandering, such as neuroticism and impulsiveness.

Piano Information Teaching Mode Based on Deep Learning Algorithm

In order to improve the effect of piano information teaching, a piano information teaching mode based on deep learning algorithm is proposed. The teaching objectives are divided into three levels: classroom teaching objectives, curriculum objectives, and education and training objectives. A piano information classroom integrating cloud application platform, teaching platform, resource platform, learning space, and interactive classroom is built. The previous teaching mode is optimized to build an innovative teaching mode of piano information classroom. The evaluation index system of piano informatization classroom teaching quality is constructed, and the hierarchical structure model of each evaluation index is established by using the analytic hierarchy process. The hierarchical analysis method is used to establish a hierarchical structure model of each evaluation index. The judgment matrix is determined by the nine-digit scale method. After the consistency verification of the judgment matrix, the weight of the quality evaluation of piano information classroom teaching is calculated. The new mode optimizes the weight and threshold of BP neural network in deep learning algorithm by genetic algorithm (GA). The weight of each classroom teaching quality evaluation index is input into the GA-BP neural network, and the network output result is the piano information classroom teaching quality evaluation score. The test results show that the optimal number of hidden layer nodes for the BP neural network is 7, when the GA-BP neural network iterations are 95. This method can evaluate the quality of piano information classroom teaching, with high evaluation accuracy and strong practical application.

Cross-mapping medical records to NANDA-I to identify nursing diagnoses in a vulnerable population.

To assess the association between vulnerable populations and nursing care needs, using NANDA-I diagnostics, in the population of the Canary Islands, Spain. Nursing social epidemiology study. Cross Mapping of Medical Records to NANDA-I to Identify Nursing Diagnoses in a Population usinga medical, epidemiological follow-up study of a cohort of 7,190 people. The level of vulnerability of the participants was assigned, among those who were also assigned nursing diagnoses, using the "ICE index" to calculate the expected associations. The most prevalent nursing diagnosis in our sample was Sedentary lifestyle (60.5%), followed by Ineffective health self-management (33.8%) and Risk-prone health behaviour (28.7%). Significant differences were found by sex, age group and social class, with the nursing diagnoses included in the study being more prevalent among the most socio-economically disadvantaged social class. The cross-mapping method is useful to generate diagnostic information in terms of care needs, using the NANDA-I classification. The expected associations between high social vulnerability and care needs have been verified in a comprehensive and representative sample of the Canarian population (Spain). From an epidemiological perspective, identifying nursing diagnoses at the population level allows us to find the most prevalent needs in the different community groups and to focus appropriate nursing interventions for their implementation and impact assessment.

Logical consistency verification of state sensing in safety‐critical decision: A case study of train routing selection

Reliable decision-making in a safety-critical system depends on the consistency of the state of the system process. Based on the decision-makeing properties of safety-critical systems, the definition of the critical component, Boolean evolution system, and cognate variables are presented here. By exploring the two lemmas of the critical components, the consistency of the safety-critical system is defined, then a new process of a safety-critical decision of consistency checking is proposed. In order to check the consistency properties of the decision, two computation tree logic formulas derived from the lemmas of the consistency checking are developed. The case of the train routing selection is modelled and verified by model checking, which shows the decision prototype guarantees the safety of the critical process. As the turnout is the critical component in the process of the train routing selection, a multi-sensor-based unit of state monitoring is developed, and a real-world test shows the accuracy of the monitoring unit. This study confirms that the state consistency verification ensures the reliability of the safety-critical system's decision output. The case can enlighten the development of the intelligent control system which has a critical component of decision-making.

Automata-Based Approach to Manage Self-Adaptive Component-Based Architectures

Dynamic software architectures in changing environments need human supervision to continue operation in all conditions. These changes may come from the software architecture or its environment. Such software architecture is required to monitor itself and its environment, detect changes, decide how to react, and act to perform such changes. Enhancing the software architecture with the ability to self-adapt offers a very interesting way forward. This work presents an automata based approach to manage self-adaptive component-based architecture. This approach allows software architecture to adjust itself to changes during runtime and without stopping its execution. It helps the maintenance of the consistency and the coherence of software architecture before, during and after adaptation. The proposed approach is based mainly on the use of the meta-model, automata concept, XML language and UPPAAL model checking tool for the verification of the software consistency before enacting adaptation.

Combining Deep Learning and Robust Estimation for Outlier-Resilient Underwater Visual Graph SLAM

Visual Loop Detection (VLD) is a core component of any Visual Simultaneous Localization and Mapping (SLAM) system, and its goal is to determine if the robot has returned to a previously visited region by comparing images obtained at different time steps. This paper presents a new approach to visual Graph-SLAM for underwater robots that goes one step forward the current techniques. The proposal, which centers its attention on designing a robust VLD algorithm aimed at reducing the amount of false loops that enter into the pose graph optimizer, operates in three steps. In the first step, an easily trainable Neural Network performs a fast selection of image pairs that are likely to close loops. The second step carefully confirms or rejects these candidate loops by means of a robust image matcher. During the third step, all the loops accepted in the second step are subject to a geometric consistency verification process, being rejected those that do not fit with it. The accepted loops are then used to feed a Graph-SLAM algorithm. The advantages of this approach are twofold. First, the robustness in front of wrong loop detection. Second, the computational efficiency since each step operates only on the loops accepted in the previous one. This makes online usage of this VLD algorithm possible. Results of experiments with semi-synthetic data and real data obtained with an autonomous robot in several marine resorts of the Balearic Islands, support the validity and suitability of the approach to be applied in further field campaigns.

Formally verifying consistency of sequence diagrams for safety critical systems

• Automated transformation from sequence diagrams to Clock Constraint Specification Language constraints. • SMT based consistency verification. • Clock graph based consistency verification. UML interactions, aka sequence diagrams, are frequently used by engineers to describe expected scenarios of good or bad behaviors of systems under design, as they provide allegedly a simple enough syntax to express a quite large variety of behaviors. This paper uses them to express safety requirements for safety critical systems in an incremental way, where the scenarios are progressively refined after checking the consistency of the requirements. The semantics of these scenarios are expressed by transforming them into an intermediate semantic model amenable to formal verification. In this paper, we rely on the Clock Constraint Specification Language (CCSL) as the intermediate semantic language. In some sense, sequence diagrams and CCSL constraints both express a family of acceptable infinite traces that must include the behaviors given by the finite set of finite execution traces against which we validate. We compare these requirements to actual execution traces to prove the validity of our transformation. As to check the consistency of the sequence diagrams, we present two verification methods based on SMT and clock graphs respectively. The SMT based method relies on our analysis tool called MyCCSL to analyze CCSL constraints. The clock graph based method transforms CCSL constraints into a clock graph, and does the analysis through traversing the clock graph. Finally, these two methods are evaluated against real cases from the railway transit systems. The results show that the SMT based method provides accurate but slow analysis, while the clock graph based method dramatically increases the verification efficiency aiming at two kinds of typical inconsistencies found by the SMT based method.

Intelligent vehicle longitudinal acceleration control method based on feedforward and feedback combination

This article proposes a longitudinal acceleration control method based on feedforward and feedback combination to solve the problem of the unfixed mapping relationship between longitudinal acceleration and pedal operation. The drive/brake system is normalized by steady-state compensation correction, and the closed-loop parameterized design is carried out with the principle of non-smooth optimization, so that the closed-loop control system conforms to the ideal second-order system accordingly, so that the pedal operation amount and the acceleration mapping relationship are kept consistent. At the same time, by designing a reasonable frequency bandwidth, the dynamic quality of longitudinal motion is improved. Through driving simulator-based dynamic response performance verification, mapping relationship consistency verification, and acceleration tracking control verification under different working conditions. The results show that the method proposed in this paper has good dynamic response performance and acceleration tracking control effect, and the mapping relationship between acceleration and pedal remains unchanged when the performance of automobile components is degraded.

CT and MRI Medical Image Fusion Using Noise-Removal and Contrast Enhancement Scheme with Convolutional Neural Network.

Medical image fusion (MIF) has received painstaking attention due to its diverse medical applications in response to accurately diagnosing clinical images. Numerous MIF methods have been proposed to date, but the fused image suffers from poor contrast, non-uniform illumination, noise presence, and improper fusion strategies, resulting in an inadequate sparse representation of significant features. This paper proposes the morphological preprocessing method to address the non-uniform illumination and noise by the bottom-hat–top-hat strategy. Then, grey-principal component analysis (grey-PCA) is used to transform RGB images into gray images that can preserve detailed features. After that, the local shift-invariant shearlet transform (LSIST) method decomposes the images into the low-pass (LP) and high-pass (HP) sub-bands, efficiently restoring all significant characteristics in various scales and directions. The HP sub-bands are fed to two branches of the Siamese convolutional neural network (CNN) by process of feature detection, initial segmentation, and consistency verification to effectively capture smooth edges, and textures. While the LP sub-bands are fused by employing local energy fusion using the averaging and selection mode to restore the energy information. The proposed method is validated by subjective and objective quality assessments. The subjective evaluation is conducted by a user case study in which twelve field specialists verified the superiority of the proposed method based on precise details, image contrast, noise in the fused image, and no loss of information. The supremacy of the proposed method is further justified by obtaining 0.6836 to 0.8794, 0.5234 to 0.6710, and 3.8501 to 8.7937 gain for , CRR, and AG and noise reduction from 0.3397 to 0.1209 over other methods for objective parameters.

Fuzzy Comprehensive Evaluation for Grasping Prioritization of Stacked Fruit Clusters Based on Relative Hierarchy Factor Set

To solve the problems of unclear boundaries and inconsistent influence weights among prioritization evaluation factors for grasping stacked fruit clusters by parallel robots, a fuzzy comprehensive evaluation method for the grasping prioritization of stacked fruit clusters based on a relative hierarchy factor set is proposed. According to the morphological features of stacked fruit clusters and motion features of parallel robots, a hierarchical tree model without a cross based on a subtree structure is constructed to analyze the multiple factors with unclear boundaries. A relative factor set with positive and negative effects is constructed, and a mathematical expectation is used to construct an average random consistency index and consistency satisfaction value for improving the consistency of influence weights and precision of consistency verification for a comparison matrix. The weight vector is constructed from the top to the bottom of the model, and the membership matrix of the multi-layer factors and grasping prioritization are calculated from bottom to top. The results showed that the average precision of grasping prioritization of stacked fruit clusters based on the proposed method increased by 27.77% compared with the existing fuzzy comprehensive evaluation method. The proposed method can effectively improve prioritization precision for grasping randomly stacked fruit clusters affected by multiple factors and can further realize accurate automatic sorting.

A SERM based framework for defining and processing rules supporting the verification of data consistency and integrity

The SERM (Structured Entity Relationship Diagram) data model is a hierarchical extension of the classic ER (Entity Relationship) model. The hierarchical structure of the SERM diagram means that any closed circuit relationship can cause inconsistency and lack of data integrity. The occurrence of these problems is uncertain and should be systematically investigated. Therefore, there is a clear need to develop an approach supporting the definition of logical rules that would control these elements. The aim of the article is to present the framework supporting the intelligent design of relational databases. Its main aspects include identification and semi-automatic handling of logical rules based on the SERM model. An important advantage of the approach is that it does not limit itself to building a semantic data model, but also uses it to design database programming objects such as procedures, functions and triggers that guard the consistency of databases.

Judicial Vulnerability on the Exercise of Orthodontics: Construction and Validation of a Questionnaire

Objective: To develop and validate a questionnaire to measure the vulnerability of orthodontists, measuring the risks of being involved in civil liability lawsuits. Material and Methods: In-depth interviews were performed with three groups: G1- law professionals, G2 – orthodontists, and G3 – orthodontic patients. From the analysis of the content of Bardin, domains for the construction of the first version of the 53-question questionnaire were identified. The questionnaire was submitted to experts for validation, inclusion and exclusion of questions, but maintaining the 53-question format. It was submitted to the test-retest phases and verification of internal consistency. Results: 247 professionals answered the final version of the questionnaire. The intraclass correlation coefficient was 88.8%. Cronbach's alpha was 0.946, with high internal consistency. The Kaiser-Meyer-Olkin and Bartlett’s tests confirmed internal consistency showing the values of 0.909 and significance of <0.001, respectively. From the total score and factorial analysis, the sample was divided into three groups of judicial vulnerability. Conclusion: The results of this study demonstrated that the questionnaire is a valid tool to measure the risks of involvement in civil liability lawsuits by orthodontists. It presented a multidimensional character and might be applied as well as face to face or online, without prejudice to quality.

Первичная адаптация и валидизация шкалы Р. Шварцера «Проактивные аттитюды» на неклинической выборке: проверка внутренней согласованности и тест-ретестовой надежности

The article presents the results of translation, verification of internal consistency, standardization, validation, and test-retest reliability of the R. Schwarzer scale, called «Proactive Attitudes». This scale consists of 8 questions, designed to assess the level of proactive attitudes as a proactive attitude towards future events. The total sample of the study consisted of 385 people (245 women and 140 men) aged 16—55 years. The Russian-language version of the scale showed acceptable internal consistency (Cronbach’s alpha coefficient — α = 0.768) between the ages of 16 and 55. Factor analysis revealed a one-factor structure of the scale (factor «Possibility of self-choice»). Spearman test-retest reliability indicators turned out to be significant (the first measurement — r = 0.68 at p≤0.01; the second measurement after 3 weeks — r = 0.78 at p≤0.01). The scale also revealed convergent validity and discrimination (according to Spearman’s rank correlation coefficient, it ranged from 0.547 to 0.800 at p≤0.01; Pearson’s item-total correlation ranged from 0.351 to 0.635 at p≤0.01). The mean values on the scale are highlighted, gender differences are presented. The scale «Proactive attitudes» is an express method in assessing the proactive orientation of a person, has the necessary psychometric characteristics. The prospect of the work consists in further standardization in other age groups, clarification of the data of construct and external validity.

Content-Aware Detection of Temporal Metadata Manipulation

Most pictures shared online are accompanied by temporal metadata (i.e., the day and time they were taken), which makes it possible to associate an image content with real-world events. Maliciously manipulating this metadata can convey a distorted version of reality. In this work, we present the emerging problem of detecting timestamp manipulation. We propose an end-to-end approach to verify whether the purported time of capture of an outdoor image is consistent with its content and geographic location. We consider manipulations done in the hour and/or month of capture of a photograph. The central idea is the use of supervised consistency verification, in which we predict the probability that the image content, capture time, and geographical location are consistent. We also include a pair of auxiliary tasks, which can be used to explain the network decision. Our approach improves upon previous work on a large benchmark dataset, increasing the classification accuracy from 59.0% to 81.1%. We perform an ablation study that highlights the importance of various components of the method, showing what types of tampering are detectable using our approach. Finally, we demonstrate how the proposed method can be employed to estimate a possible time-of-capture in scenarios in which the timestamp is missing from the metadata.