Single Point Research Articles

A Multitask Latent Feature Augmentation Method for Few-Shot Learning.

Few-shot learning (FSL) aims to learn novel concepts quickly from a few novel labeled samples with the transferable knowledge learned from base dataset. The existing FSL methods usually treat each sample as a single feature point in embedding space and classify through one single comparison task. However, the few-shot single feature points on the novel meta-testing episode are still vulnerable to noise easily although with the good transferable knowledge, because the novel categories are never seen on base dataset. Besides, the existing FSL models are trained by only one single comparison task and ignore that different semantic feature maps have different weights on different comparison objects and tasks, which cannot take full advantage of the valuable information from different multiple comparison tasks and objects to make the latent features (LFs) more robust based on only few-shot samples. In this article, we propose a novel multitask LF augmentation (MTLFA) framework to learn the meta-knowledge of generalizing key intraclass and distinguishable interclass sample features from only few-shot samples through an LF augmentation (LFA) module and a multitask (MT) framework. Our MTLFA treats the support features as sampling from the class-specific LF distribution, enhancing the diversity of support features and reducing the impact of noise based on few-shot support samples. Furthermore, an MT framework is introduced to obtain more valuable comparison-task-related and episode-related comparison information from multiple different comparison tasks in which different semantic feature maps have different weights, adjusting the prior LFs and generating the more robust and effective episode-related classifier. Besides, we analyze the feasibility and effectiveness of MTLFA from theoretical views based on the Hoeffding's inequality and the Chernoff's bounding method. Extensive experiments conducted on three benchmark datasets demonstrate that the MTLFA achieves the state-of-the-art performance in FSL. The experimental results verify our theoretical analysis and the effectiveness and robustness of MTLFA framework in FSL.

Accuracy and sensitivity of soil erosion estimation using 137Cs technology: A statistical perspective

Random spatial variation of the 137Cs inventory is the principal contributor to uncertainty in soil erosion estimation using 137Cs technology. A statistically sound sampling design is imperative for obtaining reliable soil erosion estimations. The objectives of this study are to: 1) characterize the effect of sample size on the estimates of mean inventories; 2) evaluate the sensitivity of the estimated soil redistribution to sample size using a new 137Cs model; 3) compare erosion detection limits among five conversion models; and 4) assess three spatial interpolation methods. Two small watersheds were sampled in an irregular grid design, and 30 samples were taken on a reference site. A moving window scheme was used to compute the 7-point and 11-point means of 137Cs inventories. The Welch’s modified t-test was used to test the mean estimates and to compute the detection limits for the replicated samples. Five models were selected to convert 137Cs inventories to soil redistribution rates. Since most single samples without replication yielded insignificant test results, the 137Cs technique cannot be used to estimate soil erosion at a single point due to random spatial variation. With 30 reference samples, 7 replicate samples on the sampling site provided reliable estimations of 137Cs inventories and soil redistribution. The spatial patterns of the estimated 137Cs inventories and soil redistribution became more regular and systematic as sample size increased, which agreed increasingly well with topography and surface hydrology. Replicate samples can be taken from each landform element or slope position where the erosion rate is expected to be uniform. Alternatively, a systematic grid sampling scheme can be used, and the nearest neighbors can be treated as replicates to calculate moving averages. The Type I and II errors with replicated samples showed that all sampling positions should be included in calculating watershed or area mean erosion rates even though the Type I errors indicated insignificant redistribution rates for some samples. Due to the lack of spatial autocorrelation, spline and inverse distance weighting methods performed better than kriging for interpolation. Detection limits and magnitudes of soil redistribution varied substantially with conversion models, depending on their sensitivities to changes in 137Cs inventories.

Pain trajectories and neuropathic pain symptoms following lung cancer surgery: A prospective cohort study.

Persistent postsurgical pain (PPSP) after lung cancer surgery is common and current definitions are based on evaluations at a single time point after surgery. Pain intensity and symptoms may however fluctuate and change over time, and be impacted by multiple and shifting factors. Studies of postoperative recovery patterns and transition from acute to chronic pain are needed for further investigation of preventive measures and treatments to modify unfavourable recovery paths. In this explorative study, 85 patients undergoing surgery due to either presumptive or confirmed lung cancer reported pain intensities bi-monthly for 12 months. Pain trajectories during recovery were investigated, using group-based trajectory modelling. Associations with possible risk factors for PPSP, including clinical variables and anxiety and depression score (HADS), were also explored. A trajectory model containing three 12-month pain recovery groups was computed. One group without PPSP fully recovered (50%) within two to three months. Another group with mild-intensity PPSP followed a protracted recovery trajectory (37%), while incomplete recovery was observed in the last group (13%). Acute postoperative pain and younger age were associated with a less favourable recovery trajectory. More neuropathic pain symptoms were observed in patients with incomplete recovery. Three clinically relevant recovery trajectories were identified, based on comprehensive pain tracking. Higher acute postoperative pain intensity was associated with an unfavourable pain recovery trajectory. Understanding the transition from acute to chronic postoperative pain and identifying preoperative risk factors is essential for the development of targeted treatments and the implementation of preventive measures. This study (1) identified distinct recovery trajectories based on frequent pain assessment follow-ups for 12 months after surgery and (2) evaluated risk factors for unfavourable postoperative pain recovery paths. Findings suggest that early higher postoperative pain intensity is associated with an unfavourable long-term recovery path.

A Pushover analysis approach considering structural and loading uncertainties for seismic performance assessment

This paper proposes an approach for evaluating the seismic performance of buildings. By considering the uncertainties in material and geometrical properties of the structural elements as well as those related to the seismic loading, this method aims to delimit the performance zone of the structure rather than focusing on a single performance point commonly used in pushover analysis. This zone makes it possible to identify the probable levels of performance of the building linked to uncertainties and to indicate the most severe level of damage. Reasoning by performance points leads to an answer that gives a single state of damage. The performance zone is defined by capacity curves and response spectrum representing the mean values plus and minus standard deviation obtained by Monte Carlo simulations. These simulations consider the stiffnesses of the structural elements, the natural periods, and the damping ratio of the response spectrum as random variables. The application of the procedure to a case of a building made it possible to identify the upper limit of damage corresponding to the most unfavourable scenario of uncertainties, as well as the situation of the response value of the studied structure in the performance zone, indicating a less critical level of damage (minor damage) to the building. The calculation of the performance point revealed a level of damage oscillating between Immediate Occupancy (IO) and Life Safety (LS). Considering the uncertainties in the material and geometric properties of the structural elements as well as those linked to the response spectrum, the probable extreme damage state is between the LS and CP levels (severe damage).

Federated learning with blockchain-based model aggregation and incentives

Federated learning is a privacy-preserving machine learning technique that allows mutually distrusting parties to collaboratively train a model without sharing their data. Most federated learning techniques require a centralized aggregator that stores and aggregates models received from multiple parties. However, having a centralized entity may lead to a single point of failure problem. Another problem of federated learning is the leakage of sensitive data through model updates. To address these issues, we propose a Blockchain-based protocol for federated learning. Our protocol uses Blockchain as a model aggregator solving single-point-of-failure problems. Also, we use a Blockchain-based privacy-preserving technique to avoid data leakage problems. We also incorporate a Blockchain-based incentive distribution module to distribute incentives to model contributors. We perform experiments with well-known datasets and show that the proposed model's accuracy is close to that of a centralized aggregator. We also show the overhead of Blockchain by implementing the protocol and running it on Ethereum Blockchain.

Numerical Simulation on Dispersion of Hydrogen Leaked in Particle Layers of Glass Beads and Soil

In order to understand the dispersion of hydrogen (H2) leaked in packed beds of nonporous/porous particles in a partially open space practically, the dispersion of H2 in the particle layers of glass beads and soil was analytically studied using a computational fluid dynamics code to be compared with the experiments and to elucidate the effects of the particle layer. The packed beds in the partially open space can be considered as a basic model for all processes of transfer, treatment, storage, and disposal of radioactive materials containing fuel debris in the decommissioning of nuclear facilities after a severe accident. The H2 flowing out from a single leak point in the particle layer of nonporous glass beads was affected by buoyancy around the leak point and diffused directly above the leak point in an elliptical shape faster than in the horizontal direction. After that, when it reached the air layer in the head space above the particle layer, the H2 spread horizontally, formed a large concentration gradient near the boundary between the particle layer and the air layer, and further diffused in the air layer until the H2 concentration became about 1/3 or less of the concentration near the surface of the particle layer. When the particle diameters were 512 μm and 1200 μm, this tendency was more pronounced when the porosity was the same and the particle diameter was smaller. The calculations largely reproduced the experimental concentration distributions. When the particle layer was porous decomposed granite soil, the diffusion behavior of H2 in the particle layer proceeded in the same manner as in the case of the glass beads. However, a large concentration gradient was formed near the boundary between the particle layer and the air layer, and then H2 diffused in the air layer until the H2 concentration became below the lower combustion limit. It was suggested through sensitivity analysis that the air permeability coefficient had a large effect on the time course of the H2 concentration distribution. Based on this information, we further simulated H2 behavior in the vessel containing the H2 leaked particle layer. By inserting multiple vent pipes without considering H2 generation distribution and particle properties in the particle layer, H2 accumulated from one pipe was discharged by buoyancy without depending on the H2 generation distribution and particle properties in the particle layer and air flowed in from the other pipe. It was suggested that such a natural ventilation process would reduce the H2 concentration in the container.

FTIZZY CONTROLLERS FOR SINGLE POINTCONTROLLER-I (SPC-l) SYSTEMS

Advances in computer technology have introduced computers everywhere. One of the fields that the computers have entered is the field of process control and data acquisition systems. On the other hand, fuzzy control is emerging as an alternative to conventional control to control different systems. this paper is concerned with applying fuzzy control to a locally designed and manufactured process controller. This controller is designated by single point controller-1 (SPC-l)' It is basically a flexible and general-purpose, stand-alone, single-point controller. The CPU section of the SPC-1 is the AT89C5l general purpose microcontroller. The fuzzy controlalgorithms were imple-mented as programs to be executed by this microcontroller. These programs were written in C language and were translated to machine language by Keil8051 C compiler p Vision V5.1.

A full-scale composite tidal blade fatigue test using single and multiple actuators

In order to perform fatigue testing on tidal turbine blades, it is necessary to apply cyclic loads that do not match the blade’s natural frequency. This is due to the high stiffness of the blades and the thermal challenges associated with testing composite materials at frequencies typically around 18–20 Hz. To overcome this challenge, auxiliary systems are used to load the blades. However, conventional hydraulic systems commonly used for this purpose are known to be energy-intensive and inefficient. In this work, we present results obtained at the FastBlade fatigue testing facility, which utilises a regenerative digital displacement hydraulic pump system to address these issues. This innovative system has proven to be highly efficient, resulting in up to 75% energy savings compared to standard hydraulic systems. To perform these tests, we first performed a series of Reynolds-Averaged Navier–Stokes (RANS) simulations using on-site water velocity data to determine equivalent target hydrodynamic loads. These target loads are applied to the blades using initially a single contact point and, later, three load contact points. The FastBlade facility showcases an effective approach to fatigue testing during these tests. Throughout the testing process, comprehensive measurements are taken to evaluate the response of the blades and the FastBlade test structure itself. These measurements provide valuable insights into the mechanical behaviour of the blades when a single or multi-actuator setup is used to match the root bending moment and contribute to the refinement of testing practices. Notably, the blades successfully endured the equivalent of 20 years of tides in an accelerated fatigue loading test without experiencing catastrophic failure. The data obtained from these tests will enable the identification of improvements in testing procedures, including control strategies, load introduction methods, instrumentation layout, instrument calibration, and test design. This knowledge will lead to enhanced performance and reliability of the FastBlade facility, further advancing the field of tidal turbine blade testing.

Experimental analysis of forming forces in incremental forming and its prediction by AI-based methods

Single point incremental forming (SPIF) is an emerging approach to manufacture the user-specific parts of greater strength economically without involving dedicated tools and dies. In this study, peak deforming loads have been measured and estimated in order to determine the size and capacity of forming machinery for manufacturing components, using the SPIF process, for designed process conditions on AA7075-O sheets. Estimation of maximum deforming loads has been accomplished with the help of hybrid artificial neural network (HANN) approach by taking experimental load values as the input dataset. The load values, estimated by HANN approach, have been compared with the load values estimated by two state-of-art regression approaches, that is, Support Vector Machine (SVM) and Gaussian Process Regression (GPR). Results show that the regression methods (SVM and GPR) are outperformed by the HANN model with an accuracy of 99.80%. HANN model estimated force values very close to experimental results. The forming forces were found to be greater when the flat-end tools were used as a forming agent in place of hemispherical-end tool. The proposed study would help the researchers and scientists to assess the capacity the machines used in SPIF process to prepare a particular sheet-part and to prevent the failure of hardware during the SPIF process.

Disk‐Integrated Earth’s Outgoing Longwave Radiation Viewed From a Moon‐Based Platform: Model Simulations

AbstractA Moon‐based sensor can observe the Earth as a single point and achieve disk‐integrated measurements of outgoing longwave radiation (OLR), which significantly differs from low orbital, geostationary, and Sun–Earth L1 point platforms. In this study, a scheme of determining the disk‐integrated Earth’s OLR based on a Moon‐based platform is proposed. The observational solid angle was theoretically derived based on the Earth’s ellipsoid model and the disk‐integrated observational anisotropic factor was estimated to eliminate the effects of the Earth’s radiant anisotropy. The simulated disk‐integrated Earth's OLR obtained from a Moon‐based platform varies periodically, due to changes in the observation geometry and Earth's scene distribution within the observed Earth’s disk. Clouds, meteorological parameters, and the land cover distribution notably affect the disk‐integrated Earth’s OLR. By analyzing the disk‐integrated Earth’s OLR from a Moon‐based platform, significant variabilities were investigated. Additionally, the Earth’s shape and radiant anisotropy that affecting the disk‐integrated Earth’s OLR were estimated. In conclusion, a more realistic Earth’s shape, the latest version of the angular distribution model (ADM), and accurate land cover and meteorological datasets are needed when determining the disk‐integrated Earth’s OLR. It is expected the unique variability captured by this platform and its ability to complement traditional satellite data make it a valuable tool for studying Earth’s radiation budget and energy cycle, and contributing to diagnostic of the climate General Circulation Models (GCM) performance.

2024 Roadmap on Magnetic Microscopy Techniques and Their Applications in Materials Science

Abstract Considering the growing interest in magnetic materials for unconventional computing, data storage, and sensor applications, there is active research not only on material synthesis but also characterisation of their properties. In addition to structural and integral magnetic characterisations, imaging of magnetization patterns, current distributions and magnetic fields at nano- and microscale is of major importance to understand the material responses and qualify them for specific applications. In this roadmap, we aim to cover a broad portfolio of techniques to perform nano- and microscale magnetic imaging using SQUIDs, spin center and Hall effect magnetometries, scanning probe microscopies, x-ray- and electron-based methods as well as magnetooptics and nanoMRI. The roadmap is aimed as a single access point of information for experts in the field as well as the young generation of students outlining prospects of the development of magnetic imaging technologies for the upcoming decade with a focus on physics, materials science, and chemistry of planar, 3D and geometrically curved objects of different material classes including 2D materials, complex oxides, semi-metals, multiferroics, skyrmions, antiferromagnets, frustrated magnets, magnetic molecules/nanoparticles, ionic conductors, superconductors, spintronic and spinorbitronic materials.

Differential and fuzzy differential sandwich theorems involving quantum calculus operators

The principle of subordination is useful in comparing two holomorphic functions when the range of one holomorphic function is a subset of the other and they comply at a single point. The subordination, when spoken in fuzzy set theory, becomes fuzzy subordination as the comparison between two holomorphic functions is made using the fuzzy membership function. In this article, differential and fuzzy differential Subordination, superordination, and sandwich theorems have been discussed for the classes defined by using q-derivative and symmetric q-derivative operators.

Comparison of Vibration Assisted Single Point and Two-Point Incremental Forming of Sheet Metal

Vibration-assisted single-point incremental forming (VASPIF) and two-point incremental forming(TPIF) are two innovative techniques employed in sheet metal forming processes, each offering distinct advantages and limitations. VASPIF utilizes high-frequency oscillations applied to the forming tool to reduce friction and improve material flow, resulting in enhanced formability and surface finish. Conversely, TPIF employs two independently controlled forming tools to deform the sheet metal incrementally, allowing for greater geometric complexity and improved accuracy compared to VASPIF. However, TPIF typically requires more complex tooling and setup, potentially increasing manufacturing costs and setup time. Both techniques have shown promise in improving the formability of sheet metal components while offering unique capabilities suited to specific manufacturing requirements. Vibration-assisted single-point incremental forming (VASPIF) and two-point incremental forming (TPIF) are two advanced methods used in sheet metal forming, each with its unique characteristics, advantages, and limitations. In this comprehensive comparison, we will delve into the fundamental principles, process parameters, applications, advantages, and challenges associated with both techniques. Starting with VASPIF, this innovative approach involves applying high-frequency oscillations to the forming tool during the incremental forming process. These vibrations serve to reduce friction between the tool and the work piece, facilitating material flow and enhancing formability. By effectively decreasing the frictional forces, VASPIF enables the deformation of difficult-to-form materials and improves the surface finish of the formed parts.

Abstract B033: Histone H3.3 K27M and G34R/V glioma driver mutations inhibit mitotic phosphorylation of H3.3 S31, driving tumor formation by inducing chromosomal instability

Abstract Diffuse gliomas are pediatric high-grade gliomas that frequently harbor heterozygous histone H3.3 K27M, G34R, or G34V mutations. These mutations alter histone methylations, although the precise mechanism(s) of gliomagenesis remains uncertain. H3.3 contains a unique Serine at position 31 that is phosphorylated in prophase and dephosphorylated in anaphase during normal cell division. Previously we have reported that chromosome missegregation triggers H3.3 S31 phosphorylation along chromosome arms in ana/telophase leading to p53-induced cell cycle arrest. Here we show, in vitro and in vivo, that H3.3 K27M, G34R, and G34V suppress S31 phosphorylation by its mitotic kinase, Chk1. In diploid cells, overexpression of H3.3 K27M, G34R, G34V, or non-phosphorylatable S31A significantly increases the frequency of chromosome missegregation, while H3.3 K36M did not. This missegregation phenotype could be rescued by overexpression of K27M/S31E or G34R/S31E double mutants. Next, we used CRISPR/Cas9 gene editing to revert K27M or G34V patient-derived DMG cells to WT or replace the mutant allele with S31A. Reversion to WT increased metaphase S31 phosphorylation and reduced chromosome missegregations, but inserting an S31A mutation caused more mitotic defects. Following a mitotic error, newly formed aneuploid cells overexpressing K27M, G34R, or G34V suppress p53 levels and continue to divide, while WT overexpressing cells always arrest. To test the effects of diminished S31 phosphorylation on gliomagenesis, we utilized a novel mouse model of glioma. Expression of PDGFß linked to H3.3K27M, H3.3G34R, and H3.3S31A in neural stem cells in vivo promoted the development of high-grade gliomas in under 100 days, whereas PDFß-H3.3WT did not. Notably, the S31A tumors had histologically normal levels of K27me3 and K36me3. This work shows that a single glioma-associated point mutation in H3.3 can generate chromosomal instability, and which is a major contributing factor to H3.3 K27M and G34-altered glioma formation. Citation Format: Charles A. Day, Florina Grigore, Alyssa Langfald, Faruck Hakkim, David Daniels, Kevin Vaughan, James Robinson, Edward Hinchcliffe. Histone H3.3 K27M and G34R/V glioma driver mutations inhibit mitotic phosphorylation of H3.3 S31, driving tumor formation by inducing chromosomal instability [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr B033.

Prediction of Buildings’ Settlement Induced by Metro Station Deep Foundation Pit Construction

The construction of deep foundation pits in subway stations can affect the settlement of existing buildings adjacent to the pits to varying degrees. In this paper, the Long Short-Term Memory neural network prediction model of building settlement caused by deep foundation pit construction was established using the monitoring data of building settlement around a deep foundation pit project in a metro station in Shanghai, and appropriate hyperparameters including batch size and training set ratio were determined. The accuracy of settlement prediction for single-point and multi-point monitoring of buildings was analyzed. Meanwhile, the effects of construction parameters, engineering geological parameters, and spatial parameters on the accuracy of building settlement prediction were investigated. The results show that the batch size and training set proportion can be taken as 16 and 60%, respectively, when using the Long Short-Term Memory neural network prediction model. The proposed Long Short-Term Memory network model can stably predict the settlement of buildings adjacent to deep foundation pits. The accuracy of settlement prediction at a single point of a building (80%) is lower than the accuracy of coordinated prediction at multiple points (88%). More accurate settlement prediction is achieved with the total reverse construction method. The more detailed the consideration of working conditions, geological parameters, and spatial parameters, the better. The evaluation metrics of the prediction model, RMSE, MAE, and R2, were 0.57 mm, 0.65 mm, and 0.91, respectively. The results of this paper have some practical reference value for analyzing the settlement of buildings caused by foundation pit works.

ConDENSE: Conditional Density Estimation for Time Series Anomaly Detection

In recent years deep learning methods, based on reconstruction errors, have facilitated huge improvements in unsupervised anomaly detection. These methods make the limiting assumption that the greater the distance between an observation and a prediction the lower the likelihood of that observation. In this paper we propose conDENSE, a novel anomaly detection algorithm, which does not use reconstruction errors but rather uses conditional density estimation in masked autoregressive flows. By directly estimating the likelihood of data, our model moves beyond approximating expected behaviour with a single point estimate, as is the case in reconstruction error models. We show how conditioning on a dense representation of the current trajectory, extracted from a variational autoencoder with a gated recurrent unit (GRU VAE), produces a model that is suitable for periodic datasets, while also improving performance on non-periodic datasets. Experiments on 31 time-series, including real-world anomaly detection benchmark datasets and synthetically generated data, show that the model can outperform state-of-the-art deep learning methods.

Multifactor Authentication System

The multifactor authentication system is a state- of-the-art solution created to improve security protocols across numerous sectors and shield confidential data from unauthorized access. Several layers of authentication are provided by this system, which makes use of cutting-edge technologies, ensuring that only people with the proper authorization can access particular resources or systems. It integrates biometric authentication, including fingerprint, facial, or iris scanning, which ups security because these biological characteristics are personal to each person. This system also uses multi-factor authentication, which combines two or more authentication factors, such as something the user is (a smart card), something they have (a password), or something they know (a password) (biometrics). This combination lessens the possibility of a single point of failure, hence enhancing security. Real-time monitoring, risk-based authentication, and behavioral biometrics are further aspects of the advanced authentication system that help to identify and stop fraudulent actions. Organizations can dramatically reduce security risks, safeguard sensitive data, and adhere to strict regulatory requirements by deploying this solution. The advancedauthentication system, in its whole, is a potent tool in the modern digital environment, protecting important assets with its strong and complex security measures. Keywords: advanced authentication system, security, biometric authentication, multi-factor authentication, real-time monitoring, risk- based authentication,

Semi-Bayesian active learning quadrature for estimating extremely low failure probabilities

The Bayesian failure probability inference (BFPI) framework provides a sound basis for developing new Bayesian active learning reliability analysis methods. However, it is still computationally challenging to make use of the posterior variance of the failure probability. This study presents a novel method called ‘semi-Bayesian active learning quadrature’ (SBALQ) for estimating extremely low failure probabilities, which builds upon the BFPI framework. The key idea lies in only leveraging the posterior mean of the failure probability to design two crucial components for active learning — the stopping criterion and learning function. In this context, a new stopping criterion is introduced through exploring the structure of the posterior mean. Besides, we also develop a numerical integration technique named ‘hyper-shell simulation’ to estimate the analytically intractable integrals inherent in the stopping criterion. Furthermore, a new learning function is derived from the stopping criterion and by maximizing it a single point can be identified in each iteration of the active learning phase. To enable multi-point selection and facilitate parallel computing, the proposed learning function is modified by incorporating an influence function. Through five numerical examples, it is demonstrated that the proposed method can assess extremely small failure probabilities with desired efficiency and accuracy.

A Novel Blockchain-Based Responsible Recommendation System for Service Process Creation and Recommendation

Service composition platforms play a crucial role in creating personalized service processes. Challenges, including the risk of tampering with service data during service invocation and the potential single point of failure in centralized service registration centers, hinder the efficient and responsible creation of service processes. This paper presents a novel framework called Context-Aware Responsible Service Process Creation and Recommendation (SPCR-CA), which incorporates blockchain, Recurrent Neural Networks (RNNs), and a Skip-Gram model holistically to enhance the security, efficiency, and quality of service process creation and recommendation. Specifically, the blockchain establishes a trusted service provision environment, ensuring transparent and secure transactions between services and mitigating the risk of tampering. The RNN trains responsible service processes, contextualizing service components and producing coherent recommendations of linkage components. The Skip-Gram model trains responsible user-service process records, generating semantic vectors that facilitate the recommendation of similar service processes to users. Experiments using the Programmable-Web dataset demonstrate the superiority of the SPCR-CA framework to existing benchmarks in precision and recall. The proposed framework enhances the reliability, efficiency, and quality of service process creation and recommendation, enabling users to create responsible and tailored service processes. The SPCR-CA framework offers promising potential to provide users with secure and user-centric service creation and recommendation capabilities.

Smartphone-based Vision/MEMS-IMU/GNSS tightly coupled seamless positioning using factor graph optimization

With the diversification of functionalities and enhancement of performance in smartphones, low-cost GNSS chips, Micro-Electro-Mechanical System (MEMS) inertial measurement units (IMU), and cameras have become essential hardware components. Moreover, the computational power provided by smartphones for data processing has also become more robust. The fusion of multiple sensors in smartphones for positioning has emerged as a trend. In order to further enhance the positioning performance of smartphones in complex environments, this paper proposes a smartphone-based Vision/MEMS-IMU/GNSS tightly coupled integration for indoor-outdoor seamless positioning. Factor graph optimization is employed to combine observations from monocular vision, MEMS-IMU, and GNSS. To solve the unstable timing of current smartphone sensors, we present a GNSS-to-VIO time synchronization method. Furthermore, an adaptive positioning mechanism is devised based on the presence or absence of GNSS signals, allowing indoor and outdoor environment to switch. The proposed method is validated by integrating smartphone VIO with both smartphone built-in GNSS and high-performance geodetic GNSS receivers. The test results of the complex outdoor environments demonstrate that the proposed method significantly enhances GNSS positioning performance. The positioning accuracy of the proposed method using geodetic GNSS receiver is superior to that using smartphone GNSS. In outdoor environments, our method enables positioning accuracy of smartphone to improve 29.4, 53.0 and 69.9 % in three directions compared to single point positioning. Indoor and outdoor tests verified that the proposed method can provide continuous and reliable positioning in alternating interrupted and restored GNSS signals.