Framework For Identification Research Articles

Two in One: A multi-task framework for politeness turn identification and phrase extraction in goal-oriented conversations

Goal-oriented dialogue systems are becoming pervasive in human lives. To facilitate task completion and human participation in a practical setting, such systems must have extensive technical knowledge and social understanding. Politeness is a socially desirable trait that plays a crucial role in task-oriented conversations for ensuring better user engagement and satisfaction. To this end, we propose a novel task of politeness analysis in goal-oriented dialogues. Politeness analysis consists of two sub-tasks: politeness turn identification and phrase extraction. Politeness turn identification is dependent on textual triggers denoting politeness or impoliteness. In this regard, we propose a Bidirectional Encoder Representations from Transformers-Directional Graph Convolutional Network (BERT-DGCN) based multi-task learning approach that performs turn identification and phrase extraction tasks in a unified framework. Our proposed approach employs BERT for encoding input turns and DGCN for encoding syntactic information, in which dependency among words is incorporated into DGCN to improve its capability to represent input utterances and benefit politeness analysis task accordingly. Our proposed model classifies each turn of a conversation into one of the three pre-defined classes, viz. polite, impolite and neutral, and extracts phrases denoting politeness or impoliteness in that turn simultaneously. As there is no such readily available data, we prepare a conversational dataset, PoDial for mental health counseling and legal aid for crime victims in English for our experiment. Experimental results demonstrate that our proposed approach is effective and achieves 2.04 points improvement on turn identification accuracy and 2.40 points on phrase extraction F1- score on our dataset over baselines.

A two-step identification framework for building thermal dynamic based on high fluctuation performance data: A case of office with variable refrigerant flow system

Thermal dynamics model plays a crucial role of advanced control in the intelligent building. Grey box method is widely used in the modeling of building thermal dynamics based on the operation data of air-conditioner. The performance of direct-expansion (DX) system fluctuated severely during its operation, which leads to the difficulties in identification and evaluation of the building thermal model. In this paper, a two-step identification framework for second-order thermal resistance-capacity network of building based on DX system data is proposed. A case study is conducted on an office building equipped with variable refrigerant flow system in Foshan, China. The results show that the proposed framework can identify the parameters rationally. The model achieves high accuracy for both indoor temperature (Mean absolute errors of 0.74°C) and cooling load prediction (Mean absolute errors of 4453.86W) in a short time interval of 10 minutes. Additionally, the best complexity of the grey-box model for case building and the data acquisition interval is determined. Finally, an improved set of evaluation metrics for thermal load evaluation is proposed and discussed.

Nonlinear dynamical system approximation and adaptive control based on hybrid‐feed‐forward recurrent neural network: Simulation and stability analysis

AbstractWe proposed an online identification and adaptive control framework for the nonlinear dynamical systems using a novel hybrid‐feed‐forward recurrent neural network (HFRNN) model. The HFRNN is a combination of a feed‐forward neural network (FFNN) and a local recurrent neural network (LRNN). We aim to leverage the simplicity of FFNN and the effectiveness of RNN to capture changing dynamics accurately and design an indirect adaptive control scheme. To derive the weights update equations, we have applied the gradient‐descent‐based Back‐Propagation (BP) technique, and the stability of the proposed learning strategy is proven using the Lyapunov stability principles. We also compared the proposed method's results with those of the Jordan network‐based controller (JNC) and the local recurrent network‐based controller (LRNC) in the simulation examples. The results demonstrate that our approach performs satisfactorily, even in the presence of disturbance signals.

A Self-Optimized Machine Learning Approach for Constitutive Parameters Identification of Aortic Walls

The accurate identification of constitutive parameters is considered the key challenge for the study of mechanical properties of biological soft tissues. The popular machine learning (ML)-based inverse identification frameworks always require large amounts of training datasets. This work proposes an ML framework called self-optimized ML for accurate identifying the constitutive parameters of aortic walls under few training datasets conditions. The self-optimized ML includes three steps: Step 1: the forward physical FEM models first simulate the nonlinear deformation of aortic walls subject to uniaxial tension tests and are used to establish the nonlinear relationship datasets, Step 2: the carefully designed inverse random forest (RF) of the ML model can offer rapid identification by learning the established nonlinear relationship datasets, and Step 3: forward physical FEM models are recalled to evaluate the error between the identification results in Step 2 and real values, and then the accuracy are embedded into RF for guiding optimization directions to ensure that the final identification results are accurately and physically reasonable. The accuracy and robustness validation of proposed framework was conducted by constitutive parameters identification of uniaxial tension experiment samples of bovine aortic walls. The approach proposed achieves the R-squared exceeding 96.90% in longitudinal direction and 98.30% in circumferential direction, which is better than directly ML approach and gradient-based approach under the same amounts of datasets, respectively. The comparison results show that self-optimized ML can not only achieve accurate identification of the constitutive parameters of aortic walls, but also can decrease the identification results dependency of the initial number of sampling data effectively. The identification approach developed herein provides a common and convenient framework for constitutive parameters identification of biological soft tissues.

Grey-box modeling for thermal dynamics of buildings under the presence of unmeasured internal heat gains

An accurate grey-box building thermal model is an essential component for prediction-based control of split air conditioners. As the thermal performance of building envelope improves, the internal heat gains play an increasingly noteworthy influence on the building’s thermal dynamics. However, the internal heat gains are usually not measured, which makes the conventional data-dependent identification methods of building thermal models fail. To improve the accuracy of building thermal models under unmeasured internal heat gains, a two-step parameter identification framework for resistance–capacitance (RC) models is proposed in this paper. First, the time-invariant building model parameters are estimated by designing a novel optimization objective function. Next, the time-varying internal heat gains are identified based on the mismatch in predictions of the estimated building model from the first step and the measured temperature. The effectiveness of the method is evaluated using data from a simulation case and an experiment case. The results show that the proposed method outperforms the conventional identification method as well as two other identification methods that handle the unmeasured internal heat gains. Besides, the internal heat gains estimated by the proposed method can capture the trend of the actual internal loads well.

Enhancements in Immediate Speech Emotion Detection: Harnessing Prosodic and Spectral Characteristics

Speech is essential to human communication for expressing and understanding feelings. Emotional speech processing has challenges with expert data sampling, dataset organization, and computational complexity in large-scale analysis. This study aims to reduce data redundancy and high dimensionality by introducing a new speech emotion recognition system. The system employs Diffusion Map to reduce dimensionality and includes Decision Trees and K-Nearest Neighbors(KNN)ensemble classifiers. These strategies are suggested to increase voice emotion recognition accuracy. Speech emotion recognition is gaining popularity in affective computing for usage in medical, industry, and academics. This project aims to provide an efficient and robust real-time emotion identification framework. In order to identify emotions using supervised machine learning models, this work makes use of paralinguistic factors such as intensity, pitch, and MFCC. In order to classify data, experimental analysis integrates prosodic and spectral information utilizing methods like Random Forest, Multilayer Perceptron, SVM, KNN, and Gaussian Naïve Bayes. Fast training times make these machine learning models excellent for real-time applications. SVM and MLP have the highest accuracy at 70.86% and 79.52%, respectively. Comparisons to benchmarks show significant improvements over earlier models.

Multi-module attention-guided deep learning framework for precise gastrointestinal disease identification in endoscopic imagery

The automated classification of gastrointestinal endoscopy images holds immense importance in modern health care. It streamlines the diagnostic process by enabling faster and more accurate identification of gastrointestinal diseases. While the existing automated methods have demonstrated promising performance, there still remains a gap in consistently achieving high accuracy. This is due to reason that endoscopy images suffer from inter-class similarities and intra-class differences, which complicates the classification task. To address these problems, we propose a framework for endoscopy image classification. In general, the proposed framework comprises three essential modules. The first module is the Local-Global Convolutional Neural Network (LG-CNN) which aims to extract both local fine-grained features and captures global context, second module is the Endoscopy-Lesion Attention Module (ELA) that enables the framework to emphasize more crucial regions and filter out noises and other irreverent information. Finally, the last module, Gastrointestinal Endoscopy CNN (GE-CNN) leverages the above two modules in a effective way to classify the input image into various categories. We evaluate the performance of proposed framework on two publicly available challenging datasets, namely, Kvasir, and HyperKvasir. Based on the experimental results, we illustrate the efficacy of the proposed framework in effectively classifying endoscopy images.

A Secure Satellite-Edge Computing Framework for Collaborative Line Outage Identification in Smart Grid

A Secure Satellite-Edge Computing Framework for Collaborative Line Outage Identification in Smart Grid

A robust model predictive control unified framework for autonomous rendezvous and docking with a tumbling target

During autonomous rendezvous and docking (AR&D) with a tumbling target, multiple unfavorable engineering constraints exist, such as constraints on the control, velocity, or collision avoidance. Meanwhile, many sources of uncertainty exist, including incomplete and inaccurate measurements, control deviation, dynamic uncertainty, and inertial parameter uncertainty of the tumbling target, which significantly complicate the design of the guidance and control algorithm. Considering these challenges, this paper proposes a robust identification, planning, and control unified framework for AR&D with a tumbling target, which incorporates an extended Kalman filter (EKF) with a robust tube-based model predictive control for tracking (TMPT) controller. First, the EKF is designed to estimate the system state and inertial parameters of the target and then predict the future behavior of the tumbling target. Additionally, the prediction information of the target is transmitted to the designed TMPT controller as a reference signal. This TMPT controller unites trajectory planning and control in a single layer that can ensure the stability and recursive feasibility of the algorithm with a short prediction horizon. Moreover, it can guarantee that the closed-loop system is robustly and asymptotically convergent to the reachable set whose center is the optimal reachable periodic trajectory concerning the docking port of the tumbling target. The unified framework represents a robustness-to-optimality control strategy that can accomplish the AR&D mission efficiently while considering multiple engineering constraints and uncertainties. Finally, the efficiency of the proposed control framework is verified through a numerical simulation.

Early warnings of systemic risk using one-minute high-frequency data

This study uses high-frequency principal component analysis (HF PCA) to extract information from stock prices to monitor and measure systemic risk in the financial system. The empirical analysis carried out in this study using one-minute returns of stocks included in the Russel 3000 index from 2003 to 2021 shows a clear relationship between the size of the realized eigenvalues and systemic increases in financial stress. We also found that realized eigenvectors can trace the role of firms/sectors as potential sources of financial stress in different periods. We measured the transmission of shocks from (to) the financial sector to (from) other sectors and the real economy. This provides a tool for analyzing the spread of this financial instability that could affect the functioning of the financial system to the extent that the real economy is seriously damaged. HF PCA is a risk identification framework that allows policymakers and central banks to detect risks in real-time and address potential threats to financial stability with the most appropriate policy tools.

Investigation on the joint travel behavior in bike sharing systems during the COVID-19 pandemic: Insights from New York City

As the COVID-19 pandemic worsened, many people saw bikes as one of the safest means of transportation in the hard-hit cities. All the bike sharing utilization patterns during the pandemic are worthy of careful attention. However, there is still a lack of comprehensive understanding of niche but notable cycling behaviors, such as multi-person round trip (MPRT), defined as two or more cyclists intentionally riding together then returning bikes to the original docking station. This study extends the relevant literature by firstly proposing a MPRT identification framework based on individual bike sharing trip records, with consideration of interpersonal relationships between co-travelers, as well as the specificity of round trips against one-way trips. Taking New York City as a case study, this study examines the changes over space and time of MPRT frequencies from 2019 (i.e. pre-pandemic period) to 2020 (i.e. pandemic period), and the reasons for it. Notably, special consideration of the aforementioned analysis is paid to the influence of the real-time situation of COVID-19 in terms of cases, deaths, hospitalizations, and tests. Results reveal that (1) the MPRT frequencies obey a long tail distribution, both prior to and during the COVID-19 outbreak; (2) the group size, temporal patterns and co-traveler community are profoundly affected by the COVID-19 outbreak; (3) four indicators related to COVID-19 show different influences on co-travelers over time; (4) bike sharing availability and personal economic situation are closely related with MPRT frequencies. These findings can help develop more targeted strategies for improving the operation of a bike sharing system to meet the possible diversified demands of cyclists during the future pandemics.

Unified data integration and record identification framework for diverse data sources in healthcare demographics

Challenges of bridging data from different sources with diverse data formats are faced by organizations in the modern data management environment. Problems with disparate data sources leading to different formats and inconsistencies mean it can be challenging to get the right matching of data records, especially when information errors such as typos are present. The current lack of a standard pattern for data integration and record identification presents a major problem in ensuring the accurate identification of individual records across disparate sources. The variations in data formats and the abundance of errors, such as typographical mistakes in names, dates of birth, and gender, add to the complexity of this problem. Organizations face the challenge of ensuring the correctness and consistency of data across multiple datasets without a formalized methodology.

Bidirectional machine learning-assisted sensitivity-based stochastic searching approach for groundwater DNAPL source characterization.

In this study, we designed a machine learning-based parallel global searching method using the Bayesian inversion framework for efficient identification of dense non-aqueous phase liquid (DNAPL) source characteristics and contaminant transport parameters in groundwater. Swarm intelligence organized hybrid-kernel extreme learning machine (SIO-HKELM) was proposed to approximate the forward and inverse input-output correlation with a high accuracy using the DNAPL transport numerical simulation model. An adaptive inverse-HKELM was established for preliminary estimation of the source characteristics and contaminant transport parameters to correct prior information and generate high-quality initial starting points of parallel searching. A local accurate forward-HKELM surrogate of the numerical model was embedded in the searching system for avoiding repetitive CPU-demanding likelihood evaluations. A sensitivity-based Metropolis criterion (MC), incorporating the dynamic particle swarm optimization (SD-PSO) algorithm, was developed for improving the search ergodicity and realizing precise inversion of all the unknown variables with drastic variations in sensitivity to the likelihood function. Results showed that the generalization capability and robustness of SIO-HKELM were superior to those of the traditional machine learning methods, including KELM and support vector regression (SVR), and it sufficiently approximated the forward and inverse input-output mapping of the numerical model with testing determination coefficients of 0.9944 and 0.6440, respectively. With high-quality prior information and initial starting points generated by the adaptive inverse-HKELM feed approach, the uncertainty in the inversion outputs was reduced, and the searching process rapidly converged to reasonable posterior distributions in around 60 iterations. Compared with the widely used multichain Markov chain Monte Carlo (MCMC) approach, the parallel searching lines generated by SD-PSO-MC adequately covered the searching space, and the "equifinality" effect was more effectively restrained by reducing the relative errors of all the point estimations to less than 8%. Therefore, the real source information reflected by the statistical characteristics of the SD-PSO-MC inversion outputs was more precise than that obtained using the multichain MCMC approach.

DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features.

Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their visual similarity, leading to confusion among radiologists. To imitate those issues, we created an automated system with a large data hub that contains 17 datasets of chest X-ray images for a total of 71,096, and we aim to classify ten different disease classes. For combining various resources, our large datasets contain noise and annotations, class imbalances, data redundancy, etc. We conducted several image pre-processing techniques to eliminate noise and artifacts from images, such as resizing, de-annotation, CLAHE, and filtering. The elastic deformation augmentation technique also generates a balanced dataset. Then, we developed DeepChestGNN, a novel medical image classification model utilizing a deep convolutional neural network (DCNN) to extract 100 significant deep features indicative of various lung diseases. This model, incorporating Batch Normalization, MaxPooling, and Dropout layers, achieved a remarkable 99.74% accuracy in extensive trials. By combining graph neural networks (GNNs) with feedforward layers, the architecture is very flexible when it comes to working with graph data for accurate lung disease classification. This study highlights the significant impact of combining advanced research with clinical application potential in diagnosing lung diseases, providing an optimal framework for precise and efficient disease identification and classification.

Vein segmentation and visualization of upper and lower extremities using convolution neural network.

The study focused on developing a reliable real-time venous localization, identification, and visualization framework based upon deep learning (DL) self-parametrized Convolution Neural Network (CNN) algorithm for segmentation of the venous map for both lower and upper limb dataset acquired under unconstrained conditions using near-infrared (NIR) imaging setup, specifically to assist vascular surgeons during venipuncture, vascular surgeries, or Chronic Venous Disease (CVD) treatments. A portable image acquisition setup has been designed to collect venous data (upper and lower extremities) from 72 subjects. A manually annotated image dataset was used to train and compare the performance of existing well-known CNN-based architectures such as ResNet and VGGNet with self-parameterized U-Net, improving automated vein segmentation and visualization. Experimental results indicated that self-parameterized U-Net performs better at segmenting the unconstrained dataset in comparison with conventional CNN feature-based learning models, with a Dice score of 0.58 and displaying 96.7 % accuracy for real-time vein visualization, making it appropriate to locate veins in real-time under unconstrained conditions. Self-parameterized U-Net for vein segmentation and visualization has the potential to reduce risks associated with traditional venipuncture or CVD treatments by outperforming conventional CNN architectures, providing vascular assistance, and improving patient care and treatment outcomes.

Smart city and green innovation: Mechanisms and industrial emission reduction effect

Green innovation is essential for environmentally sustainable development. The construction of smart cities offers significant potential for developing green innovation through optimizing urban administration and improving the allocation of critical resources. Using Chinese city data from 2005 to 2019, this study adopts a causal identification framework based on the multi-temporal difference-in-difference method to explore the impact of smart city construction on green innovation and the mechanism and joint industrial emission reduction effect between them. A positive and significant relationship with a weak inverted U-shaped trend was found between smart city construction and green innovation. Besides the direct channel, labor factor allocation, venture capital attractiveness, and market accessibility are essential indirect channels between the two concepts. Furthermore, the effects of smart city construction on green innovation varied depending on the marketization level, administrative rank, population size, and geographic location of the city. In addition, the interaction of the two constructs negatively affected industrial emissions, which helped optimize the environment. These findings suggest that smart city construction offers a digital dividend for developing green innovation and creating an efficient, sustainable environment.

DeepOCR: A multi-species deep-learning framework for accurate identification of open chromatin regions in livestock

A wealth of experimental evidence has suggested that open chromatin regions (OCRs) are involved in many critical biological activities, such as DNA replication, enhancer activity, and gene transcription. Accurately identifying OCRs in livestock species can provide critical insights into the distribution and characteristics of OCRs for disease treatment in livestock, thereby improving animal welfare. However, most current machine-learning methods for OCR prediction were originally designed for a limited number of model organisms, such as humans and some model organisms, and thus their performance on non-model organisms, specifically livestock, is often unsatisfactory. To bridge this gap, we propose DeepOCR, a lightweight depth-separable residual network model for predicting OCRs in livestock, including chicken, cattle, and sheep. DeepOCR integrates a single convolution layer and two improved residue structure blocks to extract and learn important features from the input DNA sequences. A fully connected layer was also employed to further process the extracted features and improve the robustness of the entire network. Our benchmarking experiments demonstrated superior prediction performance of DeepOCR compared to state-of-the-art approaches on testing datasets of the three species. The source code of DeepOCR is freely available for academic purposes at https://github.com/jasonzhao371/DeepOCR/. We anticipate DeepOCR servers as a practical and reliable computational tool for OCR-related studies in livestock species.

A novel quantum Dempster's rule of combination for pattern classification

Dempster's rule of combination (DRC) is widely used for uncertainty reasoning in intelligent information system, which is generalized to complex domain recently. However, as the increase of identification framework elements, the computational complexity of Dempster's rule of combination increases exponentially. To address this issue, we propose a novel quantum Dempster's rule of combination (QDRC) by means of Toffoli gate. The QDRC combination process is completely implemented using quantum circuits. To validate the efficiency of the proposed QDRC, we conduct simulation experiments on IBM and IonQ quantum cloud platforms. Besides, we implement the QDRC algorithm to the real quantum computer of IonQ Harmony. The experiment results show that the result of QDRC is getting more and more accurate as the number of measurements increases. In addition, we propose a quantum multisource information fusion method on the basis of QDRC. Finally, we demonstrate its effectiveness to solve pattern classification problem under several diverse real UCI datasets.

Blockchain-driven framework for construction waste recycling and reuse

Conventional management mechanisms for construction waste recycling and reuse (CWRR) often cause considerable information asymmetry, inadequate government supervision, and imperfect incentive mechanisms, resulting in frequent illegal dumping and landfilling of construction waste and relatively low recycling rates. To address these issues, we introduce blockchain technology to CWRR. The design science research methodology was adopted to identify the main steps in functional requirement identification and blockchain framework development for CWRR. To explain how blockchain can improve the CWRR process, a conceptual model for the functional requirements of the proposed framework was constructed using qualitative analysis. A blockchain-driven framework was developed to overcome practical barriers in the CWRR industry. Based on scenario simulation results, the proposed framework had execution and transaction costs of $4.735 and $1.276, respectively, and latency performance at the millisecond level. The results indicate that (1) based on specific problems systematically identified from CWRR practices, the proposed framework can address practical barriers in the CWRR industry more directly; (2) the CWRR industry can use blockchain technology to achieve information sharing, comprehensive government supervision, and effective incentive mechanisms and (3) the blockchain-driven framework has high efficacy and can promote efficient CWRR and high-quality development of CWRR industry chain. This management model is conducive to forming a collaborative CWRR industry chain that can lead to broader adoption of blockchain technology across industries.

A framework for identification of brain tumors from MR images using progressive segmentation

PurposeThis study addresses the critical health issue of brain tumors, focusing on enhancing the accuracy of tumor segmentation from Magnetic Resonance Imaging (MRI) images. The primary research question investigates the effectiveness of a novel Hybrid Watershed–Clustering framework and its underlying Progressive Segmentation of the MR Images using the Radius and Intensity Measure (PS-RIM) algorithm. The aim is to improve the detection and segmentation of brain tumors within MR images, surpassing the efficacy of current methodologies.MethodsThe methodology involves a three-stage process. In the preprocessing stage, noise reduction and intensity normalization techniques are applied to clarify the images. The next stage is region-based segmentation, which includes morphological processing, edge detection, and thresholding to delineate tumor areas accurately. The final post-processing stage enhances segmentation accuracy and reduces false positives by integrating clustering machine learning techniques, specifically the K-Means cluster algorithm, to refine tumor identification.ResultsThe framework's comprehensive evaluation across various MR images shows a significant improvement in accuracy over existing segmentation methods. The PS-RIM algorithm within the framework effectively captures the diverse presentations of tumor appearances in MR images. The research recorded an impressive accuracy rate of 98.11% in tumor detection, demonstrating enhanced identification and segmentation quality.ConclusionsThe study concludes that the proposed Hybrid Watershed–Clustering framework, powered by the PS-RIM algorithm, markedly improves the detection and differentiation of brain tumors in MR images. It exhibits exceptional accuracy, resilience, and computational efficiency. These findings hold substantial potential for advancing computer vision and image analysis in medical diagnostics, which could improve patient outcomes in managing brain tumors.Graphical abstract