Reviewerly/exHarmony huggingface dataset:
MR-CT multi-atlas registration guided by fully automated brain structure segmentation with CNNs

Abstract Purpose Computed tomography (CT) is widely used to identify anomalies in brain tissues because their localization is important for diagnosis and therapy planning. Due to the insufficient soft tissue contrast of CT, the division of the brain into anatomical meaningful regions is challenging and is commonly done with magnetic resonance imaging (MRI). Methods We propose a multi-atlas registration approach to propagate anatomical information from a standard MRI brain atlas to CT scans. This translation will enable a detailed automated reporting of brain CT exams. We utilize masks of the lateral ventricles and the brain volume of CT images as adjuvant input to guide the registration process. Besides using manual annotations to test the registration in a first step, we then verify that convolutional neural networks (CNNs) are a reliable solution for automatically segmenting structures to enhance the registration process. Results The registration method obtains mean Dice values of 0.92 and 0.99 in brain ventricles and parenchyma on 22 healthy test cases when using manually segmented structures as guidance. When guiding with automatically segmented structures, the mean Dice values are 0.87 and 0.98, respectively. Conclusion Our registration approach is a fully automated solution to register MRI atlas images to CT scans and thus obtain detailed anatomical information. The proposed CNN segmentation method can be used to obtain masks of ventricles and brain volume which guide the registration.
Missing Value Imputation Using Weighted KNN and Genetic Algorithm

Missing data cause many challenges for imputing missing value in real-world datasets. Much research has been done on these challenges, but most existing research focuses on the classification task. Only a few methods can handle the large datasets for imputation of missing value. This paper proposes a new hybrid approach to impute the missing value. This approach is based on a Weighted k-nearest neighbor (WKNN) and genetic programming algorithm. This approach aims to enhance the accuracy of the imputation of missing value in symbolic regression. This paper has used different datasets with a different missing ratio of data and applied the imputation model to the datasets. This approach makes exact imputation compared to other methods like Decision Tree, Genetic programming imputation, Bayesian Regression, logistic regression, WKNN, Multilayer Perceptron, Random Forest, and Support Vector Regression.
MWO2KG and Echidna: Constructing and exploring knowledge graphs from maintenance data

Unstructured technical texts are a rich resource of engineering knowledge underutilised for data analysis. Maintenance work orders (MWO), for example, capture valuable information to inform what work was done on an asset and why. Data in MWO short text fields is unstructured, terse and jargon-rich, complicating the ability of both humans and machines to read it. Our challenge is to efficiently extract technical information from the MWO short text field and combine it with data in structured fields such as dates, functional location, make and model of the asset. In this paper we present a technical language processing-based solution for this problem. Echidna is an intuitive query-enabling interface that visualises historic asset data in the form of a knowledge graph. This knowledge graph is produced by MWO2KG, which uses deep learning supported by annotated training data to automatically construct knowledge graphs from unstructured technical text combined with data from structured fields. The tools are tested on maintenance work order and delay accounting data provided by industry partners. These tools provide reliability engineers with an efficient way to find information in historic asset data for failure modes and effects analysis, maintenance strategy validation and process improvement work. Source code for both tools is available on GitHub under the Apache 2.0 License.
A New and More Challenging Compositive Multi-Task Optimization Problem Test Suite

Evolutionary multi-task optimization (EMTO) is a recently emerging topic in the research area of evolutionary computation, aiming at solving multiple different optimization tasks simultaneously. Recently, with the development of EMTO, many multi-task optimization problem (MTOP) benchmark test suites have been proposed. However, these MTOP test suites are not sufficiently challenging. That is, the tasks in these MTOPs always have the same dimensionality and the aligned dimensions of the tasks can have related physical meanings or similar optimal values. To address these disadvantages of existing MTOP test suites, we propose a new and more challenging compositive MTOP (cMTOP) test suite. There are ten cMTOP instances in this test suite and each instance contains two tasks. These cMTOP instances are more challenging with three properties. First, the tasks in each cMTOP have different numbers of dimensions. Second, to make the aligned dimensions of two tasks have irrelated physical meanings, each task is a composition function composed of several basic functions. Third, each task in cMTOP is shifted and rotated to make the aligned dimensions of the two tasks have different optimal values. Based on these properties, the proposed cMTOP test suite is more challenging and can better evaluate the performance of EMTO algorithms. Finally, we analyze the performance of several state-of-the-art EMTO algorithms on this new and challenging cMTOP test suite.
Enriching Style Transfer in multi-scale control based personalized end-to-end speech synthesis

Personalized speech synthesis aims to transfer speech style with a few speech samples from the target speaker. However, pretrain and fine-tuning techniques are required to overcome the problem of poor performance for similarity and prosody in a data-limited condition. In this paper, a zero-shot style transfer framework based on multi-scale control is presented to handle the above problems. Firstly, speaker embedding is extracted from a single reference speech audio by a specially designed reference encoder, with which Speaker-Adaptive Linear Modulation (SALM) could generate the scale and bias vector to influence the encoder output, and consequently greatly enhance the adaptability to unseen speakers. Secondly, we propose a prosody module that includes a prosody extractor and prosody predictor, which can efficiently predict the prosody of the generated speech from the reference audio and text information and achieve phoneme-level prosody control, thus increasing the diversity of the synthesized speech. Using both objective and subjective metrics for evaluation, the experiments demonstrate that our model is capable of synthesizing speech of high naturalness and similarity of speech, with only a few or even a single piece of data from the target speaker.
End-to-End Model Based on Bidirectional LSTM and CTC for Online Handwritten Mongolian Word Recognition

An end-to-end model for Traditional Mongolian online handwritten word recognition is proposed in this paper. According to the characteristics of input and output data, the proposed model consists of a bidirectional Long Short-Term Memory(LSTM) network and a Connectionist Temporal Classification(CTC) network. Bidirectional LSTM network is the core of the model, and the CTC network is added to LSTM network. The key step of this research is to switch from the LSTM network output to the conditional probability distribution on the label sequence through the CTC layer. Therefore, for each given input sequence, the model completes the recognition task by choosing the most possible label. In addition, There is not many researchs on online handwritten Mongolian recognition. Therefore, in this study, we will also focus on recognizing wrong labels, finding out the types of errors, and analyzing the possible causes of errors.
A Horizontal Federated Random Forest for Heart Disease Detection from Decentralized Local Data

In the modern world, reliable data is a thriving need in every sector. As the data increases, maintaining data privacy is also becoming a big concern. The healthcare sector is no different than that. Privacy in the healthcare sector is a topmost concern when sharing with other institutes. As data from a single healthcare institute is not always enough to get properly predicted outputs in machine learning approaches. There comes the idea of sharing data among multiple hospitals for having a more specified model with keeping the data details private. So, we have designed a model combining a federated central model and clients for the application of Federated Learning on heart disease patients' data. Here, we have implemented an approach for sharing only the model parameters among the clients and central in horizontal federated learning infused with random forest. At the evaluation of our model, we have come up with improved accuracy of 7.1, 2, and 6 percent respectively for the federated central and both clients.
In-Sensor Neural Network Preprocessing for ADAS Computer Systems

Currently available automotive radars are designed to stream real-time 2D image data over high-speed links to a central ADAS (Advance Driver-Assistance System) computer for object recognition, which considerably contributes to the system's power consumption and complexity. This paper presents a preliminary work for the implementation of a new in-sensor computer architecture to extract representative features from raw sensor data to detect and identify objects with radar signals. Such new architecture makes it possible to reduce the data transferred between sensors and the central ADAS computer significantly, giving rise to significant energy savings and latency reductions, while still maintaining sufficient accuracy and preserving image details. An experimental prototype has been built using the Texas Instruments AWR1243 Frequency-Modulated Continuous Wave (FMCW) radar board. We carried out experiments using the prototype to collect radar images, to preprocess raw data, and to transfer feature vectors to the central ADAS computer for classification and object detection. Two different approaches will be presented in this paper: First, a vanilla autoencoder will demonstrate the possibility of data reduction on radar signals. Second, a convolutional neural network based cross-domain deep learning architecture is presented by using a sample dataset to show the feasibility of computing Range-Angle Heatmaps directly on the sensor board eliminating the need for the raw data preprocessing on the central ADAS computer. We show that the reconstruction of Range-Angle Heatmaps can be predicted with a very high accuracy by leveraging deep learning architectures. Implementation of such a deep learning architecture on the sensor board can reduce the amount of data transferred from sensors to the central ADAS computer implying great potential for an energy efficient deep learning architecture in such environments.
Quantitative safety-security risk analysis of interconnected cyber-infrastructures

Modern day cyber-infrastructures are critically dependent on each other to provide essential services. Current frameworks typically focus on the risk analysis of an isolated infrastructure. Evaluation of potential disruptions taking the heterogeneous cyber-infrastructures is vital to note the cascading disruption vectors and determine the appropriate interventions to limit the damaging impact. This paper presents a cyber-security risk assessment framework for the interconnected cyber-infrastructures. Our methodology is designed to be comprehensive in terms of accommodating accidental incidents and malicious cyber threats. Technically, we model the functional dependencies between the different architectures using reliability block diagrams (RBDs). RBDs are convenient, yet powerful graphical diagrams, which succinctly describe the functional dependence between the system components. The analysis begins by selecting a service from the many services that are outputted by the synchronized operation of the architectures whose disruption is deemed critical. For this service, we design an attack fault tree (AFT). AFT is a recent graphical formalism that combines the two popular formalisms of attack trees and fault trees. We quantify the attack-fault tree and compute the risk metrics - the probability of a disruption and the damaging impact. For this purpose, we utilize the open source ADTool. We show the efficacy of our framework with an example outage incident.
A Novel Hybrid Approach for Intent Creation and Detection Using K-Means-Based Topic Clustering and Heuristic-Based Capsule Network

Background: The social media revolution has offered new facilities and opportunities to the online community to communicate their intentions, opinions, and views regarding products, services, policies, and events. The identification of intent focuses on the detection of intents from user reviews, that is, whether the specific review of the user includes intention or not. Intent mining is also named intent identification which helps business organizations to identify the purchase intentions of users. However, detecting user intentions encoded in text queries is a complicated task in several Natural Language Processing (NLP) applications such as robots, smart agents, personal assistants, and search engines. The existing research works have discovered the utilization of several machine learning techniques to detect the intents from queries of users. Most works consider intent detection as a classification problem, with utterances as predefined intents. Research question: Whether the researcher resolves the detection of user intentions encoded in text queries? How the researcher solves the existing challenges based on intent mining? Purpose: The main contribution of the research is to design and implement intent detection using topic clustering and deep learning. Methodology: Initially, the dataset related to diverse queries is gathered. Then, the label creation is performed by clustering. The clustering is performed by a k-means clustering model with a cosine similarity function. Once the clustering is performed for different queries, the label is created, which is used to train the network under the detection process. For the detection, this paper uses a Heuristic-based Capsule Network (H-CapNet) that could perform the intention for a new query. The hybrid meta-heuristic algorithm with Escaping Energy searched Grey–Harris Hawks Algorithm (EEG-HHA) is used for improving the capsule network. Validation: Experimental analysis shows that the developed method has superior performance in evaluating standard datasets with other approaches. Results: From the simulation results, the accuracy of the developed EEG-HHA-CapNet for dataset 1 is secured at 3%, 1.6%, 2%, and 1.1% increased than PSO-CapNet, WOA-CapNet, HHO-CapNet, and GWO-CapNet. Conclusion: Thus, the designed user intent detection models reveal their more advanced performance based on the diverse performance and error metrics for datasets 1 and 2.
Effects of <scp>3D</scp> virtual reality motionless imagery training program with an avatar

Abstract The present study aimed to explore the effects of motionless imagery training with an avatar in virtual reality (VR) on emotion, cognition, and physiological response changes in healthy adults. Participants were 30 healthy adults aged between 19 and 35 years. All participants were randomly divided into the experimental group ( n = 18), which executed the imagery training with an avatar in VR, or the control group ( n = 12), which merely experienced the VR without an avatar. Both groups underwent the intervention, a 20‐min session, 3 times a week for 6 weeks. VR experience questionnaires and physiological response changes were measured at pre‐ and post‐test and emotional states and cognition tests were measured at pre‐, post‐, and follow‐up test. The experimental group showed no significant changes in the Presence Questionnaire (PQ) and the Simulator Sickness Questionnaire (SSQ) after the intervention while the control group showed a significant decrease in the PQ after the intervention. In all emotional states, there were no significant differences in the interaction between times and groups. A significant main effect of time was revealed in all cognition tests except the delayed recall and the delayed recognition in K‐Auditory Verbal Learning Test (K‐AVLT). In physiological response changes, the experimental group showed significant improvements in the electromyogram (EMG) at rectus femoris on the left side after the intervention. Thus, imagery training with an avatar in VR can be considered to be effective for enhancements of cognitions and physiological response changes.
Applications of Digital and Computational Pathology and Artificial Intelligence in Genitourinary Pathology Diagnostics

As machine learning (ML) solutions for genitourinary pathology image analysis are fostered by a progressively digitized laboratory landscape, these integrable modalities usher in a revolution in histopathological diagnosis. As technology advances, limitations stymying clinical artificial intelligence (AI) will not be extinguished without thorough validation and interrogation of ML tools by pathologists and regulatory bodies alike. ML solutions deployed in clinical settings for applications in prostate pathology yield promising results. Recent breakthroughs in clinical artificial intelligence for genitourinary pathology demonstrate unprecedented generalizability, heralding prospects for a future in which AI-driven assistive solutions may be seen as laboratory faculty, rather than novelty.
Taylor political monarch butterfly optimization driven deep learning model for acute lymphoblastic leukemia detection and severity classification using blood smear images

Summary An image processing and classification become an exciting research region for indicative probe because of the expansion of digital microscopic imaging. Acute lymphoblastic leukemia (ALL) is caused owing to broken in bone marrow, which is general among the people around the biosphere. A leukemia generally affects blood creating organs and hence, easily spread to other tissues by blood. The hematologists afflict more to differentiate leukemia existence in body of patients based on blood smears. Hence, in this article, Taylor political monarch butterfly optimization‐enabled deep residual neural network (Taylor PMBO‐enabled DRN) is devised for ALL classification. Segmentation is the process of separating the images into small pixels. A deep joint segmentation model effectively enhances the detection accuracy and it has better segmentation capability. For a better classification procedure, the important features, such as statistical, texture, and grid features, are extracted. For ALL detection and classification processes, a deep residual neural network (DRN) classifier is trained by the Taylor PMBO. The devised Taylor PMBO‐based DRN model obtained better performance than other method by testing accuracy, sensitivity, specificity, F1‐score, and precision of 0.9656, 0.9666, 0.9709, 0.9280, and 0.9321.
Adaptive fuzzy C-means clustering integrated with local outlier factor

The conventional fuzzy C-means (FCM) is sensitive to the initial cluster centers and outliers, which may cause the centers deviate from the real centers when the algorithm converges. To improve the performance of FCM, a method of initializing the cluster centers based on probabilistic suppression is proposed and an improved local outlier factor is integrated into the model of FCM. Firstly, the probability of an object as cluster center is defined by its local density, and all initial centers are obtained by the cluster center’s probability and probability suppression function incrementally. Next, an improved local outlier factor is reconstructed according to the local distribution of an object, and its reciprocal is regarded as the contribution degree of an object to cluster center. Then, the improved local outlier factor is integrated into FCM to alleviate the negative effect caused by outliers. Finally, experiments on synthetic and real-world datasets are provided to demonstrate the clustering performance and anti-noise ability of proposed method.
A computational classification method of breast cancer images using the VGGNet model

Cancer is one of the most prevalent diseases worldwide. The most prevalent condition in women when aberrant cells develop out of control is breast cancer. Breast cancer detection and classification are exceedingly difficult tasks. As a result, several computational techniques, including k-nearest neighbor (KNN), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), and genetic algorithms, have been applied in the current computing world for the diagnosis and classification of breast cancer. However, each method has its own limitations to how accurately it can be utilized. A novel convolutional neural network (CNN) model based on the Visual Geometry Group network (VGGNet) was also suggested in this study. The 16 layers in the current VGGNet-16 model lead to overfitting on the training and test data. We, thus, propose the VGGNet-12 model for breast cancer classification. The VGGNet-16 model has the problem of overfitting the breast cancer classification dataset. Based on the overfitting issues in the existing model, this research reduced the number of different layers in the VGGNet-16 model to solve the overfitting problem in this model. Because various models of the VGGNet, such as VGGNet-13 and VGGNet-19, were developed, this study proposed a new version of the VGGNet model, that is, the VGGNet-12 model. The performance of this model is checked using the breast cancer dataset, as compared to the CNN and LeNet models. From the simulation result, it can be seen that the proposed VGGNet-12 model enhances the simulation result as compared to the model used in this study. Overall, the experimental findings indicate that the suggested VGGNet-12 model did well in classifying breast cancer in terms of several characteristics.
Maneuvering target state estimation based on separate modeling of target trajectory shape and dynamic characteristics

The state estimation of a maneuvering target, of which the trajectory shape is independent on dynamic characteristics, is studied.The conventional motion models in Cartesian coordinates imply that the trajectory of a target is completely determined by its dynamic characteristics.However, this is not true in the applications of road-target, sea-route-target or flight route-target tracking, where target trajectory shape is uncoupled with target velocity properties.In this paper, a new estimation algorithm based on separate modeling of target trajectory shape and dynamic characteristics is proposed.The trajectory of a target over a sliding window is described by a linear function of the arc length.To determine the unknown target trajectory, an augmented system is derived by denoting the unknown coefficients of the function as states in mileage coordinates.At every estimation cycle except the first one, the interaction (mixing) stage of the proposed algorithm starts from the latest estimated base state and a recalculated parameter vector, which is determined by the least squares (LS).Numerical experiments are conducted to assess the performance of the proposed algorithm.Simulation results show that the proposed algorithm can achieve better performance than the conventional coupled model-based algorithms in the presence of target maneuvers.