Reviewerly/exHarmony huggingface dataset:
Structurally-constrained optical-flow-guided adversarial generation of synthetic CT for MR-only radiotherapy treatment planning

The rapid progress in image-to-image translation methods using deep neural networks has led to advancements in the generation of synthetic CT (sCT) in MR-only radiotherapy workflow. Replacement of CT with MR reduces unnecessary radiation exposure, financial cost and enables more accurate delineation of organs at risk. Previous generative adversarial networks (GANs) have been oriented towards MR to sCT generation. In this work, we have implemented multiple augmented cycle consistent GANs. The augmentation involves structural information constraint (StructCGAN), optical flow consistency constraint (FlowCGAN) and the combination of both the conditions (SFCGAN). The networks were trained and tested on a publicly available Gold Atlas project dataset, consisting of T2-weighted MR and CT volumes of 19 subjects from 3 different sites. The network was tested on 8 volumes acquired from the third site with a different scanner to assess the generalizability of the network on multicenter data. The results indicate that all the networks are robust to scanner variations. The best model, SFCGAN achieved an average ME of 0.9 5.9 HU, an average MAE of 40.4 4.7 HU and 57.2 1.4 dB PSNR outperforming previous research works. Moreover, the optical flow constraint between consecutive frames preserves the consistency across all views compared to 2D image-to-image translation methods. SFCGAN exploits the features of both StructCGAN and FlowCGAN by delivering structurally robust and 3D consistent sCT images. The research work serves as a benchmark for further research in MR-only radiotherapy.
Developing machine learning-based models to predict intrauterine insemination (IUI) success by address modeling challenges in imbalanced data and providing modification solutions for them

This study sought to provide machine learning-based classification models to predict the success of intrauterine insemination (IUI) therapy. Additionally, we sought to illustrate the effect of models fitting with balanced data vs original data with imbalanced data labels using two different types of resampling methods. Finally, we fit models with all features against optimized feature sets using various feature selection techniques.The data for the cross-sectional study were collected from 546 infertile couples with IUI at the Fatemehzahra Infertility Research Center, Babol, North of Iran. Logistic regression (LR), support vector classification, random forest, Extreme Gradient Boosting (XGBoost) and, Stacking generalization (Stack) as the machine learning classifiers were used to predict IUI success by Python v3.7. We employed the Smote-Tomek (Stomek) and Smote-ENN (SENN) resampling methods to address the imbalance problem in the original dataset. Furthermore, to increase the performance of the models, mutual information classification (MIC-FS), genetic algorithm (GA-FS), and random forest (RF-FS) were used to select the ideal feature sets for model development.In this study, 28% of patients undergoing IUI treatment obtained a successful pregnancy. Also, the average age of women and men was 24.98 and 29.85 years, respectively. The calibration plot in this study for IUI success prediction by machine learning models showed that between feature selection methods, the RF-FS, and among the datasets used to fit the models, the balanced dataset with the Stomek method had well-calibrating predictions than other methods. Finally, the brier scores for the LR, SVC, RF, XGBoost, and Stack models that were fitted utilizing the Stomek dataset and the chosen feature set using the Random Forest technique obtained equal to 0.202, 0.183, 0.158, 0.129, and 0.134, respectively. It showed duration of infertility, male and female age, sperm concentration, and sperm motility grading score as the most predictable factors in IUI success.The results of this study with the XGBoost prediction model can be used to foretell the individual success of IUI for each couple before initiating therapy.
Visual Error Constraint Free Visual Servoing Using Novel Switched Part Jacobian Control

This research advances the state of the image-based visual servoing (IBVS) of robotic arms to handle very large visual errors without the camera advance/retreat problem. Conventional visual servoing schemes either consist of a partitioned or a switched system that relies on the feature Jacobian to find a unique feature for partitioning control along the specific DoF. We suggest a new IBVS scheme based on part-manipulator Jacobian approach for building a hybrid switched-partitioned task jacobian without the need to define new features. Utilizing this computationally efficient, directly defined Part-manipulator Jacobian an efficient second order minimization(ESM) based adaptive switching controller was constructed. The proposed scheme was tested in the eye-in-hand configuration on a 6-DoF simulated robotic arm and a 7-DoF real robotic arm for a set of large visual errors between the initial and the desired frames, including a rotational error of 180° around the camera optical axis. Compared to other IBVS schemes under various simulation conditions, the performance of the proposed scheme remained superior to that of the Jacobian-pseudo-inverse and other ESM-based IBVS schemes. The experimental results showed a notable expansion of the convergence zone up to 180° rotational errors with a 40% improvement in the convergence rates with a significant 90% reduction in the joint velocities and joint energies required to complete the task. The proposed controller possesses no camera advance/retreat motion, has a task Jacobian matrix that is well-conditioned, and it is computationally efficient. Moreover, the method is independent of the robot’s DoF and is extendable to other visual servoing schemes.
An Artificial Intelligence Technology Based Algorithm for Solving Mechanics Problems

As an indispensable technology of intelligent education, intelligent tutorial algorithms for solving mathematical or physical problems have attracted much attention in recent years. Nevertheless, since solving mechanics problems requires complex force analysis and motion analysis, current researches are mainly focus on solving geometry proof problems and direct circuit problems. There are some inherent challenges on developing such algorithms, including the low intelligence, mobility and interpretability of the comprehension algorithm. Therefore, this article develops a novel algorithm for solving mechanics problems. First, we propose a comprehension model for mechanics problems and convert problem understanding into relation extraction. Furthermore, a novel neural model combining pretrained model BERT and graph attention network (GAT) is proposed to extract the direct conditions of input mechanics problems. Second, a hidden information mining method is proposed for supplementing the conditions of the input problem. Third, a predicate logic based algorithm is proposed for force analysis. Finally, a solving algorithm is presented for choosing equations to acquire the solutions. Solving experiments and sensitivity analysis are provided to demonstrate the effectiveness of the proposed algorithm.
Multiuser Physical-Layer Authentication Based on Latent Perturbed Neural Networks for Industrial Internet of Things

Recently, learning (DL)-based physical-layer authentication (PLA) has attracted much attention since artificial neural networks (ANNs) can be built to extract useful features from complex wireless environments, thus achieving high authentication performance and lightweight deployment in mobile edge computing (MEC)-Industrial Internet of Things (IIoT) scenario. However, the low latency characteristic of MEC makes it impossible to have much time to obtain sufficient signals for training the authentication system, which will cause over-fitting issues and deteriorate the authentication performance. Data augmentation is an effective method to address this problem. However, existing PLA with data augmentation can not generate representative and high-quality samples, consequently lacking generality in the actual identity authentication. To tackle this problem, a novel channel impulse response (CIR)-based multiuser authentication named latent perturbed neural networks (LPNNs) is proposed in this article, aiming at achieving high authentication performance even when trained a few data. Instead of relying on the generation of synthetic samples, the proposed LPNN adds Gaussian noise in the smooth latent space to avoid underdetermined and poor generalization, which has better interpretability. Specifically, to obtain a better understanding than a black box that connects input CIRs to authentication results, we defined Fingerprint Library and provided post-hoc explanations to answer the following question: which library examples explain the authentication results issued for a given CIR sample? Moreover, the simulations under the static and dynamic IIoT scenarios verify the superiority in authentication accuracy of the proposed LPNN over vanilla deep neural network (DNN) and convolutional neural network (CNN).
GMIF: A Gated Multiscale Input Feature Fusion Scheme for Scene Text Detection

The feature fusion of the multi-scale features plays a significant role in localizing text instances of different sizes in the scene text detection (STD) paradigm. The existing approaches are not sufficient to tackle the issues of multi-scale text; consequently, their performance also varies with the text size. Here, we propose a gated multi-scale input feature fusion (GMIF) approach to overcome this issue in STD. The GMIF generates the local features from down-scaled input images and propagates these features from low resolution to the higher resolution global features through a gated recurrent unit-like mechanism. The consistent performance of the GMIF is validated with different text instance sizes of the test-set of the Total-text dataset. The GMIF obtained the performance in range (Precision 88.554-89.106, Recall 85.452-85.790, and f-measures 87.072 - 87.417) with marginal deviation, whereas the current state-of-the-art method, DBNet++, acquired in range (Precision 73.005-82.666, Recall 80.912-87.274, and f-measures 76.755 - 84.183) with significant deviation. Besides this, GMIF also achieved the best performance (f-measures) over ICDAR 2015 (as 88.0), Total-Text (as 87.4), and the second-best over the MSRA-TD500 (as 85.2) dataset. We have conducted an ablation study to show the impact of different components of the GMIF on the STD tasks, which shows the effectiveness of the overall GMIF approach.
Projective Bisector Mirror (PBM): Concept and Rationale

Our world is full of cameras, whether they are installed in the environment or integrated into mobile devices such as mobile phones or head-mounted displays. Displaying external camera views in our egocentric view with a picture-in-picture approach allows us to understand their view; however, it would not allow us to correlate their viewpoint with our perceived reality. We introduce Projective Bisector Mirrors for visualizing a camera view comprehensibly in the egocentric view of an observer with the metaphor of a virtual mirror. Our concept projects the image of a capturing camera onto the bisecting plane between the capture and the observer camera. We present extensive mathematical descriptions of this novel paradigm for multi-view visualization, discuss the effects of tracking errors and provide concrete implementation for multiple exemplary use-cases.
Network Pruning by Feature Map Sharing with K-Means Clustering

Network pruning can be used to reduce the size of a DNN (deep neural network). As a result, the memory storage requirement for a DNN can be saved. In this paper, we address the similarity of feature maps. Based on that observation, for each layer of a DNN, we use K-means clustering to group the channels with similar feature maps. Then, the channels in the same group can share the same feature map for network pruning. We have used VGG16 to test the effectiveness of the proposed approach. Experiment results show that the proposed approach can save about 45.7% memory storage requirement. Note that, after retraining, the accuracy loss of the pruned DNN is at most 2.1%.
Q-learning based Collision-free and Optimal Path Planning for Mobile Robot in Dynamic Environment

Mobile robots with artificial intelligence are more and more popular on the rescue and human-service in complex environment. Path planning techniques for robots become the important topic to achieve it. Recently, Q-learning becomes a popular topic since the property of model-free. In this paper, generating the collision-free and optimal path with Q-learning for an mobile robot is proposed. Q-learning is adopted to let the mobile robot achieve the destination successfully through designing the states, actions and reward function in this paper. The system structure is integrated by two parts. First, the Q-learning algorithm is applied to find the collision-free and optimal path for an mobile robot. Second, Robot Operation System (ROS) is used to be the data transmission system among the dynamic path planning system, global position system and mobile robot. In the simulation result, the dynamic path planning system generates the collision-free and optimal path for the mobile robot. In addition, the movable obstacles appear on the original path suddenly, then the dynamic path planning system would regenerate a new optimal path to achieve the goal successfully.
Using t-SNE to Evaluate the Brand Style of New Mice Design

Machine learning has gradually become popular in product design, including prediction, classification, evaluation, interpretation, etc. Using machine learning to evaluate the brand style of new mice designs is more objective than the designer's evaluation. This study collects two brand styles of computer mice: Mad Catz and Genius styles. A total of 30 participants created new mice designs in this investigation. The experiments on the computing thinking concept could allow students to practice the mastery of brand style and the recognition of design features. The results revealed that applying the t-SNE approach identifies two different mouse brand styles well and evaluates the brand style of new mice design.
The Development of Robot-based Storytelling Platform for Designing STEAM Learning Systems

Science, technology, engineering, arts, and mathematics (STEAM) education has attracted some attention in recent years. The use of robots and physical objects to improve learners' STEAM knowledge acquisition is well established. However, platforms for developing robot-based narrative applications were rare. A suitable designed scenario of humanrobot interaction (HRI) in storytelling may foster learners' engagement in learning activities. The design of HRI is crucial for learners to physically interact with storytelling robots. The above facts in a simple form by saying that the development of robotbased storytelling has not only affected learners' learning outcomes but cultivated their narrative skills. Therefore, the proposed study developed a Robot-based Storytelling Platform (RSP) to help creators to design their robot-based storytelling systems for particular applications.
Biometric User Identification by Forearm EMG Analysis

The recent experience in the use of virtual reality (VR) technology has shown that users prefer Electromyography (EMG) sensor-based controllers over hand controllers. The results presented in this paper show the potential of EMG-based controllers, in particular the Myo armband, to identify a computer system user. In the first scenario, we train various classifiers with 25 keyboard typing movements for training and test with 75. The results with a 1-dimensional convolutional neural network indicate that we are able to identify the user with an accuracy of 93% by analyzing only the EMG data from the Myo armband. When we use 75 moves for training, accuracy increases to 96.45% after cross-validation.
Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna

Automatic Dependent Surveillance-Broadcast (ADS-B) signals are very vital in air traffic control. However, the space-based ADS-B signals are easily overlapped and their message cannot be correctly received. It is challenge to separate overlapped signals especially for a single antenna. The existing methods have a low decoding accuracy for small power difference, carrier frequency difference and relative time delay between overlapped signals. In order to solve these problems, we apply the deep learning method to single antenna ADS-B signal separation. A multi-scale Conv-TasNet (MConv-TasNet) is proposed to capture long temporal information of the ADS-B signal. In MConv-TasNet, a multi-scale convolutional separation (MCS) network is proposed to fuse different scale temporal features extracted from overlapping ADS-B signals and generate an effective separation mask to separate signals. Moreover, a large dataset is created by using the real ADS-B data. In addition, the proposed method has been evaluated on the dataset. The average decoding accuracy on the test set is 90.34%. It has achieved the state-of-the-art results.
A Solar Radiation Forecast Platform Spanning over the Edge-Cloud Continuum

The prediction of PV output represents an important task for PV farm operators as it enables them to forecast the energy they will produce and sell on the energy market. Existing approaches rely on a combination of satellite/all-sky images and numerical methods which for high spatial resolutions require considerable processing time and resources. In this paper, we propose a hybrid egde–cloud platform that leverages the performance of edge devices to perform time-critical computations locally, while delegating the rest to the remote cloud infrastructure. The proposed platform relies on novel metaheuristics algorithms for cloud dynamics detection and proposes to forecast irradiance by analyzing pixel values taken with various filters/bands. The results demonstrate the scalability improvement when using GPU-enabled devices and the potential of using pixel information instead of cloud types to infer irradiance.
Transforming STEM into STEAM

International scientific conference «The role of psychology and pedagogy in the spiritual development of modern society» : conference proceedings, July 30–31, 2022. Riga, Latvia : «Baltija Publishing», 2022. 363 pages.
Shape Dynamic Time Warping for Seismic Waveform Inversion

ABSTRACT We illustrate shape dynamic time warping (ShapeDTW) for 1D structure waveform inversion with examples from Tanzania, East Africa, and Northeastern Oklahoma. Seismograms of the Mw 5.9 Lake Rukwa, Tanzania, earthquake (18 August 1994) recorded by regional broadband seismographic stations of the Tanzania Broadband Seismic Experiment are used to demonstrate the method. We also test the inversion through solving for 1D crustal velocity structure of the Cherokee Platform in Oklahoma using seismograms of the 2011 Mw 5.7 Prague, Oklahoma, earthquake recorded by the EarthScope Transportable Array. Dynamic time warping (DTW) bypasses the time expense required for manual identification of seismic phases and shows potential for linking discrete seismic phases between two similar waveforms. ShapeDTW improves inversion performance by characterizing the character of the time series near each amplitude point. Applying Sakoe-Chiba band limits to ShapeDTW and an additional amplitude constraint improves the overfitting problem that occurs with the classic DTW method. We apply ShapeDTW to estimate optimal time shifts between observed and synthetic waveforms with time shifts reduced through inversion. The crustal velocity model of Tanzania found using ShapeDTW is close to that found using “phase time inversion” of numerous regional seismic phases observed in the three-component waveforms in a previous study but avoids the necessity of isolating and interpreting different seismic phases in the waveforms. Reducing the ShapeDTW functional misfits can overcome the cycle-skipping problem that often occurs with direct comparison of amplitudes in waveform inversion. This method may be useful in generating starting models for other 1D and 3D inversion methods.
Blockchain Implementation Framework for Tracing the Dairy Supply Chain

As the dairy industry grows increasingly more diverse and complex, it needs a much more reliable and efficient supply chain management infrastructure. Blockchain addresses these challenges by providing a traceable and trustworthy network, which helps in boosting consumer confidence while also assuring the smooth flow of the supply chain. This paper focuses on how blockchain can be implemented in the dairy industry and how it can benefit the supply chain.
Tilted Wave Fizeau Interferometer for flexible and robust asphere and freeform testing

Tilted Wave Interferometry (TWI) is a measurement technique for fast and flexible interferometric testing of aspheres and freeform surfaces. The first version of the tilted wave principle was implemented in a Twyman-Green type setup with separate reference arm, which is intrinsically susceptible to environmentally induced phase disturbances. In this contribution we present the TWI in a new robust common-path (Fizeau) configuration. The implementation of the Tilted Wave Fizeau Interferometer requires a new approach in illumination, calibration and evaluation. Measurements of two aspheres and a freeform surface show the flexibility and also the increased stability in both phase raw data and surface measurements, which leads to a reduced repeatability up to a factor of three. The novel configuration significantly relaxes the tolerances of the imaging optics used in the interferometer. We demonstrate this using simulations on calibration measurements, where we see an improvement of one order of magnitude compared to the classical Twyman-Green TWI approach and the capability to compensate higher order error contributions on the used optics.
General parameter control framework for evolutionary computation

This study proposes a general multiple parameter control framework by leveraging the ability of a reinforcement learning system to learn empirical knowledge for evolutionary computation. We design a feedback evaluation mechanism to define the rewards offered to agents, using which they can learn to choose appropriate parameters in formulated action sets. Moreover, a learning strategy is proposed to utilize the parameter selection-related knowledge that is gained during training episodes. Three famous evolutionary computation (EC) methods (i.e., particle swarm optimization, artificial bee colony, and differential evolution) are selected as the baseline algorithms and applied to the proposed framework. The aforementioned redesigned algorithms are tested on 15 common benchmark functions, as well as the CEC2017 benchmarks. In addition, the robustness of the algorithms is demonstrated through parameter sensitivity analysis. The results of the comparative analysis reveal that the three improved algorithms exhibit a faster overall convergence and higher accuracy than their state-of-the-art variants. It is also confirmed that our proposed framework has the capability to improve the performance of EC approach.