Reviewerly/exHarmony huggingface dataset:
A Robust Pixel-Aware Gyro-Aided KLT Feature Tracker for Large Camera Motions

Tracking fails in the optimization step of conventional Kanade-Lucas–Tomasi (KLT) feature tracker mainly due to the inadequate initial condition that falls out of the convergence region, especially when a camera rotates rapidly or shakes severely. To overcome the problem, we propose a pixel-aware gyro-aided KLT feature tracker that remains accurate and robust under fast camera-ego motion conditions. In particular, we develop a pixel-aware gyro-aided feature prediction algorithm to predict the initial optical flow and obtain the patch deformation matrix of each feature point. It increases the probability of initial estimates to locate in its convergence region. Unlike the existing methods, which assume all the tracked feature pairs were constrained by the same homography prediction matrix, our prediction matrix is adjustable for each feature as it considers the pixel coordinates in the prediction process. A geometric validation based on homography and fundamental check is also adopted to remove outlier tracks. Experimental results on both public datasets and real-world sequences demonstrate that the feature tracking accuracy and robustness can be significantly improved by the proposed method. To facilitate further development, the code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/weibohuang0314/pixel_aware_gyro_aided_klt_feature_tracker</uri> .
Path Planning and Following Control of a Quadrotor Helicopter in Three-Dimensional Space With Limited Information

This paper presents a real-time path planning and following control strategy based on rapidly exploring random tree (RRT) and Line of Sight (LOS) for achieving autonomous flight of a quadrotor helicopter in three-dimensional complex space with limited available obstacle information. Regarding the path planning problem, a new three-dimensional path planning algorithm is proposed to generate efficient and executable paths based on RRT and cubic Bézier curve. With respect to path following control, LOS algorithm is employed and extended to three-dimensional space to enable the quadrotor helicopter to follow the preplanned path. Finally, a series of simulation tests are carried out to validate the effectiveness of the proposed strategy. The demonstrated results show that with the proposed path planning and tracking control strategy, the studied quadrotor helicopter can successfully accomplish the deployed mission in both static and dynamic obstacle environments.
FPGA Utilized to Control the Coincidence Logic of Quantum Entangled Pairs (QEP)

Control of quantum signals is very robust design technique and very important in quantum signal processing (QSP). It is not easy in experimentation platforms. Field Programmable Gate Array (FPGA) is using to control very various range of devices in quantum fields through PCs. FPGA needs to run modules of components to communicate and interfacing with the PC, through decode perform commands to direct control of digital hardware’s. If programmer has a real-time control of the FPGA via USB, it can be possible to evaluate design parameters changes in real-time, without reprogramming the FPGA. That makes the proposed design platforms easier for researchers. This paper discusses experiment of control quantum signals by FPGA to control coincidence logic for Quantum Entangled Pair (QEP), that able to measure polarization correlations relationships between photons of the QEP. This FPGA helps to determine detection events at different detectors which can be attributed to a single photon pair. Also to determine the correlation time between two different beams frequencies is extremely accurate according to the delay between the beams, which is very short.
Face mask detection based on MobileNet with transfer learning

After a few cases of COVID-19 had emerged in Wuhan, China, the disease had rapidly spread all over the world. As the World Health Organization confirmed that COVID-19 is an infective and dangerous respiratory virus, it is important to wear masks to reduce the rate of infections. Thus, a face mask detector is essential to encourage people to wear masks and prevent the virus from spreading. In this paper, we proposed a mask detection model based on transfer learning of MobileNet. This face mask detector can be applied to images, videos, or live video streaming to detect whether the people have worn a mask or not. The model has achieved an accuracy of 99.88% on the training set and 99.64% on the testing set. In addition, we used the K-Fold Cross Validation to evaluate our model, and the average accuracy was 97.4%.
BasiCal mobile application development using multimedia element’s adoption on basic calculus teaching and learning

Teaching and studying are a challenging task. Calculus can be seen through the lens of a variety of topics, including a teacher's lack of imagination, the adoption of conventional methods, and the complexity of illustration. The importance of adopting a creative and strategic approach to assist with Calculus teaching and learning (T&L) in university should be seriously considered. This article tries to explain how a mobile application for Calculus learning called BasiCal was created using multimedia elements. In this report, descriptive analysis was used to ensure that the whole process ran smoothly. The findings revealed that creating a mobile application for illustrative Calculus T&L would add informative value to the entire process of raising the joyful atmosphere to promote the level of understanding on absorbing Calculus learning. With the development of interactive multimedia media, this study is expected to contribute to the interest stage in learning Calculus at university. The creation of immersive digital learning media for mobile applications would be beneficial in fostering student learning interest and making Calculus learning materials easier to comprehend.
Partial Re-colorization Method for the Auto-colorized Image using a Prior Information

In this paper, we propose a method to perform partial re-colorization of an image that has been miscolored by fully automatic color restoration. This makes it possible to make partial colorization without changing the areas that were correctly colorized by the fully automated method.
Using Machine Learning to Recognise Novice and Expert Programmers

Understanding and recognising the difference between novice and expert programmers could be beneficial in a wide range of scenarios, such as to screen programming job applicants. In this paper, we explore the identification of code author attributes to enable novice/expert differentiation via machine learning models. Our iteratively developed model is based on data from HackerRank, a competitive programming website. Multiple experiments were carried using 10-fold cross-validation. Our final model performed well by differentiating novice coders from expert coders with 71.3% accuracy.
Subspace Segmentation and Clustering of Medical Image Data Based on Low-Rank Representation

Biomedical cell image contains different types of components. The segmentation of these components is essential for characterizing the overall structure of leucocytes. However, the task is challenging because the data generally possess irregular patterns, varying eccentricity and congestion of inter class substructures. In order to perform the automatic segmentation of leucocytes, many approaches in machine learning have been proposed. Most of the recent methods utilize k-means and fuzzy c-means algorithms. These methods suffer from the under-segmentation and the oversegmentation of data points. We have adopted an efficient Low Rank Representation (LRR) method for the segmentation of cell image patterns. Its overall segmentation performance is very high in comparison to the k-means and fuzzy c-means segmentation methods. It discriminates sub structures by incorporating multiple feature data into the joint representation matrix. The main improvements of our adopted approach, include the global geometric representation of different cell patterns and the control of different sources of noise in the data. Data points of the cell image are evolved with the basic assumption as x = m + n. Where x is the input observation, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> is the set of data points that lies in the correct subspace and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> represent the noise in the data. The performance of the model was evaluated on the different types of medical images. It has reported the clustering accuracy of 97.98% on the biomedical database. Whereas an accuracy of 93.1 % was reported on ALL-IDB cell dataset.
Exploiting 3D Spatial Relationships for Target-driven Visual Navigation

We propose a visual navigation system that uses a currently popular deep reinforcement learning algorithm to make reasonable decisions in complex virtual 3D environments. The goal of our model is to train an intelligent agent to move towards a target location given an egocentric view of the target and its current observation. Our end-to-end trained agent uses RGB and 3D spatial relationships to make it possible to handle large environments across multiple scenes and different targets. When compared to several baselines, we demonstrate that our method: (1) has higher accuracy to arrive at target locations, (2) generates better solutions with shorter paths for navigation tasks, and (3) generalizes to unseen scenes and targets.
The mediating effect of self-leadership on the media literacy and learning agility of nursing students based on the experiences of online classes during the COVID-19 pandemic

Purpose: The purpose of this study was to investigate the mediating effect of self-leadership on the relationship between media literacy and learning agility in nursing students based on their experiences in online classes during the Coronavirus Disease-19 pandemic.Methods: A descriptive survey was conducted among 165 nursing students from four universities in Busan. Data were collected from June 2 to 13, 2021, and was analyzed using a t-test, one-way ANOVA, Pearson’s correlation coefficients, and stepwise multiple regression with SPSS/WIN 26.0.Results: Significant relationships were found between learning agility and media literacy (r=.62,p&lt;.001), between learning agility and self-leadership (r=.58, p&lt;.001), and between media literacy and self-leadership (r=.53, p&lt;.001). Additionally, self-leadership had a partial mediating effect on the relationship between media literacy and learning agility (Z=4.30, p&lt;.001); its explanatory power was 46.0%.Conclusion: These results indicate that interventions to increase the level of media literacy, along with self-leadership, are necessary to improve the level of learning agility of nursing students who will be essential human resources in a rapidly changing healthcare field.
Application Of Web-Based Industrial Environment Test Results

Tests on an industrial environment is needed to ensure the health and as a way to combat pollution in the surrounding environment. To obtain data and information about the content of chemical substances that exist in the region need of good accuracy in order to provide accurate research results. If the process using the manual method, it will take a long time to make a report of the information obtained. As for the various samples taken have different standards or methods based on government regulations. The application for processing the results of industrial environment test uses the PHP programming language and uses a MySQL database. Application uses the Black Box method from display to input action. Moreover,testing this application using a Likert scale, the author gives a questionnaire to the marketing of 5 people and laboratory 5 people with questions about the appearance results of input and output application with 5 items. The results of this test, the application of industrial environmental results can be accepted by laboratory application with a percentage of 74.4%. This laboratory application helps data management and administration PT. Xyz, becomes temporary information or the final report on the evaluation of research conducted.
Toward Automatic Mammography Auditing via Universal Language Model Fine Tuning

Successful mammography screening reduces the average breast cancer mortality by 20% to 22%. However large-scale screening of mammography is subject to variability due to the inherent subjective nature of evaluating mammograms. The Mammography Quality Standards Act (MQSA) require radiologists to reports their outcome in an attempt to reduce the variability of mammography practice. Therefore to improve quality of mammography screening and to comply with MQSA requirements, hospitals deploy a significant amount of resources to track radiologists performance over time in terms of predicting cancer. One of the core bottle necks is the manual review of pathology reports by experts. In this paper we successfully pilot an AI tool that automates this review. We take a two step approach that leverages the Universal Language Model Finetuning (ULMFiT) deep learning framework. First, we train an ULMFiT model that classify biopsy reports into Benign vs. Malignant with 97% precision and a recall of 96%. We fine-tune a second ULMFiT based model that further classifies Benign reports into Benign vs. Benign (High Risk Lesion) with a precision of 77% and recall of 92%. In the process of deploying, the model corrected 24 previously mislabeled reports. Finally we present a pilot deployment study that demonstrates 94 % or higher agreement between human expert and corresponding AI model.
Constructing a computer model for discipline data governance using the contingency theory and data mining

Data governance is an important part of modernizing the governance capacity of universities. Discipline data governance plays an important role in promoting the development of university disciplines, and is a key factor in improving the governance of university disciplines, the science of educational decision-making and the effectiveness of management. It is an important way to promote the "precision" and "science" of discipline governance. In this paper, we construct a model of discipline data governance based on the Contingency Theory with a view to shedding light on discipline governance.
Research on risk analysis of pedestrian and vehicle flow in community scenes based on machine vision

Surveillance video is vital to community emergency management and risk control. Existing surveillance video analysis has limitations of low utilization, low sharing rate and low data correlation analysis rate. Based on these shortcomings, this paper reports a framework of community risk identification and warning using machine vision to analyze surveillance video. The prototype is based on the YOLOV4 video analysis network, which extracts and records spatio-temporal coordinate data of key categories of people, vehicles and objects related to community risks. The data are computed to obtain key state information about the direction of flow, speed and range of activity. By analyzing the logical, inclusive and spatio-temporal relationships among objects, the process makes risk judgment in real-time and provides early warning of visualization results. In addition, by accumulating historical data to discover potential dangers, the level of community services are optimized and the construction of smart communities are accelerated. This paper introduces a community risk management and prevention framework based on machine vision, which focuses on networking, sharing and exchanging of video resources as well as multi-dimensional association of video big data. Through real-time surveillance and early warning of risks, analysis and research, the study aims at the visualization, monitorability and early warning of smart community operation.
Reconstruction of High-Resolution Computed Tomography Image in Sinogram Space

An important part of any computed tomography (CT) system is the reconstruction method, which transforms the measured data into images. Reconstruction methods for CT can be either analytical or iterative. The analytical methods can be exact, by exact projector inversion, or non-exact based on Back projection (BP). The BP methods are attractive because of thier simplicity and low computational cost. But they produce suboptimal images with respect to artifacts, resolution, and noise. This paper deals with improve of the image quality of BP by using super-resolution technique. Super-resolution can be beneficial in improving the image quality of many medical imaging systems without the need for significant hardware alternation. In this paper, we propose to reconstruct a high-resolution image from the measured signals in Sinogram space instead of reconstructing low-resolution images and then post-process these images to get higher resolution image.
Processing Tissue Micro-Array Images Using Machine Learning Techniques as Preparation for Determining Gleason Grade of Prostate Cancer

Prostate Cancer (PCa) is the sixth most common and the second deadliest cancer among men in the world. While the diagnosis of PCa mostly depends on the decisions of pathologists based on clinical symptoms of the patients and their careful observations upon the patients' prostate biopsy, in particular, the biopsy of the patients' glands. Often there are disagreements and debates among pathologists in deciding the Gleason grade of the same biopsy samples. Majority votes take place to avoid disputes. In this research, we aim to find an automatic method to facilitate the process of determining Gleason grade of PCa. In order to achieve that, we explore both Support Vector Machine (SVM) and Convolutional Neural Networks (CNNs) in order to determine whether the local patches of the Tissue Micro-Array (TMA) images contain glands, border region of glands, or stroma, and it turns out that with limited amount of labelled data, SVM outperforms the other. With the results from classifiers, we can filter irrelative portions of images. This processed TMA images would allow pathologists to focus on the locations of glands existing potentially. Moreover, with processed images, the next level of training – predicting the Gleason grade of PCa and generating corresponding images under certain Gleason grade – using models like CNNs and Generative Adversarial Networks (GANs) is expected to be much faster.
Explicit representation of operator control-actions in Multilevel Flow Modeling

Multilevel flow models have been used for automated reasoning in control-rooms in complex industrial plants, mainly focusing on the representation of system design goals and online root-cause analysis. Further research into automated generation of counteraction plans in emergency scenarios has shown that a formal extension of the modeling language is required to extend it's utility for planning applications. This contribution presents two novel modeling concepts for Multilevel Flow Models that are designed to enable the explicit representation of an operators means to interact with a system. The presented additions to the modeling language enable both the identification of viable actions as well as conditions for their execution. The applications of the new modeling concepts is demonstrated using a case-study featuring the primary system of a pressurized water reactor.
Assessment of Global Solar Energy Under All-Sky Condition Using Artificial Neural Network

The most integral aspect of optimizing solar energy applications is the assessment of solar radiation. Devises of solar energy will work better if the radiation that is to be obtained is measured long before. Solar radiation measurement models are created because of the unavailability of solar radiation measuring instruments at the station of interest. In this paper, four (4) artificial neural network models are developed for assessing daily solar radiation. Solar radiation data for training and testing the ANN models are collected from Reggane city, in the south of Algeria. Input data based on meteorological and astronomical parameters such as: relative humidity, declination, average air temperature, hour angle and extraterrestrial solar irradiation. Those ANN-based models study uses the Levenberg–Marquardt algorithm and collected during six years. The efficiency of models was assessed using statistical tests, including mean bias error, root mean squared error, mean absolute error and correlation coefficient. The statistical analysis of the results showed that, relative to other models, Model 4 provided the best combination of global solar radiation for estimating, with coefficient of correlation (R) higher than 0.91, the mean absolute error does not exceed 7% and relative RMSE does not exceed 10%. This model can be used when data is available for heating, cooling and designing solar energy systems in Sahara climatic regions.
MLPP: Exploring Transfer Learning and Model Distillation for Predicting Application Performance

Performance prediction for applications is quintessential towards detecting malicious hardware and software vulnerabilities. Typically application performance is predicted using the profiling data generated from hardware tools such as linux perf. By leveraging the data, prediction models, both machine learning (ML) based and non ML-based have been proposed. However a majority of these models suffer from either loss in prediction accuracy, very large model sizes, and/or lack of general applicability to different hardware types such as wearables, handhelds, desktops etc. To address the aforementioned inefficiencies, in this paper we proposed MLPP, a machine learning based performance prediction model which can accurately predict application performance, and at the same time be easily transferable to a wide both mobile and desktop hardware platforms by leveraging transfer learning technique. Furthermore, MLPP incorporates model distillation techniques to significantly reduce the model size. Through our extensive experimentation and evaluation we show that MLPP can achieve up to 92.5% prediction accuracy while reducing the model size by up to 3.5 &#x00D7;.
Vision-based Scene Recognition for Product Search

This paper proposes a method that combines a relevance model with general object recognition. This method can detect general objects and recognize scenes. Based on scene recognition, object detection accuracy can be improved, and the product search time at a store can be reduced. To recognize a scene from the detected objects, the proposed algorithm uses a relevance model constructed by a Bayesian network. Experiments were conducted to verify the effectiveness of the proposed method. In the experiments, the stock status of the products and the categories of the corners were estimated. The results confirmed that the stock status of the products and corner categories were correctly estimated.