Reviewerly/exHarmony huggingface dataset:
Automatic Region of Interest Extraction from Finger Nail Images for Measuring Blood Hemoglobin Level

Point-of-care medical diagnosis has gained popularity for rapid diagnosis in medical field. Historically blood hemoglobin test was confined to medical laboratory and diagnostic centres. Currently, cell phone-based telehealth has empowered remote diagnosis. Researches on hemoglobin measurement by colorimetric analysis of user image taken from a smartphone are adding a new dimension to existing medical diagnosis. Images of body features like fingertip and fingernail are commonly used. Colorimetric analysis requires manually selecting perfect region of interest which has uniform color. The main objective of this paper is to extract region of interest automatically from fingernail images to detect blood hemoglobin level. A contour detection based process has been introduced to separate user hand image from the background, from which fingernails are detected. Our system can successfully subtract background if user hand is the closest object to the camera. By analyzing the color of fingernail data from smartphone images, our system calculates hemoglobin levels. Our application enables anyone with a smartphone to immediately detect blood hemoglobin level anywhere, anytime. A simple linear regression model was used to measure blood hemoglobin level. Our application is able to measure blood hemoglobin level with MAE value of 0.64, MSE value of 0.62, RMSE value of 0.78 and R2 of 0.69.
Student Action Recognition and Early Warning Machine Based on Online Class

In order to ensure the students' listening status, this paper proposes and implements a student action recognition and early warning mechanism based on online class. Firstly, use Kinect to collect the time series data of the human body action feature description, and construct the human body posture description vector; Then, use the principal component analysis (PCA) method to extract different action features, reduce the dimension of the feature vector space, eliminate the difference between the original features, and reduce the redundancy of data information in order to judge and recognize actions; Finally, for the identified actions that are not conducive to listening to the class, the warning tone is used to remind and wake up the students who are not paying attention in class.
Fuzzy Model of Situational Control of the Flight Parameters of an Autonomous Unmanned Aircraft under Uncertainty Conditions

Th e article outlines the main problems of automatic planning of the behavior of an autonomous unmanned aerial vehicle in unstable air conditions. It is shown that the urgency of the problem is due to the fact that an autonomous unmanned aerial vehicle independently forms and implements its flight route without support from a ground control station. There is therefore a need to develop a method for automatic control of programmed movements associated with the implementation of the route constructed by the problem solver. To solve this problem we propose an approach to regulating the parameters of the state of dynamic objects based on the principle of situational control of the goal-directed behavior of complex systems in changing environmental conditions. The expediency of choosing this control principle is due to the fact that the state of an autonomous unmanned aerial vehicle during its flight is characterized by a large number of parameters and disturbing environmental factors. In order to effectively implement this control principle, we introduce the concept of a complete problematic situation, which consists of deviations of the state parameters of an autonomous unmanned aerial vehicle from the required values during flight and disturbing environmental factors. On this basis, a fuzzy model of situational control of the state parameters of an autonomous unmanned aerial vehicle functioning in an unstable environment is developed, in which linguistic variables and functions are used to provide a generalized presentation of reference problem situations, as well as to describe the deviations of the state parameters and disturbing environmental factors. The conditions are determined under which the reference indistinctly presented problem situations generalize the actual problem situations that arise at the control object. This makes it possible to significantly reduce the number of logical-transformational decision rules in the situational control model and to promptly automatically determine effective control actions in problematic situations that e nsure the effective implementation of programmed movements of an autonomous unmanned aerial vehicle under conditions of uncertainty. In conclusion, it is shown that for the implementation of control actions which are selected on a situational b asis with increased requirements for the accuracy of regulation of the time-varying parameters of the control object and a significant level of possible discrepancies between their actual and specified values in conditions of uncertainty, it is advisable to use indistinctly implemented proportional, integral and differential regulation laws.
Dual Attention Fusion Network for Single Image Dehazing

Recently, there have been significant advances in image dehazing methods based on the convolutional neural network. However, the convolutional kernel is a fixed receptive field. It considers that image features are equally weighted on channels and pixels, which leads to the loss of some vital feature information. In this paper, we propose an end-to-end Dual Attention Fusion Network(DAF-Net). The network consists of three residual groups, and each group comprises three residual dual attention fusion modules. The module consists of a residual block and channel and pixel attention fusion to strengthen the global dependence and obtain pixel characteristics. Based on the different features of the fusion modules, adaptive learning is performed using channel and pixel attention to give more weight to essential features. This model structure also keeps the shallower information and transfers it to the deeper layers. The experimental results show that DAF-Net retains details and improves the visual effects by using a stack of fewer residual dual attention fusion modules on synthetic and real-world images.
Analyzing Uncertainty in Release Planning: A Method and Experiment for Fixed-Date Release Cycles

Release planning—deciding what features to implement in upcoming releases of a software system—is a critical activity in iterative software development. Many release planning methods exist, but most ignore the inevitable uncertainty in estimating software development effort and business value. The article’s objective is to study whether analyzing uncertainty during release planning generates better release plans than if uncertainty is ignored. To study this question, we have developed a novel release planning method under uncertainty, called BEARS, that models uncertainty using Bayesian probability distributions and recommends release plans that maximize expected net present value and expected punctuality. We then compare release plans recommended by BEARS to those recommended by methods that ignore uncertainty on 32 release planning problems. The experiment shows that BEARS recommends release plans with higher expected net present value and expected punctuality than methods that ignore uncertainty, thereby indicating the harmful effects of ignoring uncertainty during release planning. These results highlight the importance of eliciting and analyzing uncertainty in software effort and value estimations and call for increased research in these areas.
A Multiperson Pose Estimation Method Using Depthwise Separable Convolutions and Feature Pyramid Network

In the process of multiperson pose estimation, there are problems such as slow detection speed, low detection accuracy of key point targets, and inaccurate positioning of the boundaries of people with serious occlusion. A multiperson pose estimation method using depthwise separable convolutions and feature pyramid network is proposed. Firstly, the YOLOv3 target detection algorithm model based on the depthwise separable convolution is used to improve the running speed of the human body detector. Then, based on the improved feature pyramid network, a multiscale supervision module and a multiscale regression module are added to assist training and to solve the difficult key point detection problem of the human body. Finally, the improved soft-argmax method is used to further eliminate redundant attitudes and improve the accuracy of attitude boundary positioning. Experimental results show that the proposed model has a score of 73.4% in AP on the 2017 COCO test-dev dataset, and it scored 86.24% on PCKh@0.5 on the MPII dataset.
Attention-Based Deep Learning Model for Aspect Classification on Vietnamese E-commerce Data

This article introduces methods for applying Deep Learning in identifying aspects from written commentaries on Shopee e-commerce sites. The used datasets are two sets of Vietnamese consumers' comments about purchased products in two domains. Words and sentences will be performed as vectors, or characteristic matrices through language models such as one-hot, fastText, PhoBERT. We then used Convolutional Neural Network (CNN) and the Fully Connected Neural Network (Multilayer perceptron - MLP) to learn the aspects which are mentioned in the comments. Experimental results showed that our team's methods achieved much better results than traditional learning algorithm using other word-level vectors such as SVM, Naïve Bayes, etc.
Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems

Abstract As more users across the world are interacting with dialog agents in their daily life, there is a need for better speech understanding that calls for renewed attention to the dynamics between research in automatic speech recognition (ASR) and natural language understanding (NLU). We briefly review these research areas and lay out the current relationship between them. In light of the observations we make in this article, we argue that (1) NLU should be cognizant of the presence of ASR models being used upstream in a dialog system’s pipeline, (2) ASR should be able to learn from errors found in NLU, (3) there is a need for end-to-end data sets that provide semantic annotations on spoken input, (4) there should be stronger collaboration between ASR and NLU research communities.
Fast and in-situ correction of camera channel crosstalk based on Fourier transform of carrier fringes in multi-colour interferometry

To deal with the light intensity crosstalk between R, G and B channels of colour camera in multi-colour interferometry, we present a fast and in-situ correction method based on Fourier transform of carrier fringes. The crosstalk coefficients of camera channels are obtained in the Fourier frequency domain of captured colour carrier fringes, and the crosstalk is corrected by matrix inverse operation. Simulations and experiments verify that this method has advantages of simple implementation, fast correction speed and high measurement accuracy, and it effectively achieves in-situ correction without removing the camera.
Adam Optimized Deep Learning Model for Segmenting ROI Region in Medical Imaging

Medical image processing played a crucial role in healthcare sectors because it produces visible images of inner body structure and internal tissues. With medical imaging, the organs' functions are analyzed, and anomalies are detected effectively. The imaging process utilizes enhancement, analysis, and recognition procedures that maximize disease detection accuracy. However, the gathered medical images have noise while performing the medical research that affects the ROI region segmentation process. The improper identification of the ROI region causes to reduce disease detection accuracy and create complexity. Therefore, this paper uses the Adam optimization technique with deep learning approaches for examining the medical images. The deep learning (DL) approaches utilizing the multiple layers, expert-tuned parameters, and learning function to deriving the affected ROT region. The DL method automatically extracts the medical imaging features that are optimized according to the RMS prop and momentum-based learning parameters. The optimized parameter minimizes the complexity while deriving the disease affected region. This process ensures high region segmentation accuracy with minimum time. The process has detected the affected region with 99.27% of accuracy.
Multicamera Calibration Optimization Method Based on Improved Seagull Algorithm

There are some problems in the process of camera calibration, such as insufficient accuracy and poor accuracy. Based on the seagull algorithm, the adaptive differential evolution algorithm is combined with the seagull algorithm to optimize the multicamera calibration. The seagull algorithm can achieve good results on multiparameter problems and effectively avoid falling into local optima. In this paper, the adaptive differential search algorithm is adopted to improve the local search ability and optimize the local search and global search ability. According to Zhang Zhengyou's method, the calibrated parameter is obtained, in which the parameter is used as the initial value. Then, taking the minimum mean error as the criterion, the improved seagull algorithm (SOA-SaDE) is used to establish the objective function, and the internal parameters and distortion coefficient of the camera are further solved. Verification experiments showed that the fusion algorithm has less reprojection error and higher calibration accuracy gull algorithm.
Domain-Robust Pedestrian-View Intersection Classification

Pedestrian-view intersection classification is one of the key components in building navigation systems for small autonomous vehicles maneuvering on pedestrian paths. However, until recently, such systems operate only in one domain, such as only in outdoor environments. In this work, we propose to train a model that is robust across multiple domains, i.e., indoor and outdoor. In order to achieve such robustness, the network is trained to extract only intersection classification related features while not extracting the domain information. Such objective is achieved by using two-branched network: intersection classifier and domain predictor. The network is then encouraged to fail predicting the domain while successfully predict the intersection categories. Extensive experiments demonstrate the superiority of our proposed model against the baseline trained using the dataset containing samples from both domains.
Segmentation of Cervical Cell Cluster by Multiscale Graph Cut Algorithm

The segmentation and recognition of cervical cell clusters is a major challenge for the automatic screening of cervical cancer cells. This paper presents a model based on a multiscale graph cut algorithm for automatically segmenting cervical cell clusters. Global seed nodes are obtained using a multiscale graph cut algorithm to coarsely segment the cervical cell sample image and combine the confidence region method. Then, according to the global seed nodes and the global graph cut algorithm, the segmentation of the sample image is performed. The experimental data shows that the proposed algorithm is better than the currently widely used threshold watershed algorithm in DSC, accuracy, and recall measures.
Positioning Control Algorithm of Vehicle Navigation System Based on Wireless Tracking Technology

To improve the accuracy and reliability of the on-board navigation system positioning, the positioning control algorithm of vehicle navigation system based on wireless tracking technology is proposed. By using modern information fusion technology, the accurate positioning of vehicle integrated navigation is realized, and the design goal of omnidirectional, all weather, and self-contained positioning function is realized. Finally, the test shows that the accuracy and reliability of the positioning control algorithm of vehicle navigation system based on wireless tracking technology are improved than existing point system, speed measurement accuracy can reach 0.02 m/s, and positioning accuracy is about 18 meters. The vehicle operation efficiency and safety are greatly improved, and the traffic capacity is improved. And the traffic congestion is effectively alleviated, which provides reliable guarantee for the realization of traffic management automation and intelligent vehicle driving.
Smart Contract as a Way to Exchange Digital Values in Blockchain

The main components of blockchain technology, specific features of this technology, the basics of this network, and technological features are identified. A SWOT analysis of the advantages, disadvantages, opportunities, and threats of this technology is conducted. The specifics of Smart Contract implementation as a way of exchanging digital values in the Blockchain are studied. The directions of Blockchain technology development in accounting, insurance, supply chain management, smart trade, storage, and data exchange are investigated. The specifics of the application of blockchain technology in business are studied according to their experience, contents, and limits (complications of accounting, the risk of neglecting the interdependence of key blockchain concepts, fraud risk for cloud services).
A Face Recognition Algorithm Based on Intermediate Layers Connected by the CNN

Face recognition is difficult due to the higher dimension of face image features and fewer training samples. Firstly, in order to improve the performance of feature extraction, we inventively construct a double hierarchical network structure convolution neural network (CNN) model. The front-end network adopts a relatively simple network model to achieve rough feature extraction from input images and obtain multiple suspect face candidate windows. The back-end network uses a relatively complex network model to filter the best detection window and return the face size and position by nonmaximum suppression. Then, in order to fully extract the face features in the optimal window, a face recognition algorithm based on intermediate layers connected by the deep CNN is proposed in this paper. Based on AlexNet, the front, intermediate and end convolution layers are combined by deep connection. Then, the feature vector describing the face image is obtained by the operation of the pooling layer and the full connection layer. Finally, the auxiliary classifier training method is used to train the model to ensure the effectiveness of the features of the intermediate layer. Experimental results based on open face database show that the recognition accuracy of the proposed algorithm is higher than that of other face recognition algorithms compared in this paper.
Interactive Visualizations to Support Randomized Clinical Trial Monitoring

Despite current technological advances, interactive tools to facilitate the analysis of data collected during clinical trials are still not widely available. Such a scenario makes researchers rely on time-consuming extractions from databases and subsequent application of analytical methods by statisticians to obtain results from which they can get insights. Moreover, during clinical trials, researchers need to keep track of subjects’ progress by monitoring their participation and the quality of the data collected at specific phases of the trial. We have developed a visualization-based interface that assists the epidemiologists of a randomized clinical trial focused on the effects of lifestyle intervention in developing type 2 diabetes for patients with Gestational Diabetes Mellitus (GDM). Coaches are responsible for the intervention, and research assistants collect data from hundreds of questionnaires and clinical exams. We adopted user-centered design principles, which allowed continuous improvements to the visualizations and interactive features during a year-long development process. Besides typical selection and filtering features, the visualizations we have designed allow the research team to monitor each participant’s progress and perform analyses that facilitate findings in and between subjects’ histories. Two formal evaluations were also performed with experts and non-experts, where the visualization-based interface proved to be intuitive and useful for assisting coaching activities, monitoring the progress of data collection, and performing analyses.
Autonomous Vehicle Path Planning using Q-Learning

Abstract Path planning is vital in autonomous vehicle technology, from robots to self-driving cars and driverless trucks, it is impossible to navigate without a proper path planning algorithm, various algorithms exist Q-learning being one of them. Q-learning is used extensively in discrete applications as it is effective in finding solutions to these problems. This research investigates the possibility of using Q-learning for solving the local path planning problem with obstacle avoidance. Q-learning is split into two phases, the first being the training phase, and the second being the application phase. During training, Q-learning requires exponentially increasing training time based on the system’s state space. However, when Q-learning is applied it becomes as simple as a lookup table which allows it to run on even the simplest microcontrollers. Two simulations are conducted with varying environments. One to showcase the ability to learn the optimal path, the other to showcase the ability for learning navigation in variable environments. The first simulation was run on a static environment with one obstacle, with enough training episodes, Q-learning could solve the path planning problem with minimal movement steps. The second simulation focuses on a randomized environment, obstacles and the agent’s starting position are randomly chosen at the start of every episode. During testing, Q-learning was able to find a path to the target when a path did exist, as It was possible in certain configurations for the vehicle to be stuck in between obstacles with no feasible path or solution.
Comparative Study of Deep Learning Models for Devanagari OCR

Emergence of Deep Learning Approaches have led to major breakthroughs and improvement in image classification tasks. Handwritten character recognition is one such area where the deep learning methods are employed for enhanced performance. This paper reviews the state-of-the-art deep learning methods viz. CNN, ANN, Capsule Network, Residual Network, and Transfer Learning Networks that are pre-trained on the ImageNet dataset— AlexNet, DenseNet, VGG, Inception used for recognition of the handwritten characters of Devanagari script. The paper presents an exhaustive comparison of all mentioned techniques both in terms of qualitative and quantitative analysis. The paper also highlights the challenges, and future directions for Devanagari characters recognition.
Blind Image Quality Assessment by Fast Quality Assessment Network

In this paper, a new neural network structure, which is called fast quality assessment network (FQA-Net), is proposed for fast blind image quality assessment (BIQA). FQA-Net is a very simple neural network, which mainly includes convolution layer, standard deviation measurement layer and regression layer. In order to improve the efficiency of the network, a group of visual filters (VFs) are obtained by simulating the neurons in the cerebral cortex. The VFs are used as the convolution kernels in the convolution layer, then the outputs of the convolutional layer are a set of feature maps. After that the standard deviation of each feature map is calculated directly. Finally, the regression function is used for the mapping between the standard deviation values and the quality scores. FQA-Net not only reduces the number of parameters and the output dimensions in the training process, but also prevents network overfitting effectively. The experiment results show that FQA-Net has relatively low computational complexity and high competitiveness compared with the leading BIQA methods.