Reviewerly/exHarmony huggingface dataset:
Frame interpolation in video stream using optical flow methods

Abstract The article deals with the issues of interpolation and extrapolation of video stream frames in real time. Results of research of the method of generation of intermediate frames of a video stream are given. The method is based on the optical flow calculate. The results of comparison of implementations of the method using the Farneback, Brox and Duality based TV-L1 approaches for the calculation of optical flow and CUDA technology are presented. It is shown that the Farneback method best result is gives. The Duality based TV-L1 method shows a smooth and high-quality optical flow, but gives a very slow calculation speed. The Brox method is not suitable at all, since it shows the worst result both in terms of calculation speed and optical flow quality. The results of comparison of implementations based on CUDA and OpenCL technologies are also presented. OpenCL implementations are almost two times slower than CUDA implementation. This leads to the inability to generate frames in 60 FPS in real time even for low resolution images. However, CUDA can work only on NVidia GPU accelerators. In conclusion, the ways of further research on this topic based on the use of deep neural networks are presented.
A Novel Grey Wolf Optimizer for Solving Optimization Problems

As a well-known bio-inspired optimization algorithm, the gray wolf optimizer mimics the social dominant hierarchy and social interactions of gray wolves in nature. Inspired by this hierarchy, this study attempted to present a novel gray wolf optimizer in which the wolves are classified into four groups, namely alpha, beta, delta, and omega. These classes may include male or female wolves or both. The gender of wolves and their superior classes determine the updating position of wolves in each class. After allocating each wolf to one of the alpha, beta, and delta classes, the position of the other wolves is updated with respect to these classes. To evaluate the performance of the proposed method, a set of benchmark functions were used. The results showed that the proposed gray wolf optimizer outperforms the conventional wolf optimizer in most cases.
CLEAR: A Holistic Figure-of-Merit for Post- and Predicting Electronic and Photonic-based Compute-system Evolution

Continuing demands for increased computing efficiency and communication bandwidth have pushed the current semiconductor technology to its limit. This led to novel technologies with the potential to outperform conventional electronic solutions such as photonic pre-processors or accelerators, electronic-photonic hybrid circuits, and neural networks. However, the efforts made to describe and predict the performance evolution of compute-performance fall short to accurately predict and thereby explain the actually observed development pace with time; that is all proposed metrics eventually deviate from their development trajectory after several years from when they were originally proposed. This discrepancy demands a figure-of-merit that includes a holistic set of driving forces of the compute-system evolution. Here we introduce the Capability-to-Latency-Energy-Amount-Resistance (CLEAR) metric encompassing synchronizing speed, energy efficiency, physical machine size scaling, and economic cost. We show that CLEAR is the only metric to accurately describe the historical compute-system development. We find that even across different technology options CLEAR matches the observed (post-diction) constant rate-of-growth, and also fits proposed future compute-system (prediction). Therefore, we propose CLEAR to serve as a guide to quantitatively predict required compute-system demands at a given time in the future.
Language identification using deep neural network for Indian languages

The problem of automatic language identification (LID) can be defined as the process of automatically identifying the language of a given spoken utterance. The need for reliable LID is continuously growing due to the technological trend toward increased human interaction with machines. In this paper a language identification system is implemented using Deep Neural Network (DNN) framework using MFCC features in context free and context dependent manner. DNN models for languages are also built using bottleneck features derived using an autoencoder structure. Performance of proposed method is validated using five Indian languages, namely, Gujarati, Hindi, Malayalam, Telugu, and Urdu
POI Recommendation Based on First-Order Collaborative Filtering Tree

Point-Of-Interest (POI) recommendation plays an important role in Location-Based Social Networks(LBSN), which is widely used in popular attraction recommendations and travel route planning applications. The traditional recommendation algorithms fail to make full use of social relationships, user check-in distribution features and geographic information because they only use a simple linear function to model the above features. In order to solve the existing problems, we propose a recommendation framework-NCFT(Neural Collaborative Filtering Tree), which can fuse various side information. In the NCFT model, we propose an unsupervised user check-in distribution feature extractor, namely CD-Ex, which unsupervised learning user checkin distribution features. We also propose to build a user-based collaborative filtering tree and item-based collaborative filtering tree, and use the idea of messaging to learn deep representations of users and items. In these two modules, we use multi-head attention and vanilla attention to learn the representations of users and POIs. As for the user social relationship, we use the user's friends in the user-based collaborative tree to assign weights and aggregate his friends' features. The experimental results show that our model has a significant improvement in AUC and F1 compared to other models.
Real-Time Phase Tracking Scheme With Mismatched-Basis Data for Phase-Coding Quantum Key Distribution

Phase drift is an inevitable problem in the practical implementation of phase-coding quantum key distribution (QKD) systems. Conventional active phase tracking and compensation solutions cannot be implemented during the key transmission process, resulting in reduced efficiency of the system. In this paper, we propose a single-photon level real-time phase tracking scheme using only the estimated quantum bit error rate (QBER) and mismatched-basis data to acquire the phase drift parameter instead of a phase scanning process or reference light pulses, without extra hardware or interrupting the transmission of quantum signals. This scheme is applied into a phase-coding QKD system with active phase disturbance and obtains an average QBER of $1.01\%$ with a standard derivation of $0.37\%$ over 50 h of continuous operation. Experimental results show that this scheme enables QKD systems to operate continuously with long-term stability and keep a low level of QBER even with rapid phase drift, suggesting its suitability for practical applications.
Elderly Body Movement Alteration at 2nd Experience of Digital Art Installation with Cognitive and Motivation Scores

The prevalence of advanced medical treatment has led to global population aging, resulting in increased numbers of dementia patients. One of the most intractable symptoms of dementia is apathy, or lack of interest and enthusiasm, which can accompany memory and cognitive deterioration. Development of a novel method to ameliorate apathy is desirable. In this feasibility trial, we propose a series of digital art installations as a candidate dementia intervention approach. Seven, three-minute scenes of digital images and sounds were presented to visitors either passively or in response to their reactions (motion and sound). We evaluated the potential of this application as an intervention against apathy in an elderly living home. We collected the dementia global standard Mini-Mental State Examination (MMSE) and questionnaire scores of sensory pleasure and motivation along with behavioral motion data in twenty participants. We further compared responses between the first and second experiences in the thirteen participants that were present for both days. Overall, we found a significant increase in participants’ motivation. In these subjects, head and right hand motion increased over different scenes and MMSE degrees, but was most significant during passive scenes. Despite a small number of subjects and limited evaluations, this new digital art technology holds promise as an apathy intervention in the elderly and can be improved with use of wearable motion sensors.
A Comprehensive Comparison of Machine Learning Based Methods Used in Bengali Question Classification

QA classification system maps questions asked by humans to an appropriate answer category. A sound question classification (QC) system model is the pre-requisite of a sound QA system. This work demonstrates phases of assembling a QA type classification model. We present a comprehensive comparison (performance and computational complexity) among some machine learning based approaches used in QC for Bengali language.
Reduce coherence loss in coherence-assisted transformation

We discuss the coherence-assisted transformation of pure coherent states under incoherent operations and show that the amount of coherence loss can be reduced with the help of the auxiliary coherent state. Starting with the simplest case i.e., the main coherent state and auxiliary coherent state are both 2×2 dimensional, we compare the change of coherence before and after the transformation and find that the amount of coherence loss can be reduced due to the increase of the coherence of auxiliary coherent state. In general, when the main coherent state and auxiliary coherent state are high-dimensional, the result is also true. For the coherence of auxiliary coherent state is increased, we can get the maximally coherent state at some conditions, this case can be viewed as a new way for the distillation of coherent state. These interesting results of coherence transformation will give us a better understanding of the resource manipulation.
A combined visual navigation method for greenhouse spray robot

In the greenhouse environment, in order to solve the problem that there be deviations in the straight path of the spraying robot, and the navigation information needs to be quickly obtained at the turn, a navigation algorithm combining the red navigation line and the QR code is designed. After processing the acquired navigation line image, the navigation correction processing is performed based on the obtained deviation and yaw angle. Thereby achieving precise navigation in straight lines.However, red navigation line has problems such as low turning efficiency and poor corrective effect of deviation during cornering. Therefore, a QR code navigation method was designed to accomplish the requirement of prompt and accurate navigation at a turning point by identifying the navigation commands stored in the QR code.A total of 120 navigation line images and 120 QR code images under various conditions were collected to verify the algorithm. The average correction rate of the navigation line is 93.33%, and the average recognition rate of the QR code is 92.50%. The result of the navigation experiment shows that the position deviation can be controlled at 2.2 cm under different navigation speed conditions. The average deviation angle of heading angle is kept below 2.1°, and the maximum deviation angle is 4.6°. Which meets the real-time and accuracy requirements of the navigation of spraying robots.
Performance Comparison of Deep Learning Based Face Identification Methods for Video Under Adverse Conditions

Face identification is an important problem in computer vision, which has many application areas. Recently, a number of deep-learning-based face identification and verification methods have been proposed in the literature, which demonstrate remarkable results on large image and video databases. Although the databases used for training and testing deep-learning architectures contain illumination, head pose, and expression variations, they do not reflect the difficult distortions (such as blur and low resolution), which may be encountered when using data from various sources (e.g. surveillance cameras). In this work, our goal is to systematically compare the performance of recent deep-learning-based methods for face identification using video under challenging conditions. We evaluate three deep learning architectures OpenFace, VGGFace2, and ArcFace. The experimental results indicate that even the most successful deep-learning-based face identification methods show poor performance under challenging distortions on the images such as noise, blur and contrast variations.
Punjabi to ISO 15919 and Roman Transliteration with Phonetic Rectification

Transliteration removes the script barriers. Unfortunately, Punjabi is written in four different scripts, i.e., Gurmukhi, Shahmukhi, Devnagri, and Latin. The Latin script is understandable for nearly all factions of the Punjabi community. The objective of our work is to transliterate the Punjabi Gurmukhi script into Latin script. There has been considerable progress in Punjabi to Latin transliteration, but the accuracy of present-day systems is less than 50% (Google Translator has approximately 45% accuracy). We do not have the facility of a rich parallel corpus for Punjabi, so we cannot use the corpus-based techniques of machine learning that are in vogue these days. The existing systems of transliteration follow grapheme-based approach. The grapheme-based transliteration is unable to handle many scenarios such as tones, inherent schwa, glottal stops, nasalization, and gemination. In this article, the grapheme-based transliteration has been augmented with phonetic rectification where the Punjabi script is rectified phonetically before applying character-to-character mapping. Handling the inherent short vowel schwa was the major challenge in phonetic rectification. Instead of following the fixed syllabic pattern, we devised a generic finite state transducer to insert schwa. The accuracy of our transliteration system is approximately 96.82%.
Anomaly Detection for Screw Tightening Timing Data with LSTM Recurrent Neural Network

Screws are important fasteners in industrial production and are also weak points in various mechanical devices. Therefore, screw tightening detection is of great significance. The traditional tightening quality detection method mainly collects the parameters of the tightening process and draws the rotation angle-torque curves, and draws conclusions through manual analysis, which is time-consuming and labor-intensive and low in efficiency. In order to solve this problem, this paper proposes a model based on LSTM that can automatically analyze the quality of the tightening curve, which improves the timeliness and accuracy of the test. The work of this paper is mainly divided into the following two phases. First, the original data is preprocessed using a feature extraction algorithm based on traditional sampling. In the second phase, we used a classification sample as training data to train a neural network based classifier. In the experiment, we compared the model with traditional machine learning methods, such as SVM, Random Forest. The result is better than traditional machine learning methods.
Hepatic vessel segmentation based on animproved 3D region growing algorithm

Abstract Hepatic vessel segmentation of CT image is of great importance in the computer aided diagnosis. This paper proposes an automatic segmentation method of 3D vessel CT images to obtain better segmentation results. First, the single Gaussian kernel of Hessian matrix in the Jerman’s algorithm is replaced by bi-Gaussian kernel. Then, a histogram-based method is adopted to adaptively estimate the threshold value of the region growing. Finally, a new scheme is proposed forautomatically searching seed points of the region growing. The experimental results show that the proposed method achieves a significant enhancement of hepatic vessels segmentation with an average accuracy 98.1%.
Eye-Movement and Touch Dynamics: A Proposed Approach for Activity Recognition of a Web User

Behavioral Profiling is the way in which the user interacts with the mobile sensors and services. Identifying the context in which users' interaction occur is an important step toward automatic interpretation of behavior. Activity Recognition and online task monitoring are required for various context aware applications. As a result, two sources of behavioral biometric data are investigated for the development of user Web identification models. The adopted approach is based on eye-gaze movements along with touch dynamics interactions. Fusing different behavioral biometric traits, i.e, the unique characteristics of eye movements and the distinctive way a person touches on a touchscreen device, can improve the identification accuracy.
A multi‐tone central divided difference frequency tracker with adaptive process noise covariance tuning

Summary The problem of real‐time frequency estimation of nonstationary multi‐harmonic signals is important in many applications. In this paper, we propose a novel multi‐frequency tracker based on a state‐space representation of the signal with Cartesian filters and the second‐order central divided difference filter (CDDF), which improves the performance of the extended Kalman filter (EKF) by using Stirling's interpolation method to approximate the mean and covariance of the state vector. A crucial element of the method is the adaptive scaling of the process noise covariance matrix appearing in the filter equations, as a function of the innovation sequence, which tunes the accuracy‐reactivity trade‐off of the filter. The proposed solution is evaluated against two approaches from the literature, namely the factorized adaptive notch filter (FANF) and the extended Kalman filter frequency tracker (EKFFT). Several experiments emphasize the estimation accuracy of the proposed method as well as the improved robustness with respect to initial errors and input signal complexity. The presented method appears to be particularly efficient with rapidly varying frequencies, thanks to the update mechanism that adjusts the filter parameters based on the amplitude of the estimation error.
Application Research of Vision Sensor in Material Sorting Automation Control System

Abstract Based on the PLC in the material sorting automation system, the paper builds an automatic material sorting system based on visual sensors. The control system involves various sensor technologies such as photoelectric sensors and vision sensors, mechanical technology, electrical and electronic technology, frequency converter technology, motor drive technology, air pressure control technology, human-machine interface technology, etc., and integrates the application of various technologies in training. On the platform, students can familiarize themselves with the process of detecting, transmitting, and processing the automatic production line and the control process of the system through the practice of this training platform. They can be familiar with the mechatronics technology. The system first detects the metal through the inductive sensor, detects the colour through the photoelectric sensor, and finally detects whether the processing is qualified by the machine vision system, and obtains the target data, and finally controls the robot to realize the sorting action, and completes the storage operation of the stereo warehouse.
Head posture detection with embedded attention model

Abstract Based on Convolutional Neural Network, the paper presents a compact detection algorithm that can estimate the head pose from a single picture. Our method is based on soft stage wise regression. In order to reduce model complexity, three-dimensional detection of the “pitch, yaw, and roll” of the head posture adopts multi-level classification. Each level of classification requires only a small number of classification tasks and fewer neurons. In order to enhance the feature expression of the algorithm, the attention model is embedded. Attention model includes channel attention structure and spatial attention structure, enhancing the feature expression of the feature map in the two dimensions of the intermediate feature map channel and space. The attention model can be seamlessly integrated into the CNN architecture with low overhead. The experiment proves that the improved algorithm compares the method model proposed by Yang with a smaller complexity of 4.36M and an average absolute error of 0.7%~0.9%.
Technical Comparison Of Cloud Enterprise Resource Planning Using Web Systems

Enterprise Resource Planning (ERP) become an essential information system for most organization. With the revolution of cloud computing and its advantages, the cloud ERP system become an attractive option. The aim of this paper are to make a good comparison of the different cloud ERP systems and give advice and recommendations on the use of any kind of system. ERP systems are now the backbone of many companies to collect business data from a different department in a single database and allow employees to generate reports that simplify business processes. Cloud computing is growing and is starting to get into more and more companies. The best and most tested cloud systems allow more companies to gain confidence in the clouds. Traditional ERP systems are hosted locally in every company where they themselves need to maintain the system. Since the growth of cloud computing, are beginning to be developed as cloud applications. By removing the maintenance responsibilities of business equipment, the cloud-based ERP system is on an upward trend. This study contains a comparison of different ERP cloud systems. The aim of paper is to propose a new framework and develop a web-based support system to compare ERP systems. The three systems, which will be analyzed, are SAP S/4HANA, Microsoft Dynamics 365, and Oracle Cloud.
Identification of Code Smell Using Machine Learning

Code smells are used to improve the quality of the software. Code smell detection detects the code problems like long method, large class, lazy class, long parameter list, feature envy, primitive obsession detector and too many literal detectors present in the code. In this work, two algorithms are used namely Support Vector Machine (SVM) and Random Forest algorithm. Support Vector Machine acts as classifier and the Random Forest algorithm are used for predicting the range of data. Decision making technique is used to identify the various problems present in the code. Code smell detection is a testing tool and it is mainly used by the developers, when the size of the code becomes unmanageable for manual detection. It also identifies deeper problems like syntax error, runtime error. It gives the output by analyzing the code in six different modules in this work.
Enhancement Algorithms for Low-Light and Low-Contrast Images

Images in bad weather can be degraded by scattering atmospheric particles, and suffer from low contrast and faint color. Main focus of this work is on dehazing methods that can be used for low-light and low-contrast conditions. Dehazing algorithms can be applied to low-light (night) imaging in a way that inverted night images are considered as hazy images. Different image quality metrics are used to measure quality of image enhancement algorithm. Since it is not easy to obtain original image without haze, special attention is given to the no-reference metrics that evaluate image contrast enhancement.