Reviewerly/exHarmony huggingface dataset:
Covid-19 - Detection of Unconstrained Face-Mask Framework

The main objective of this project is to identify if a person is wearing a mask or not, properly wearing a mask or face hand interaction. The present scenario of COVID-19 demands an efficient face mask detection application. Reports indicate that wearing face masks clearly reduces the risk of transmission [1]. Thus we are introducing a system based on Deep learning and Machine learning such that it can identify if a person is either wearing a mask properly or not. To implement the process, we consider the dataset that contains images of masked and unmasked faces that will undergo pre-processing such as data cleansing and data augmentation. The data will be trained using ML techniques like transfer learning that will accelerate the training speed and calculate the probabilities as well. The project is built using Convolution Neural Network (CNN) with OpenCv libraries along with computer vision. The implementation will be in Python. This system can be implemented in various areas such as grocery stores, movie theatres, hospitals, malls, and other places where there is more crowd.
Decision Support System for Scoring In Informatics Engineering Practicum Assistant With Simple Additive Weighting (Saw) Method Web 2.0 Based

Abstract— In this modern era, the assessment of an individual with other individuals is still in the subjective stage, a human being assesses the abilities of other humans by using various points of view. Likewise, what Pratikan did to his Pratikum Assistants gave them an assessment that was in accordance with what they had, but the assessment sometimes had different parameters so that the conclusions between one person and another were different, there was no similarity in judging one person.&#x0D; With the Simple Additive Weighting (SAW) method, the problem can be solved with the help of this method. Websites that have become part of social life and are closely integrated in daily activities. So using the SAW method, one of the alternatives in the assessment can provide a Pratikum Assistant.&#x0D; The results obtained from these values ​​can provide a new perspective on various kinds of problems. Deciding that no Practicum Assistant can be reused or the results of a given Assessor can help create a new resolution to the pattern of scoring in Practicum Assistants.
Vehicle Counting and Classification for Traffic Surveillance

This work was carried out to determine the performance of image processing algorithms in classifying and counting moving vehicles in video streams of traffic scenes recorded by stationary cameras. The method for detection and tracking approach is as follows. The moving vehicles are first extracted from the traffic scene by applying the adoptive background substraction in order detect and count the vehicles using Gaussian Mixture Model. Isolated picture blobs are identified as individual vehicles after background subtraction using threshold and median filters. After the blobs have been identified, the vehicles in a given location are counted and classified. The preliminary results demonstrate that the developed system can track vehicles efficiently and reliably when a clear view of the traffic scene is available. For optimal camera calibration, an accuracy better than 80% in counting vehicles was recorded. The results of the developed system demonstrate that with additional enhancements, it may be utilized in real-time to count and classify vehicles on busy traffic routes
CLIENT DEALER WEB APPLICATION PORTAL

Sales force is a cloud computing service as a software (SaaS) company that specializes in customer relationship relation management (CRM). Salesforce’s services allow business to use cloud technology to better connect with customers to use cloud technology to better connect with customers, partners and potential customers. Salesforce impressed investors recently by crushing third quarter estimates, reporting a third quarter revenue of $4,5 billion – up 33% year over year. As of 2017, Salesforce reportedly had 150,000 companies using their software – among which include Amazon (AMZN) – Get report, Adidas (ADDYY) , ADP (ADP) – get Report , American Express (AXP) – get Report and many , many more. Now a day’s internet has become sophisticated which made the data storage ease, previously we use to store the data in floppy, disk or a cd and it was really hard to find the data once lost. Now, through cloud computing platform it’s pretty easy to store bulk amount of data without causing any damage to it. This project basically revolves around internet, its usage and how it’s used to reduce the human burden.
Special Issue on Information Retrieval, Recommender Systems and Adaptive Systems

The current spread of the Internet across an ever-increasing number of devices, including mobile and IoT devices, has created an enormous flow of data [...]
Natural Language Enhancement for English Teaching&amp;nbsp;Using Character-Level Recurrent Neural Network with&amp;nbsp;Back Propagation Neural Network based Classification&amp;nbsp;by Deep Learning Architectures

Natural Language Processing (NLP) is an efficient method for enhancing educational outcomes. In educational settings, implementing NLP entails starting the learning process through natural acquisition. English teaching and learning have received increased attention from the relevant education departments as an integral aspect of the new curriculum reform. The environment of English teaching and learning is undergoing extraordinary changes as a result of the constant improvement and extension of teaching level and scale, as well as the growth of Internet information technology. As a result, the current research aims to look into techniques for efficiently using AI (artificial intelligence) apps to teach and learn English from the perspective of university students. This research can measure the levels as well as effectiveness of the employment of AI applications for teaching English based on deep learning techniques. There, the NLP based language enhancement has been carried out using Character-level recurrent neural network with back Propagation neural network (Cha_RNN_BPNN) based classification. With the help of this DL (deep learning) technique, it is possible to use AI methods to assist teachers in analysing and diagnosing students&amp;#39; English learning behaviour, replacing teachers in part to answer students&amp;#39; questions in a timely manner, and automatically grading assignments during the English teaching process. Experimental analysis shows Word Perplexity, Flesch-Kincaid (F-K) Grade Level for Readability, Cosine Similarity for Semantic Coherence, gradient change of NN, validation accuracy, and training accuracy of the proposed technique.
Algebraic representation of Three Qubit Quantum Circuit Problems

The evolution of quantum states serves as good fundamental studies in understanding the quantum information systems which finally lead to the research on quantum computation. To carry out such a study, mathematical tools such as the Lie group and their associated Lie algebra is of great importance. In this study, the Lie algebra of su(8) is represented in a tensor product operation between three Pauli matrices. This can be realized by constructing the generalized Gell-Mann matrices and comparing them to the Pauli bases. It is shown that there is a one-to-one correlation of the Gell-Mann matrices with the Pauli basis which resembled the change of coordinates. Together with the commutator relations and the frequency analysis of the structure constant via the algebra, the Lie bracket operation will be highlighted providing insight into relating quantum circuit model with Lie Algebra. These are particularly useful when dealing with three-qubit quantum circuit problems which involve quantum gates that is derived from the SU(8) Lie group.
State of Mapping and Path Planning Research for Multi-Wheeled a Literature Review

With the continuous advancement of autonomous navigation in the automotive industry, a wide range of research is being conducted with the ultimate goal of improving or applying the autonomous navigation of passenger vehicles.
Identification of WFRFT signals' parameter based on multi‐domain statistical features

An efficient approach to recognize the parameter of the weighted-type fractional Fourier transform signals based on the higher-order cumulant is proposed. Due to the index additivity of weighted-type fractional Fourier transform, the statistical features on different weighted fractional domains could be jointly considered, to overcome the problem of parameter ambiguity arising from the existing estimation methods based on higher-order cumulant. This method can improve the ability of blind signal recognition no matter in a cooperative or non-cooperative scenario.
Impact of Land Use and Transport Interaction on Transmition of COVID-19 in Jakarta

Land use and transportation are well known to interact and influence one another. The COVID-19 pandemic is the global health crisis. The world faces difficulties managing disasters while making efforts to slowing the spread of COVID-19. The COVID-19 pandemic spreads quickly through physical contact, making it easy to spread in public places. The purpose of this study to determine the effects of land use and transportation interactions on COVID-19 transmission. The quantitative descriptive analysis and spatial statistical methods in Geographic Information Systems (GIS) have been used in this study. Descriptive analysis to describe data related to land use characteristics in the research area. Spatial Statistical Method in GIS to analyze data from both structured and spatial observations. Cross-checking the land use from Google Street View around the bus stop within a 400 meter radius with the existing land use directly yielded the observations. The findings revealed that the type of land use demonstrated an intensity of activity that had a high potential for spreading COVID-19 because at the bus stop with land use around the trading center, shopping centers, and office buildings that have a high activity intensity, it appears that they have a high positive number of COVID-19
Analysis of Big Data Utilization in Technology Companies (Gojek Case Study: PT GoTo Gojek Tokopedia Tbk)

Rapid technological developments create trillions of bytes of data every day from various platforms. This amount of data is also called big data, which is often used by various companies in running their business. This study aims to find out how Gojek, under the auspices of PT GoTo Gojek Tokopedia Tbk, utilizes and uses big data. This study uses a literature review research method, while data collection techniques are carried out by exploring journals and other information relevant to the study. The results of the study show that Gojek uses big data to improve business by analyzing personal data such as travel track records, types of food purchased to drug lists. The use of big data is to know individual customers better.
Using Benime to Develop the Animation Board for Production Écrite Élémentaire Courses

This research is based on the observations regarding learning to write in production écrite élémentaire course that has not reached the target because of inappropriate media and relatively low value data from the evaluation of learning outcomes. The research aims to design and develop learning media through an animation board using benime in accordance with the demands of the A2 DELF level of French writing competence and to determine student learning outcomes. The procedure begins by carrying out four stages, namely collecting information and data, designing learning media, and expert validation of the media and of the material included. After the four stages are carried out and declared feasible by the validator, the learning media was tested on students in the classroom. The outputs are international journal publication, copyright proposal, textbook, research reports, and animated whiteboard products. Keywords: writing, learning media, animation board, benime
A Study on the Integration of the 4th Industrial Revolution Technology and Taekwon-Gymnastics in Preparation for the Infectious Disease Pandemic

This study proposed a convergence plan for Taekwon-gymnastics with the 4th industrial revolution technology by conducting text analysis using literature research and group focus research methods to find out how the 4th industrial revolution technology can be applied to taekwondo in preparation for the infectious disease pandemic.&#x0D; First, in constructing Taekwon-gymnastics works, competition rules, Taekwondo poomsae, characteristics of trainees, physical strength elements, dance music of various genres, popular dance movements, and data of all works should be collected and entered into artificial intelligence to develop and distribute standardized works. Second, artificial intelligence should develop an artificial intelligence guidance system by identifying the wrong movements or beats of Taekwon- gymnastics trainees by themselves and giving feedback to the trainees. Third, data on the training results of trainees using the Internet of Things can be collected and used for trainee management. Fourth, it is necessary to develop programs on the theme of Taekwon-gymnastics, such as virtual reality in the form of games and making one's own works in games, to provide feedback as well as induce interest of trainees.&#x0D; In the era of the infectious disease pandemic, changes in training methods using core technologies of the 4th Industrial Revolution will be essential in view of the times, and efforts to change this trend in a positive direction are needed.
A Study on the Instructional Design of English Reading for Writing Class in Senior High School Based on Text Analysis

Taking Textbooks for Senior English of People’s Education Press (2019 edition), Book one, Unit one, Teenage Life, Reading for Writing section as an example, this article intends to explore an instructional design in the light of text analysis put forward by General Senior High School English Curriculum Standards (2017 Edition). It is aimed at helping students better comprehend the text and improve the teaching effect of English reading for writing class.
Histopathological Evaluation of the Microtomy Artifacts on Haematoxylin and Eosin Section; Hospital Based Cross-Sectional Study

Background information: Microtomy artifacts are abnormal structures or features in histological slides resulting from tissue sectioning by microtome. Objective: To determine the type and prevalence of microtomy artifacts found in histopathological tissue sections slides at Bugando Medical Centre (BMC). Methodology: This was a cross-sectional observational study that involved 547 consecutive hematoxylins and eosin (H&amp;E) stained sections of histological archived tissue slides of January 2021. The slides were retrieved from the archives of the histopathology laboratory at BMC, Mwanza Tanzania and analysed for artifacts under a light microscope. Results: A total number of 547 histopathological slides were retrieved for the study and 412 (75.3%) slides had microtomy artifacts present while the remaining 135 (24.7%) histopathological slides had no microtomy artifacts. Of 412 slides with microtomy artifacts, 204(49.5%) slides had only one type of microtomy artifacts while the remaining 208 (50.5%) slides had more than one type of microtomy artifacts. There was a total of 672 microtomy artifacts, and the majority 576 (85.7%) were due to section cutting, followed by trimming artifacts in 92 (13.69%) of the slides. The least artifact was floatation which was seen in 4 (0.6%) of the slides. For the floatation artifact, the folding artifact was the most commonly seen in 300(54.8%) of the slides. Conclusion: Higher prevalence of microtomy artifacts at BMC reflects the problem of interpretation of histopathological slides in our setting. Section folding artifacts were the most prevalent pattern of artifact observed in this study.
Improved Convergence Guarantees for Learning Gaussian Mixture Models by EM and Gradient EM

We consider the problem of estimating the parameters a Gaussian Mixture Model with K components of known weights, all with an identity covariance matrix. We make two contributions. First, at the population level, we present a sharper analysis of the local convergence of EM and gradient EM, compared to previous works. Assuming a separation of $\Omega(\sqrt{\log K})$, we prove convergence of both methods to the global optima from an initialization region larger than those of previous works. Specifically, the initial guess of each component can be as far as (almost) half its distance to the nearest Gaussian. This is essentially the largest possible contraction region. Our second contribution are improved sample size requirements for accurate estimation by EM and gradient EM. In previous works, the required number of samples had a quadratic dependence on the maximal separation between the K components, and the resulting error estimate increased linearly with this maximal separation. In this manuscript we show that both quantities depend only logarithmically on the maximal separation.
Adversarial Dual-Student with Differentiable Spatial Warping for Semi-Supervised Semantic Segmentation

A common challenge posed to robust semantic segmentation is the expensive data annotation cost. Existing semi-supervised solutions show great potential for solving this problem. Their key idea is constructing consistency regularization with unsupervised data augmentation from unlabeled data for model training. The perturbations for unlabeled data enable the consistency training loss, which benefits semi-supervised semantic segmentation. However, these perturbations destroy image context and introduce unnatural boundaries, which is harmful for semantic segmentation. Besides, the widely adopted semi-supervised learning framework, i.e. mean-teacher, suffers performance limitation since the student model finally converges to the teacher model. In this paper, first of all, we propose a context friendly differentiable geometric warping to conduct unsupervised data augmentation; secondly, a novel adversarial dual-student framework is proposed to improve the Mean-Teacher from the following two aspects: (1) dual student models are learned independently except for a stabilization constraint to encourage exploiting model diversities; (2) adversarial training scheme is applied to both students and the discriminators are resorted to distinguish reliable pseudo-label of unlabeled data for self-training. Effectiveness is validated via extensive experiments on PASCAL VOC2012 and Cityscapes. Our solution significantly improves the performance and state-of-the-art results are achieved on both datasets. Remarkably, compared with fully supervision, our solution achieves comparable mIoU of 73.4% using only 12.5% annotated data on PASCAL VOC2012. Our codes and models are available at https://github.com/cao-cong/ADS-SemiSeg.
High-fidelity tracking of the evolution of multilevel quantum states

The method of quantum tomography, which allows us to track with high accuracy the evolution of multilevel quantum systems (qudits) in Hilbert spaces of various dimensions is presented. The developed algorithms for quantum control are based on the use of the spinor representation of the Lorentz transformation group. In the simplest case of one-qubit states, it turns out that, in addition to three-dimensional rotations on the Bloch sphere, one can introduce four-dimensional Lorentz pseudorotations, similar to the transformations of the special theory of relativity. We show that feedback through weakly perturbing adaptive quantum measurements turns out to be capable of providing high-precision control of the quantum system, while introducing only weak perturbations into the initial quantum state. It turns out that, together with the control of a quantum system through its weak perturbation, the developed algorithms for controlling the evolution of the state of a quantum system can be super-efficient, providing a higher measurement accuracy than any standard POVM (Positive-Operator Valued Measure) protocols. The results of the study are important for the development of optimal adaptive methods for quantum states and operations controlling. The results obtained are essential for the development of high-precision control methods for quantum information technologies.
Online Feature Selection for Efficient Learning in Networked Systems

Current AI/ML methods for data-driven engineering use models that are mostly trained offline. Such models can be expensive to build in terms of communication and computing cost, and they rely on data that is collected over extended periods of time. Further, they become out-of-date when changes in the system occur. To address these challenges, we investigate online learning techniques that automatically reduce the number of available data sources for model training. We present an online algorithm called Online Stable Feature Set Algorithm (OSFS), which selects a small feature set from a large number of available data sources after receiving a small number of measurements. The algorithm is initialized with a feature ranking algorithm, a feature set stability metric, and a search policy. We perform an extensive experimental evaluation of this algorithm using traces from an in-house testbed and from a data center in operation. We find that OSFS achieves a massive reduction in the size of the feature set by 1-3 orders of magnitude on all investigated datasets. Most importantly, we find that the accuracy of a predictor trained on a OSFS-produced feature set is somewhat better than when the predictor is trained on a feature set obtained through offline feature selection. OSFS is thus shown to be effective as an online feature selection algorithm and robust regarding the sample interval used for feature selection. We also find that, when concept drift in the data underlying the model occurs, its effect can be mitigated by recomputing the feature set and retraining the prediction model.
Besov rough path analysis

Rough path analysis is developed in the full Besov scale. This extends, and essentially concludes, an investigation started by [Pr\"omel--Trabs, Rough differential equations driven by signals in {B}esov spaces. J. Diff. Equ. 2016], further studied in a series of papers by Liu, Pr\"omel and Teichmann. A new Besov sewing lemma, a real-analysis result of interest in its own right, plays a key role, and the flexibility in the choice of Besov parameters allows for the treatment of equations not available in the H\"older or variation settings. Important classes of stochastic processes fit in the present framework.
Learning to Minimize the Remainder in Supervised Learning

The learning process of deep learning methods usually updates the model's parameters in multiple iterations. Each iteration can be viewed as the first-order approximation of Taylor's series expansion. The remainder, which consists of higher-order terms, is usually ignored in the learning process for simplicity. This learning scheme empowers various multimedia based applications, such as image retrieval, recommendation system, and video search. Generally, multimedia data (e.g., images) are semantics-rich and high-dimensional, hence the remainders of approximations are possibly non-zero. In this work, we consider the remainder to be informative and study how it affects the learning process. To this end, we propose a new learning approach, namely gradient adjustment learning (GAL), to leverage the knowledge learned from the past training iterations to adjust vanilla gradients, such that the remainders are minimized and the approximations are improved. The proposed GAL is model- and optimizer-agnostic, and is easy to adapt to the standard learning framework. It is evaluated on three tasks, i.e., image classification, object detection, and regression, with state-of-the-art models and optimizers. The experiments show that the proposed GAL consistently enhances the evaluated models, whereas the ablation studies validate various aspects of the proposed GAL. The code is available at \url{https://github.com/luoyan407/gradient_adjustment.git}.