Reviewerly/exHarmony huggingface dataset:
CaptureIt! - A Web-based Attendance System Using Face Recognition

Covid-19 has caused an unprecedented paradigm shift in the daily, quotidian tasks. The current world needs social distancing and minimal physical interaction. Despite all these hardships, an educational institution must keep working. The methods currently used in the majority of institutions, for recording attendance in the classroom, involve manually writing the student's signature and then uploading these sheets into the system for further analysis. This method is now highly obsolete and anachronistic. Moreover, it is deleterious as it hardly corroborates with the contactless guidelines. This paper describes a system, where the user can record his/her attendance using face recognition, and all the statistics will be inherently calculated by our system for the teachers to analyze in a contactless but efficient manner.
A Study on Various Phishing Techniques and Recent Phishing Attacks

Now-a-days internet has become a very unsafe space to deal with. Hackers are constantly trying to gain the user's personal information, and detailed credentials. So many websites on the internet, even though safe, this safety cannot be assured by all websites. These rule breakers avoid abiding by rules, and try to employ methods like trickery and hacking to gain illegal access to private information. T o be able to overcome this problem, we need to first understand the intricacies of how the virus is designed. This paper mainly deals with the different phishing techniques and recent phishing attacks that took place during COVID 19. like Link Manipulation, Filter Evasion, Website Forgery, Phone Phishing and Website Forgery. We have also studied a subtle method to perform phishing attacks that makes links appear legitimate, but actually redirect a victim to an attacker's website called Convert Redirect. In this paper , we present some phishing examples like Paypal phishing which involves sending an email that fraudulently claims to be from a well known company and Rapidshare Phishing where in the spoofed web page, phishers attempt to confuse their victims just enough to entice them to enter their login name and password. To perform these types of phishing the Phishers uses so many phishing techniques like Link Manipulation, Filter Evasion, Website Forgery, Phone Phishing and Website Forgery. Phishing techniques include the domain of email messages. Phishing emails have hosted such a phishing website, where a click on the URL or the malware code as executing some actions to perform is socially engineered messages. Lexically analyzing the URLs can enhance the performance and help to differentiate between the original email and the phishing URL. As assessed in this study , in addition to textual analysis of phishing URL, email classification is successful and results in a highly precise anti phishing. From the thorough analysis of the research paper, we have understood how phishing attacks work and the different methods employed to carry out the attack. Also, we have studied some of the most recent phishing attacks and measures taken by the authorities to overcome and prevent any such attacks in future.
Self-Trained Video Anomaly Detection Based on Teacher-Student Model

Anomaly detection in videos is a challenging problem in computer vision. Most existing methods need supervised information to train their models, which limits their applications in real world scenario. Therefore, self-trained methods which do not need manually labels receive increasing attentions recently. In this paper, we propose a novel self-trained video anomaly detection method based on teacher-student model. The teacher-student architecture can significantly improve the performance of self-trained video anomaly detection by utilizing the unlabeled samples. We test our method on two surveillance datasets. Experiment results show that our method achieves better performance than state-of-the-art unsupervised methods on both datasets and achieves comparable performance as semi-supervised methods, which experimentally proves the effectiveness of our method.
Learning Quantities of Interest from dynamical systems for observation-consistent inversion

Dynamical systems arise in a wide variety of mathematical models from the physical, engineering, life, and social sciences. A common challenge is to quantify uncertainties on model inputs (i.e., parameters) that correspond to a quantitative characterization of uncertainties on observable Quantities of Interest (QoI). To this end, we consider a stochastic inverse problem (SIP) with a solution described by a pullback probability measure. This is referred to as an observation-consistent solution since its subsequent push-forward through the QoI map matches the observed probability distribution on model outputs. A distinction is made between QoI useful for solving the SIP and arbitrary model output data. In dynamical systems, model output data are often given as a series of state variable responses recorded over a particular time window. Consequently, the dimension of output data can easily exceed O(1E4) or more due to the frequency of observations, and the correct choice or construction of a QoI from this data is not self-evident. We present a new framework, Learning Uncertain Quantities (LUQ), that facilitates the tractable solution of SIPs for dynamical systems. Given ensembles of predicted (simulated) time series and (noisy) observed data, LUQ provides routines for filtering data, learning the underlying dynamics in an unsupervised manner, classifying the observations, and performing feature extraction to learn the QoI map. Subsequently, time series data are transformed into samples coming from the underlying predicted and observed distributions associated with the QoI so that solutions to the SIP are computable. Following the introduction and demonstration of LUQ, numerical results from several SIPs are presented for a variety of dynamical systems arising in the life and physical sciences. In the interest of scientific reproducibility, we provide links to our Python implementation of LUQ, as well as all data and scripts required to reproduce the results in this manuscript.
Construction of University Comprehensive Budget Management Information System Based on Big Data and Cloud Platform

With the gradual increase of the state's support to the angle, the enrollment scale of colleges and universities is gradually expanding, so the financial data volume of colleges and universities is also increasing. In order to make the university's financial management and internal control more scientific and reasonable, cloud computing and big data mining method is considered using. This paper according to the financial data characteristics of colleges and universities management decision analysis, decision making method based on cloud computing is put forward at present the advantages and disadvantages of cloud computing, using cloud computing data mining method., controlling the internal financial budget of the university.
Research on Quality Evaluation of Distance Teaching in Public Security Colleges Based on Decision Tree Classification Algorithm

Because the quality of distance teaching in Public Security Colleges is related to a variety of factors, it has the characteristics of complex and changeable, but the traditional evaluation method of distance teaching quality in Public Security Colleges does not have the characteristics of this change, so the evaluation error of distance teaching quality in Public Security Colleges is huge, and it can not meet the actual requirements of modern distance teaching quality control in Public Security Colleges. In order to improve the quality evaluation effect of distance teaching in Public Security Colleges, a quality evaluation method of distance teaching in Public Security Colleges Based on decision tree classification algorithm is proposed. Firstly, it analyzes the principle of distance teaching quality evaluation in Public Security Colleges, and designs the corresponding function module of distance teaching quality evaluation in Public Security Colleges; Then, the hardware and software of the system are designed in detail; Finally, a comparative experiment is carried out with the current distance teaching quality evaluation system in other universities. The results show that the work efficiency of the distance teaching quality evaluation system designed in this paper can adapt to the development trend of Modern Distance Teaching in Public Security Colleges. At the same time, the precision of distance teaching quality evaluation in Public Security Colleges is high, which solves the problem of large error in current distance teaching quality evaluation in Public Security Colleges, and has high practical application value.
Blockchain Technology and Healthcare Applications

Blockchain technology (BCT) is an evolving digital technology that changes digital operations' dimensions by conducting distributed ledger transactions without the involvement of third parties. The chapter aims to examine the use of BCT in healthcare and develop a hierarchical theoretical framework. The framework is based on the stakeholder principle and exposes BC's effect on smart healthcare. Its decentralised existence is an ideal catalyst for the much-needed transition toward well-organised healthcare as it solves the most pressing issues.
Event-triggered control optimal tuning through bio-inspired optimization in robotic manipulators

This paper proposes the tuning approach of the event-triggered controller (ETCTA) for the robotic system stabilization task where the reduction of the stabilization error and the data broadcasting of the control update are simultaneously considered. This approach is stated as a dynamic optimization problem, and the best controller parameters are obtained by using fourteen different bio-inspired optimization algorithms. The statistics results reveal that, among the tested bio-inspired optimization algorithms, the most reliable algorithm in the proposed tuning problem is the differential evolution variant DE/Best/1/Exp. The obtained result is validated both in numerical simulation as well as using a laboratory prototype. The simulation results indicate that the obtained control parameters can also deal with disturbances and reference changes not considered in the ETCTA's optimization problem formulation without significantly worsening the control design objective. Experimental results disclose that the proposed event-triggered control tuning approach provides the best trade-off between the number of control signal updates and the position error among other tuning approaches, decreasing the data broadcasting of the control update by around 86.33% with a non-significant increase in the stabilization error of around 26.53%.
Signal Source Distribution Approximation to Speedup Scalar Quantizer Design

Classical quantizer design approaches using the Lloyd-Max algorithm (or k-means) have served signal processing applications for more than three decades. With the advent of distributed signal processing and machine learning at edge devices, novel alternatives for quantizers design will be desired to address the energy, communication and hardware constraints. To address these resource challenges, we propose a model-driven approach, termed Approximate Lloyd-Max (ALM) design, based on piecewise linear approximation of the signal-source density. From the principles of the ALM design, we develop a data-driven quantizer, or Learning ALM (LALM), using statistical learning methods. By mathematical analysis, we show convergence of the ALM quantizer near the limit of the Lloyd-Max quantizer. Both ALM and LALM quantizers satisfy asymptotic optimality and exponential convergence rate. Simulation performed over smooth signal source distributions validate our mathematical analysis. Experiments for LALM quantizer are implemented on an Android-based edge device, and the proposed quantizer demonstrate improved performance over k-means, in terms of algorithm speedup, energy usage and memory utilization.
Knowledge Management System to Improve The Competence of Aglaonema Farmers

Optimization of digital business penetrates in almost business sectors. Digital Business or online business is becoming increasingly popular in small and medium-sized business in the emergency of Covid-19. This is happened in almost sectors, including the agricultural sector of ornamental plants. During the pan demy, aglaonema production is declining and even their existence in the market are limited. On the other hand, the welfare of aglaonema farmers is still far from prosperous. Therefore, it is necessary to develop an aglaonema farmer information sharing system. This research is aimed to design KMS aglaonema farmers to help farmers share information both knowledge, experience in the form of documents, videos, in acquiring and sharing knowledge related to cultivation and marketing Aglaonema. So that the competence of farmers can be achieved. The system development used RAD method with Object Oriented. The data collection method was conducted using interviews and observations in Depok and Bogor districts of West Java. The result of this research is the establishment of knowledge management system aglaonema farmer. Conclusion: The design of this KMS can help farmers share information both knowledge, experience in the form of document, videos, to obtain and share knowledge related to aglaonema cultivation and marketing in improving the competence of aglaonema farmers.
Testing Cross-Database Semantic Parsers With Canonical Utterances

The benchmark performance of cross-database semantic parsing has climbed steadily in recent years, catalyzed by the wide adoption of pre-trained language models.Yet existing work have shown that state-of-the-art crossdatabase semantic parsers struggle to generalize to novel user utterances, databases and query structures.To obtain transparent details on the strengths and limitation of these models, we propose a diagnostic testing approach based on controlled synthesis of canonical natural language and SQL pairs.Inspired by the CheckList (Ribeiro et al., 2020), we characterize a set of essential capabilities for cross-database semantic parsing models, and detailed the method for synthesizing the corresponding test data.We evaluated a variety of high performing models using the proposed approach, and identified several non-obvious weaknesses across models (e.g.unable to correctly select many columns).
Comparing CNNs and GANs for Image Completion

Imperfections or defects inevitably occur in images due to inexperienced photographers, inadequate methods of preservation, or even some deliberate hacking. Image restoration or completion has been performed using various manual methods in the past, be it being drawn by artists based on their creativity or deleting noise and blur effects using software like Photoshop. On a large scale, manual image completion is infeasible and has quite a lot of limitations. Modern advancements in Computer Vision and Deep Learning have allowed man to automate such tasks with high efficiency. Manual restoration usually relies on prior experience in the subject and sometimes even creativity to reconstruct the image based on the artist's imagination. At the same time, deep learning produces excellent results given enough training data. Deep learning methods can improvise and generalize better too and hence outperform the traditional manual methods. In this project, image completion is performed using 2 Deep Learning models - Convolutional Neural Networks(CNN) and Generative Adversarial Networks(GAN). Adversarial Networks have been proven to be very handy in image to image translation tasks and image reconstruction and hence this is explored widely. Both Deep Convolutional GANs as well as Conditional GANs are used for this task and their respective performances are compared for the above task.
Design &amp; Development of Insurance Money Predictor to Claim with Insurance Company

In this pandemic situation of Covid-19, the whole world had suffered a lot. Whatever issues occurred is very uncertain and unexpected. In the life cycle of a human being, it is always desirable to be safe and have a secure lifestyle in every aspect. To face sudden, unexpected situation insurance is one of the solutions. Nowadays due to increasing awareness among the society people are more inclined towards taking medical insurance from various companies. As we know there are several companies in ensuring the various diseases, climatic hazards, man-made and natural emergencies. Insurance money is going to support the human in morally and financially. The insurance companies are having their own rules and regulations for giving money/claims to a client. Some hidden calculations are always out of reach to the common man. Day by day accurate insurance calculation and claim for money become a challenging task for society. In this paper, a system has been developed using a machine learning algorithm for accurate claim calculation. Implemented system will help the client to claim monthly or quarterly premiums based on various parameters like the number of family members you want to insure, the total income of the family, the expected amount of insurance, age group of family members, etc. To make the process more user-friendly and fast, we have developed a website. The claim will be confirmed after the proper identification and verification of the client.
Methods and Tools for Visualization of Graphs and Graph Algorithms

Graphs are the most common abstract structure encountered in computer science and are widely used for structural information visualization. In the paper, we consider practical and general graph formalism of so called hierarchical graphs and present the Higres and ALVIS systems aimed at supporting of structural information visualization on the base of hierarchical graph models.
Chaos-generalized regression neural network prediction model of mine water inflow

Abstract Artificial neural network (ANN) provides a new way for mine water inflow prediction. However, the effectiveness of prediction using ANN model would not be guaranteed if the influencing factors of water inflow are difficult to quantify or there are only a few observation data. Chaos theory can recover the rich dynamic information hidden in time series. By reconstructing water inflow time series in phase space, the multi-dimensional matrix could be obtained, with each column representing an influencing factor of water inflow and its value representing the change of the influencing factor with time. Therefore, a new prediction model of mine water inflow can be established by combining ANN with chaos theory when lacking data on the influencing factors of water inflow. In the present study, the No. 12 coal mine of Pingdingshan China was selected as the study site. The Chaos-GRNN model and Chaos- BPNN model of mine, water inflow were established by using the water inflow data from February 1976 to December 2013. The model was verified by using the water inflow values in the 24 months from 2014 to 2015. The number embedded dimension ( M ) of influencing factors of water inflow determined by phase space reconstruction was 7, meaning that there were 7 influencing factors of water inflow and 7 neurons in GRNN input layer, and the time delay was 13 months. The value of GRNN input layer neurons was determined accordingly. The maximum Lyapunov index was 0.0530, and the prediction time of GRNN was 19 months. The two models were evaluated by using four evaluation indices ( R , RMSE, MAPE, NSE) and violin plot. It was found that both models can realize the long-term prediction of water inflow, and the prediction effectiveness of Chaos-GRNN model is better than that of Chaos-BPNN model.
Safe planning and deconfliction for multiple UAVs in high density low altitude urban environments

This paper tackles the problem of strategic path planning in high density urban environment. Safe paths with respect to fixed and moving obstacles are defined for a set of UAVs by accounting for navigation and control errors in the definition of the minimum separations. Strategic path definition consists in two steps, i.e., global and local planning, which are aimed first at defining the path to avoid fixed obstacles, and then at refining it accounting for the deconfliction with moving obstacles, respectively. Deconfliction is handled by dealing with time varying occupancy maps where the moving obstacles are represented by their actual trajectory inflated with safety margins and 4D trajectory (position and time) errors. GNSS coverage and UAV on-board equipment, i.e., navigation system performance, impact positioning errors and thus the margins considered in the planning process. Therefore, better equipped UAVs are expected to enable more efficient replanning and airspace utilization. The algorithm is tested in a simulation scenario taken from real world environment and chosen with the aim of highlighting navigation issues at the (low) altitudes where the UAVs are demanded to fly.
Cryptocurrencies Meet Equities: Risk Factors and Asset Pricing Relationships

We consider a variety of cryptocurrency and equity risk factors as potential forces that drive cryptocurrency returns and carry risk premiums. In a cross-section of 2,000 biggest cryptocurrencies, only downside market risk, cryptocurrency size and policy uncertainty factors are systematically priced with significant premiums. Momentum premium vanished in the recent years. Equity market risk appears to be more important than cryptocurrency market risk, suggesting greater linkages between cryptocurrency and equity markets than we used to think.
Neural Architecture Search Based on Tabu Search and Evolutionary Algorithm

Most existing optimization methods for neural architecture search (NAS), including evolutionary algorithms, reinforcement learning and gradient-based approaches, have not employed memory strategies explicitly, which may lack of efficiency when searching neural architectures. To solve this issue, we propose a new NAS approach by using an evolutionary algorithm which employs a tabu mechanism to help to improve the search efficiency. To be more specific, the individuals of parent population are selected by tournament selection and tabu list. The tournament selection select parent population according to the accuracy of each individual. And the tabu mechanism builds a tabu list to record the chosen operations in the last previous search process, which employs a search memory mechanism to improve the efficiency explicitly. To confirm the superior performance of our approach, a well-designed surrogate model is used to accelerate the process of performance evaluation on CIFAR-10. The comprehensive experimental results show that the proposed method can reach to 2.48% error rate with about 2 GPU days, which demonstrates the superiority of the suggested method.