Reviewerly/exHarmony huggingface dataset:
Preliminary Critical Examination of the Range of Web-Enabled Gadgets in Modern Workplace Environments

The Net of Objects is an interconnected network of stuff, organizations, data, and persons that allows for more useful and pertinent relationships than ever before. Every facet of the economy and society will be linked to a Internet of Things network through software and sensors in this system, and the resultant data will continually flow into big data at every node. This paper explores the Network of Things using a reworked theory of the subject, looking at the years 1985 to 2015 in order to gain a more thorough building. Its objective is to recognize the growth avenues the Internet of Things presents to the capitalist model and discover.Graphics: Combining a descriptive method with a single methodology to the body of existing material was the Internet of Everything.In order to further the investigation, we looked at both domestic and foreign literature. Then, we ran four specific phrases through two data sets (Internet of Things, Energy Internet, Communication systems Internet, and Transportation Online world) to find a wide range of academic supplement the systematic review. The study was conducted using reputable information.Novelty: The Iot can create a worldwide network that connects everyone and everything. Such network, which measures and monitors hitherto immeasurable traits, offers economy and advantages for both the person and the social environment. Therefore, the examination of the review article on the Web of Things is a means to completely understand the features, prospects, prospective implementations, dangers, and concerns associated with the subject.
Text-based Fine-Grained Emotion Prediction

Text-based emotion prediction is an important task in the field of affective computing. Most prior work has been restricted to predicting emotions corresponding to a few high-level emotion classes. This paper explores and experiments with various techniques for fine-grained (27 classes) emotion prediction <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> which appeared at ACII 2021. In particular, (1) we present a method to incorporate multiple annotations from different raters, (2) we analyze the model's performance on fused emotion classes and with sub-sampled training data, (3) we present a method to leverage the correlations among the emotion categories, and (4) we propose a new framework for text-based fine-grained emotion prediction through emotion definition modeling. The emotion definition-based model outperforms the existing state-of-the-art for fine-grained emotion dataset GoEmotions. The approach involves a multi-task learning framework that models definitions of emotions as an auxiliary task while being trained on the primary task of emotion prediction. We model definitions using masked language modeling and class definition prediction tasks. We show that this trained model can be used for transfer learning on other benchmark datasets in emotion prediction with varying emotion label sets, domains, and sizes. The proposed models outperform the baselines on transfer learning experiments demonstrating the model's generalization capability.
Tucker Bilinear Attention Network for Multi-scale Remote Sensing Object Detection

Object detection on very high resolution (VHR) remote sensing images plays an indispensable role in many applications. However, the large-scale target variation of remote sensing presents a significant challenge to high-precision VHR remote sensing target detection. Although existing methods attempt to enhance the feature pyramid structure and utilize various attention modules to improve the accuracy of high-resolution remote sensing object detection, small objects can still be overlooked due to the loss of critical detail features. There remain ample opportunities for improvement in multi-scale feature fusion and balance. To address this issue, this paper proposes two novel cross-layer modules: Guided Attention and Tucker Bilinear Attention. The former effectively preserves crucial detailed features, while the latter further enhances features by reasoning about semantic-level correlations. Based on these two modules, a new multi-scale remote sensing object detection framework is introduced. Compared to state-of-the-art methods on DOTA, DIOR, and NWPU VHR-10 datasets, the framework achieves comprehensive average detection accuracy for full-scale objects, and significantly enhances the detection accuracy for small objects. Code and models are available at https://github.com/Shinichict/GTNet.
A Robot Navigation System in Complex Terrain Based on Statistical Features of Point Clouds

The robot navigation system is mature in 2D flat terrain, however, in 3D complex terrain scenarios the computational burden increases dramatically because an additional dimension is added to the mapping and path planning problems. To this end, a hierarchical controlled robot navigation system in complex terrain (HCRNS-CT) is proposed for mapping and path planning by using a 2D grid map based on statistical features of the height of point clouds to replace the ordinary 3D map and implemented in a mobile platform in this paper. In HCRNS-CT, a 2D grid map that can reflect the curve of the ground is proposed from the viewpoint of statistical features. These point cloud features from the depth camera can be used to represent the traversability of each grid, even if the grid size is large, and thus can reduce the time consumption on mapping and path planning dramatically. And Kalman filter is used to update the global map with these features by local point clouds perceived by the depth camera. In addition, to match the introduced map, we propose a path planning algorithm fully utilizing those features and considering the mechanical structure of the robot. In HCRNS-CT, a hierarchical control structure is adopted as its backbone to reduce the frequency existing in the high computational burden module, and thus maintains the real-time standard of control. Therefore, HCRNS-CT can handle slopes and obstacles effectively in complex environments. A series of real-world experiments validate the robustness and effectiveness of HCRNS-CT.
Power BI Licensing

There are many ways to license Power BI. Users often do not understand which features are included in which licensing plans. In this chapter, you learn about all the different licensing plans for Power BI, the scenarios in which to use the licensing, and scenarios in which you may need to change your chosen licensing plan. This chapter is intended to help you to find the most cost-effective licensing plan for your requirements. You learn about the following licensing options:
Comparison of Sharing Methods for Power BI

You have published your Power BI report and want to share it with others. You can share it using various methods—basic sharing, workspaces, apps, Publish to Web, Power BI Embedded, Secure Embed, and SharePoint Online. The wide range of sharing methods makes it confusing to choose the best one. It is very important to determine which method is best before sharing your content with users. In this chapter, you learn about all the different methods of sharing, the pros and cons of each, and scenarios in which to use each method. By the end of this chapter, you'll be able to choose the best sharing mechanism for sharing your Power BI reports.
Paginated Reports

If you want to print a Power BI report and have a tabular visual in your report (such as a table or matrix), your options are limited. Fortunately, the Power BI Paginated Report feature can help with that. This chapter explains the paginated report and the differences between it and a normal Power BI report, plus situations that you need to use each.
An Event Analysis of Bitcoin Based on a Novel DRE Methods

Since Bitcoin came into the world, modelling and analyzing the underlying characteristics of Bitcoin has attracted increasing attention.This paper uses a framework including decomposition, reconstruction and extraction method (DRE) to analyze price fluctuations based on ultra-high-frequency data from Dec.1, 2019, to Nov.30, 2021.First, the ensemble mode decomposition (EMD) is employed to decompose the Bitcoin hourly spot price into 13 intrinsic mode functions (IMF) plus a residual.Second, the IMFs are reconstructed into high-frequency components, low-frequency components and a trend based on fine-to-coarse reconstruction.Furthermore, the intraday volatility analysis based on LM test is applied on 15-minutes frequency data to detect discontinuous jump arrivals and extract jump from realized quadratic variation.Empirical results show that three components of reconstruction can be identified as short term fluctuations process caused by microstructure noise, the shocks affected by major events, and a long-term trend based on inelastic supply and rigid demand.We find that approximately 40% of jumps can be matched with the news from the public news database (Factiva), and the jump sizes are larger than that of stock markets.This finding indicates that the Bitcoin market has more irregularly noise and unforeseen shocks from unscheduled events.
Face Recognition Authentication System with CNN and Blink Detection Algorithm

Authentication is a process that verifies a user’s identity by recognising the user's credential to see whether it matches the credential in the database of the information system. It is one of the most widely used methods for protecting sensitive information stored in modern computer systems from theft by unauthorised users such as cybercriminals. Facial recognition technology is perceived as a world-changing technology that can benefit society with its convenient, non-intrusive characteristics. Concerns about the security and privacy issues associated with facial recognition technology arise as a result of the large number of face images available through social media platforms. Facial recognition technology encounters challenges when intruders attempt to spoof the system by using photos or videos that contain the user’s face. This project proposes a Python-based facial authentication system that is capable of locating and extracting the user's face from images captured by a camera using a histogram of oriented gradient-based feature descriptors and a 68-point feature landmark predictor to detect blink action with the Dlib library. The face images extracted are fed into a convolutional neural network developed using Pytorch, which verifies the identity of a given person from a given face image and produces an authentication result. To gain access, the users must present their frontal faces and blink to prevent spoofing attacks by using photos or videos. The testing result of the authentication system shows significant recognition accuracy with a robust authentication process.
A Comparative Study Between Various Machine-Learning Algorithms Implemented for the Proper Detection of Fraudulent and Non-fraudulent Transactions Through Credit Card

E-commerce has dramatically increased the usage of credit cards for online and offline purchases. A considerable proliferation of transactions done through credit cards has led to a substantial and consequential surge in fraud. To combat the highly imbalanced dataset, we have performed oversampling on the dataset. This paper demonstrates various machine-learning algorithms for credit card fraud detection in this paper. We have also provided a detailed comparison between the performance of four algorithms. The algorithms used here in this paper were LightGBM, DNN, XG Boost, Random Forest, ANN and finally LSTM. As a result of the comparison, it is observed that LightGBM has the lowest accuracy score. So, we implemented other algorithms to improve our accuracy score. After a detailed comparison between six algorithms, it is observed from the graph that LSTM resulted in maximum accuracy as compared to other algorithms. It is observed that after running 30 epochs, LSTM resulted in a 0.9373 accuracy score. The performance of LSTM increases as we run it with more epochs, resulting in maximum accuracy.
Transfer Learning for Computational Chaos System Based on Pool

Can a neural network trained by the time series of system A be used to predict the evolution of system B? This question, known as transfer learning, has great significance in machine learning and data mining, but has not been solved in chaotic systems. This paper studies the transfer learning of chaotic systems from the perspective of synchronization theory, that is, using the reservoir computer trained by the periodic system A to infer the unmeasured variables of the chaotic system B, and the reservoir computer trained by the chaotic system A to infer the chaotic system B. unmeasured variable. And system A is different from chaotic system B in parameters and dynamics. The study found that if the parameters of system A and system B are different, the pool calculation can be well synchronized with system B. In this paper, the transfer learning of logistic mapping, henon mapping and Lorentz equation is used to study the information transfer on the computational coupling chain of the storage pool. Finally, the four-dimensional power system experiment shows that the information learned from the modeling system can be used for prediction Evolution of experimental systems.
An Automatic Segmentation Fitting Method for Freehand Sketches Based on Greedy Strategy

In this paper, we propose an automatic segmentation and fitting method for hand-drawn sketches based on greedy strategy. This method first resamples and smoothly filter the input sketch stroke sequence, and then automatically segments the hand-drawn sketches based on the greedy strategy. The maximum Euclidean distance is found as the segmentation point in the sketch strokes before each iteration starts, the proximity error of sub-strokes between adjacent segmentation points is calculated, the parameters of sub-strokes whose proximity error less than the threshold are stored, the sub-strokes whose proximity error great than the threshold are recorded to be repartitioned in next iteration processing until proximity error of all the sub-strokes less than the threshold. The least squares method is used to fit the sub-strokes one by one, and removes the redundant segmentation points by relying on error control to maximize the retention of the original information of the hand-drawn sketches while obtaining the canonical geometric This provides the basis for recognition, semantic understanding and retrieval from sketches to normative design drawings. The experiments show that the method has good segmentation fitting effect for all kinds of figures, and the fitting residuals are controllable.
Research on Feature Space Migration Fault Diagnosis for Missing Data Signals

The phenomenon of data deletion generated by signals based on occasional occurrences in actual transmission changes the signal structure, which is aimed at the change of data missing signal structure and the loss of data volume. This paper proposes a fault diagnosis method for integrated multidimensional feature migration in Broad-Tree. Firstly, the features are extracted from multiple dimensions based on the sample structure, and the feature subspace facing the target domain is constructed by removing redundant features. Secondly, the multidimensional feature space in the Bald-Tree integrated classification model is reduced dimensional increment, based on the sampling diagnosis of the subclassifier, and the integrated classification layer is used to make integrated decisions for sub-classification, and finally the diagnosis of the target domain is realized based on the supervised sample parameter fine-tuning, and the open data set experiment proves that the method can effectively diagnose the fault of the missing signal of the data, and greatly improves the operation speed and diagnostic accuracy while greatly reducing the training data set.
Topological Analysis of Vector-Field Guided Path Following on Manifolds

A path-following control algorithm enables a system’s trajectories under its guidance to converge to and evolve along a given geometric desired path. There exist various such algorithms, but many of them can only guarantee local convergence to the desired path in its neighborhood.
Research on Micromouse Maze Search Algorithm Based on Centripetal Algorithm

Aiming at the problem that the micromouse guided by the centripetal search algorithm has reached the maze lattice adjacent to the target area but cannot immediately enter the target area, an improved centripetal search algorithm is proposed. The maze is modeled in two dimensions, and the micromouse’s operation, coordinate memory, and wall data storage are realized through the conversion of relative direction and absolute direction. Based on the centripetal search algorithm, the eight adjacent maze grids in the target area are filled with local algorithms. The test results of 10 mazes show that compared with the previous algorithm, the improved algorithm has reduced the number of search steps and increased the stability of the micromouse search maze. When the micromouse is located in the adjacent grid with access to the target area of the maze, 100% directly enters the target area. It can be seen that unnecessary search is reduced during the maze search, and it’s an efficient maze fusion search algorithm.
Electronic Voting Machine as a Service on the Cloud—Azure for EVM (A4EVM)

Electronic voting machine (EVM) is a well-known device used to record votes. In India, world largest democracy, EVM is extensively used in all the elections. This mode of election is preferred over ballot paper due to many reasons. The main reason is to discourage the use of paper to save the environment. Another reason is to reduce electoral fraud and abuse. An ideal EVM is expected to be accurate and secure. An EVM should not have any networking capability, like Bluetooth connectivity, or any type of wireless connectivity, to make sure that the data (saved votes) stored within EVM cannot be accessed, altered, or modified. In recent years, there are various questions arises on its credibility, because data is recorded locally within the EVM, it can be manipulated easily. However, the M3 version of the EVMs includes the Voter Verifiable Paper Audit Trail (VVPAT) system for cross-verification. In recent elections, it has been noticed that election is delayed due to faulty EVMs. To solve all the above issues, we have proposed an advanced solution, based on Microsoft Azure cloud service. In this paper, we are proposing EVM as a service on Azure-based client–server architecture, where the machine will act as a client, the data will be recorded on Azure server. The proposed solution is secure, cost-effective, and robust due to the security guaranteed from Microsoft. Other advantages include, the counting can be done automatically using Azure services, result can be published very next day, data can be dumped permanently in Azure data lake (ADLS) for future use, various analyses can be done using machine learning and analytical services like power BI. Last but not least, EVM is free immediately after the election.
Using Smart Glasses for Monitoring Cyber Threat Intelligence Feeds in a Multitasking Environment

The surge of COVID-19 has introduced a new threat surface as malevolent actors are trying to benefit from the pandemic. Because of this, new information sources and visualization tools about COVID-19 have been introduced into the workflow of frontline practitioners. As a result, analysts are increasingly required to shift their focus between different visual displays to monitor pandemic-related data, security threats, and incidents. Augmented reality (AR) smart glasses can overlay digital data to the physical environment in a comprehensible manner. However, the real-life use situations are often complex and require fast knowledge acquisition from multiple sources. In this study, we report results from a pilot and a trial where we used an overlaid AR information interface coupled with traditional computer monitors. Both the pilot and the study had six test subjects. Smart glasses and their applications often have a limited amount of adjustments for individual differences in physiology, which may influence task performance in fast-paced situations. Our goal was to evaluate a multi-tasking setup with traditional monitors and an AR headset where notifications from the new COVID-19 Malware Information Sharing Platform (MISP) instance were visualized along with some interactability. Our results indicate some gender differences in some aspects of situational awareness. Some parts of the task load index also showed correlation with certain aspects of situational awareness. While our experiments were small scale, in this study, we showed that AR has the potential to improve situational awareness and task performance in monitoring tasks.
Face Recognition with Mask Using CNN, LBP, and Fuzzy Techniques

People all across the world are now compelled to wear facial covers in order to prevent the spread of the COVID illness 2019 (COVID-19). Because the majority of the important facial features, including the nose, mouth, and other prominent features, are covered by the facial covering, it is now impossible for the standard face recognition systems used for security purposes to perceive the same thing. The method described in this research uses deep learning, local binary pattern (LBP) features, and fuzzy algorithms to recognize the face with mask. RetinaFace, a hybrid self-supervised and extra-supervised multi-task learning face detector, is employed as a speedy yet effective encoder. Combining features from RetinaFace with local binary pattern features taken from the masked face’s eye, forehead, and eyebrow regions results in a unified structure for recognizing the faces with mask. Additionally, we obtained 300 individuals for the COMASK20 data set. We ran an experiment where we compared our recommended algorithm with numerous facial identification methods using the publicly available Essex data set and COMASK20 data set.
Hybrid Binary Dragonfly Algorithm with Grey Wolf Optimization for Feature Selection

The curse of dimensionality produced by the fast proliferation of information science will have a detrimental impact on the machine learning model's performance. In the data mining process, feature selection is often thought of as a preprocessing technique for finding an optimal subset of features from a given collection of all features. Grey Wolf Optimizer (GWO) suffers with the problem of global optimal solution and Dragonfly Algorithm (DA) suffers from local optima. To combine the benefits of these two algorithms, an enhanced New Levy Updated Dragonfly Algorithm (NL-DA) and Grey Wolf Updated Dragonfly Algorithm (GWU-DA) are developed in this article as a wrapper-based strategy for finding the best subset of features. An improvement is made in the Levy Flight Mechanism (LFM), food and predator to improve the algorithm's randomisation, probabilistic behaviour and searching behaviour. The fitness function of NL-DA and GWU-DA is evaluated with a hybrid classifier, such as Support Vector Machine (SVM) and Deep Belief Network (DBN). The suggested NL-DA is compared to three existing algorithms and GWU-DA is compared to nine existing algorithms over eighteen datasets from the University of California, Irvine (UCI). Various evaluation metrics are utilised to assess how well the proposed algorithms perform. According to the results, enhanced NL-DA and GWU-DA are superior in terms of enhancing prediction accuracy and lowering the number of selected features.