Reviewerly/exHarmony huggingface dataset:
Mathematical Foundations of Hypergraph

Abstract In this chapter, we introduce the mathematical foundations of hypergraph and present the mathematical notations that are used to facilitate deep understanding and analysis of hypergraph structure. A hypergraph is composed of a set of vertices and hyperedges, and it is a generalization of a graph, where a weighted hypergraph quantifies the relative importance of hyperedges or vertices. Hypergraph can also be divided into two main categories, i.e., the undirected hypergraph representation and the directed hypergraph representation. The latter one further divides the vertices in one hyperedge into the source vertex set and the target vertex set to model more complex correlations. Additionally, we discuss the relationship between hypergraph and graph from the perspective of structural transformation and expressive ability. The most intuitive difference between a simple graph and a hypergraph can be observed in the size of order and expression of adjacency. A hypergraph can be converted into a simple graph using clique expansion, star expansion, and line expansion. Moreover, the proof based on random walks and Markov chains establishes the relationship between hypergraphs with edge-independent vertex weights and weighted graphs.
Excitation of Semiconductor Nanosystems by Multy-Frequency Quantum Field

Abstract The formation of Frenkel excitons in a 2D semiconductor nanosystem induced by frequency-multimode non-classical electromagnetic field is investigated. Strong sensitivity of excitation dynamics to the initial states of quantum field modes is found. The possibility to manage the excitation dynamics and controllably enhance a certain excitation channel by varying field frequency detuning and by choosing a proper initial state of the non-classical multimode field is demonstrated. The specific features of the excitation arising due to efficient coupling and energy exchange between initially independent field frequency modes are demonstrated. The polarization response of the considered nanosystem on the multi-frequency field impact is studied and its peculiarities arising due to the quantum features of the affecting non-classical light are found.
Hypergraph Computation Paradigms

Abstract This chapter introduces three hypergraph computation paradigms, including intra-hypergraph computation, inter-hypergraph computation, and hypergraph structure computation. Intra-hypergraph computation representation aims to conduct representation learning of a hypergraph, where each subject is represented by a hypergraph of its components. Inter-hypergraph computation is to conduct representation learning of vertices in the hypergraph, where each subject is a vertex in the hypergraph. Hypergraph structure computation is to conduct hypergraph structure prediction, which aims to find the connections among vertices. This chapter is a general introduction of hypergraph computation paradigms to show how to formulate the task in the hypergraph computation framework.
Neural Networks on Hypergraph

Abstract With the development of deep learning on high-order correlations, hypergraph neural networks have received much attention in recent years. Generally, the neural networks on hypergraph can be divided into two categories, including the spectral-based methods and the spatial-based methods. For the spectral-based methods, the convolution operation is formulated in the spectral domain of graph, and we introduce the typical spectral-based methods, including hypergraph neural networks (HGNN), hypergraph convolution with attention (Hyper-Atten), and hyperbolic hypergraph neural network (HHGNN), which extend hypergraph computation to hyperbolic spaces beyond the Euclidean space. For the spatial-based methods, the convolution operation is defined in groups of spatially close vertices. We then present spatial-based hypergraph neural networks of the general hypergraph neural networks (HGNN+) and the dynamic hypergraph neural networks (DHGNN). Additionally, there are several convolution methods that attempt to reduce the hypergraph structure to the graph structure, so that the existing graph convolution methods can be directly deployed. Lastly, we analyze the association and comparison between hypergraph and graph in the two areas described above (spectral-based, spatial-based), further demonstrating the ability and advantages of hypergraph on constructing and computing higher-order correlations in the data.
End-to-End Traffic Monitoring and Management in Real-Time Road Traffic

The detection of vehicles and pedestrians on the road is one of the most challenging problems in object detection and autonomous vehicles. This paper reports a novel intelligent traffic monitoring and management system using You Only Look Once (YOLO) and OpenCV tracker. A new database named 'Kannur University Vehicle Database (KNUVDB)’ is created and used for the purpose of studying vehicle detection., in addition to the available datasets in literature. We focus on counting the vehicles after they have been detected and tracked. Later., performs the traffic update by using the vehicle count. The proposed method provides better detection accuracy on the real-time traffic video dataset available in the literatures and also on the KNUVDB dataset. Experimental studies have shown that Discriminative Correlation Filter with Channel and Spatial Reliability (CSRT) and Kernelized Correlation Filter (KCF) provide better performance than other OpenCV trackers. In KNUVDB dataset., YOLO-CSRT gives 100% accuracy and YOLO-KCF provides 90.90% accuracy in vehicle detection., tracking and counting. In real-time road traffic video dataset., YOLO-CSRT provides 100% accuracy and YOLO-KCF provides 93.70 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> accuracy in vehicle detection., tracking., and counting accuracy.
Hypergraph Structure Evolution

Abstract In practice, noise exists in the process of data collection and hypergraph construction. Therefore, missing, abundant, and noisy connections may be introduced into the generated hypergraph structure, which may lead to inaccurate inference on hypergraph. Another issue comes from the increasing data stream, which is also very common in many applications. It is important to consider the structure evolution methods on the hypergraph, which optimize the hypergraph structure accordingly. Early hypergraph computation methods mainly rely on static hypergraph structure, which may suffer from the limitation of the static mechanism when confronting random and increasing data scenarios. In this chapter, we introduce dynamic hypergraph structure evolution methods, including both hypergraph component optimization and hypergraph structure optimization. Finally, we briefly introduce the incremental learning method on growing data.
The DeepHypergraph Library

Abstract This chapter introduces the DeepHypergraph library, which bridges the hypergraph theory and hypergraph applications. This library provides the generation of multiple low-order structures (such as graph and directed graph), high-order structures (such as hypergraph and directed hypergraph), datasets, operations, learning methods, visualizations, etc. We first introduce the design motivation and the overall architecture of the library. Then, we introduce the “correlation structure” and “function library” of the Deephypergraph library, respectively.
Identity Management with Blockchain : Indian Migrant Workers Prospective

The agricultural sector and other Micro, Small, and Medium Enterprises in India operate with more than 90% migrant workers searching for better employment opportunities far away from their native places. However, inherent challenges are far more for the migrant workers, most prominently their Identity. To the best of our knowledge, available literature lacks a comprehensive study on identity management components for user privacy and data protection mechanisms in identity management architecture. Self-Sovereign Identity is regarded as a new evolution in digital identity management systems. Blockchain technology and distributed ledgers bring us closer to realizing an ideal Self-Sovereign Identity system. This paper proposes a novel solution to address identity issues being faced by migrant workers. It also gives a holistic, coherent, and mutually beneficial Identity Management Solution for the migrant workforce in the Indian perspective towards e-Governance and Digital India.
Optimal Phase Sensitivity in an Unbalanced Mach-Zehnder Interferometer

Abstract In this work we address the problem of phase sensitivity optimization for an unbalanced Mach-Zehnder interferometer. While the quantum Fisher information can be employed in order to obtain the optimum transmission coefficient for the first beam splitter, this is no longer true for the second one, whose optimization is detection-scheme dependent. We thus consider three commonly used detection schemes and provide the optimal solution for each case. We also provide applications of the optimization method showing that for some input states a non-balanced Mach-Zehnder interferometer can outperform its balanced counterpart in terms of phase sensitivity.
A Non-Content based Optimized Approach for Image Spam Detection

Image spam is a form of email spam that gained popularity in the last decade. Spammers have introduced the technique of image spam to bypass the traditional text-based spam filters. In this research, two novel techniques for image spam detection have been proposed and developed that are efficient as well have low computational complexity. The proposed techniques are non-content based. The first technique extracts low-level and high-level image properties and then feed these features into Random Forest classifier to perform classification. The second technique uses a Convolutional Neural Network where the raw image is directly fed into the CNN model to perform the classification between spam and non-spam images. This approach will remove the need for manual feature extraction thus reduce computational complexity. A comparative analysis is then performed between the two approaches.
Perception Analysis of Travelers Regarding India's Medical and Spiritual Tourism

Twitter is the most frequently used platform with an open network, and therefore, travelers often turn to Twitter to share their travel experiences, satisfaction as well as discontent and other perspectives. This research offers a framework and methodology for analyzing public emotions on Twitter for tourist insights, as well as real-time dataset having insights of India's medical and spiritual tourism along with Indian cultural and natural destination locations, extracted using the Twitter API. The results are analyzed using sentiment polarity analysis, exploratory data analysis, and data visualization in Python.
Hypergraph Computation for Computer Vision

Abstract In this chapter, the applications of hypergraph computation in computer vision are introduced. Computer vision is one of the most widely used areas of hypergraph computation. The hypergraphs can be constructed by modeling the high-order relationship among inter- or intra-visual samples, and then computer vision tasks can be solved by hypergraph computation procedures. More specifically, four typical applications, including visual classification, 3D object retrieval, and tag-based social image retrieval, are provided, in which hypergraphs are used to model high-order relationship among samples and solve visual problems by hypergraph computation. For example, in social image retrieval, hypergraphs are used to model the high-order relationship among social images based on both visual and textual information, which is the high-order modeling of elements within samples.
Large Scale Hypergraph Computation

Abstract As introduced in the previous chapters, the complexity of hypergraph computation is relatively high. In practical applications, the hypergraph may not be in a small scale, where we often encounter the scenario that the size of the hypergraph is very large. Therefore, hypergraph computation confronts the complexity issues in many applications. Therefore, how to handle large scale data is an important task. In this chapter, we discuss the computation methods for large scale hypergraphs and their applications. Two types of hypergraph computation methods are provided to handle large scale data, namely the factorization-based hypergraph reduction method and hierarchical hypergraph learning method. In the factorization-based hypergraph reduction method, the large scale hypergraph incidence matrix is reduced to two low-dimensional matrices. The computing procedures are conducted with the reduced matrices. This method can support the hypergraph computation with more than 10,000 vertices and hyperedges. On the other hand, the hierarchical hypergraph learning method splits all samples as some sub-hypergraphs and merges the results obtained from each sub-hypergraph computation. This method can support hypergraph computation with millions of vertices and hyperedges.
In Real Time Face Mask Detection Alert System using CNN Model

Today's current scenario of the coronavirus pandemic (Covid19), where in the future there will be a need for efficient applications of real-time mask detection. Because, nowadays it is very difficult for doctors to handle patients infected with corona virus. Our major purpose of building a face-mask detection alert system using OpenCV that can detect individual person's if he/she is wearing a face mask or not wearing a face-mask using CCTV Camera, with quite a good accuracy. And also building and training the Convolutional Neural Network (CNN) using keras framework. After that, He / She refused to go to the locations or the regions wherever the officials were strictly asked to wear face-mask. After denying way in to the individual, the officers or the authorized person will receive an email in real time where the photograph of the person can be attached. In away screen panels could be installed at the entrances where the person's denied access can see a pop-up warning message. Where he/she would be advised to wear a face mask before getting access. This type of face mask detection alert system has some applications in schools, colleges, malls, theaters, offices and also other major crowded places or areas where it expects large public gathering.
Enhanced Hybrid Symmetric Cryptography for IoT Devices

IoT has a lot of applications in various domains. The uses of IoT gadgets include different fields from more limited sizes to bigger ones, for example, from keen Gird to keen City. The security and protection in the Internet of things (IoT) has stayed a genuine worry because of the heterogeneous idea of enormous scope of gadgets and its weaknesses in the working environments. IoT gadgets normally run on less computational assets and restricted battery power. Therefore typically top of the line algorithms like TLS/SSL and RSA, which are broadly utilized for security purposes can't be executed. This is primarily because these standard encryption techniques are computationally expensive and not attainable with regards to financial parts of IoT. This paper portrays a novel hybrid encryption technique. The combination of symmetric and asymmetric encryption is applied here to ensure maximum efficiency and security. Moreover, randomness of both the techniques are immensely enhanced with the assistance of a matrix which each time produces another key for encryption and decryption, making it totally immune to brute-force attacks.
Efficient Modulation Format Identification Using Transfer Learning

An efficient modulation format identification (MFI) at an optical signal-to-noises ratios (OSNRs) spanning from 20 to 30 dB is proposed using the transfer learning (TL) technique. Transmission setup are created to demonstrate the technique for 8QAM, 16QAM, 64QAM, and 128QAM systems. TL can process constellation diagrams from an image processing approach owing to its self-learning capabilities. The obtained research shows that even at low OSNR, the suggested techniques may accurately be utilized to detect the modulation format with classification rates up to 100%The suggested method can intelligently analyse the basic hardware to allow MFI, and the analysis results are used to identify additional modulation schemes at various transmission rates for better management of optical systems.
Hypergraph Computation for Social Media Analysis

Abstract Social media, such as Twitter and Weibo, have grown rapidly over the past decade. Large numbers of active social media users produce a voluminous amount of data each day, from which important insights can be drawn. Several applications, such as recommender system and sentiment analysis, have been developed to help study the users’ intension and portrait. One common challenge faced by these social media applications is how to leverage the complex and multi-modal data on social networks and model the higher-order associations hidden in the data. Hypergraph computation has the huge potential to be used in such analysis. In this chapter, we introduce three typical applications of hypergraph computation, i.e., recommender system, sentiment analysis, and emotion recognition, from which hypergraph computation has shown great value on social media analysis.
An Empirical Study on Continuous Feature Delivery and Conventional Methods

Organizations engaged in development and shipment of software solutions constantly look for deployment models that generate minimum wastage, provide quick response to customer requirements, and resolve issues with minimal response time. Hence this has motivated organizations to move to cloud deployment models for consuming software solutions and to operate intelligently. With cloud adaption there is a necessity to use latest project management methodologies that aid to deliver new features in shorter periods with minimal disruption to customer business processes. In this paper an evaluation is carried out with conventional project management methodologies to realize the pros and cons when intelligent enterprises migrate to cloud deployment strategy. Later a methodology based on continuous feature delivery (CFD) is proposed and compared with conventional methodologies by considering various performance indicators like execution time, efforts, automations, and number of features delivered.
Learning a crowd-powered perceptual distance metric for facial blendshapes

Abstract It is known that purely geometric distance metrics cannot reflect the human perception of facial expressions. A novel perceptually based distance metric designed for 3D facial blendshape models is proposed in this paper. To develop this metric, comparative evaluations of facial expressions were collected from a crowdsourcing experiment. Then, the weights of a distance metric, based on descriptive features of the models, were optimized to match the results with crowdsourced data, through a metric learning process. The method incorporates perceptual properties such as curvature and visual saliency. A formal analysis of the results proves the high correlation between the metric output and human perception. The effectiveness and success of the proposed metric were also compared to other distance alternatives. The proposed metric will enable intelligent processing of 3D facial blendshapes data in several ways. It will be possible to generate perceptually valid clustering and visualization of 3D facial blendshapes. It will help reduce storage and computational requirements by removing redundant expressions that are perceptually identical from the overall dataset. It can also be used to assist novice animators while creating plausible and expressive facial animations.
Python Based Smart Trial Room

Other material endeavors hit during these challenging intervals, as the overwhelming majority of things moved to virtual mode and challengers attempted to advance beyond one. Every small material shop can assist their customers with an internet-based trial room with a virtual trial room without danger of contracting the illness. The advantage of using this method is that it saves time and effort to evaluate the clothing accurately. Everything, from food to apparel, is now available online. Instead of travelling to shopping malls and textile stores, where they have to try on items in a trial room to see if they fit correctly and how they appear on themselves, they now go to the internet. Furthermore, most malls have a limited number of trial rooms that are overcrowded. As a result, people have to wait a long time, which is inconvenient. Trial rooms are not so secure that no one can be certain that hidden cameras are not present. As a result, using these online buying websites is extremely safe. People can simply choose from a variety of outfits. On the other hand, people do not know how a piece of clothing will appear on them until it is brought to them. People can try on their items by just selecting the goods and seeing how it appears on them. As a result, this article uses python to create a “Smart Trial Room” that allows users to try on garments virtually. This “Smart Trial Room” project comprises a homepage with several clothing-related products comparable to an e-commerce website. HTML and CSS were used to create the website. Users can choose a product they like, and it will launch a webcam that will show them how the thing looks on them. Multiple people can also try the product at the same time. The major goal of this experiment is to show that greater interaction features in clothes websites can increase online sales.
Implementation of Proof-of-Work using Ganache

One of the most essential characteristics of Blockchain is the ability to validate the integrity of new transactions added to the blockchain. And one of the most essential consensus algorithms, Proof of Work, performs this job. In this work, we implemented the Proof of Work consensus method on the block formed by performing the transaction using Ganache. The primary goal of this implementation is to understand the process and record how Proof of Work works in reality on newly created blocks.