Reviewerly/exHarmony huggingface dataset:
A Study of Modular Association Relation Based on Association Relation Mining

With the continuous accumulation of knowledge and the rapid development of science, modularization, as an advanced form of generalization, serialization and combination, is a structural mode especially suitable for complex products. It makes the products have the characteristics of rapid derivation and upgrading, high reliability and economy, and continuous pursuit of high performance, and can realize the rapid development of products. However, it can only represent the surface correlation existing in the product production process, and a large number of implicit correlation can not be visualized effectively. In order to solve the above problems, association rule mining technology is used to mine hidden association relations, so as to enrich the connotation of association relations in modular design. Firstly, the basic concept of modular design is briefly summarized; Then it analyzes the development status and existing problems of modular design in association relationship, expounds the application of association rule mining method in modular design in order to solve the existing problems, and finally summarizes the development trend of association relationship in modular design in the future.
Enhancement of Image Forgery and Improvement of Image Parameters Using DWT Algorithm

Choosing a reliable and effective fraudulent method of determining counterfeit coefficients is the key to image fraud. This document sets out a new algorithm based on discrete wavelet transform (DWT) and Eigen operators from the point of view of the acquisition. First, create many sculptural images using DWT and PCA, then get detailed, vertical and diagonal edge details by finding the edges of the lower parts and the higher frequency. Thereafter, comparing the pixel strength of each pixel and the confirmation of consistency to accurately determine the points of the edge and ensure the clarity of the forgery image.
Security Analysis and Security Model Design of Smart Home System

With the advent of the Internet of things era, Internet of things application systems based on sensor technology and RFID technology have appeared. This paper analyzes the security issues of information security and communication authentication in smart home system, a topic oriented security model is proposed and designed, Improving the reliability and stability of smart home system. The perfect experience and satisfaction of users using smart home.
Determining Objects Surface and Its Characteristics by Mathematical Approach

This study presents a mathematic algorithm for determining position of the model in three dimensional surface. The work is important for robotic vision, where the role of robotic increases in our life and industry. We showed that the technique can detect positions and surface of the model well. Details of the technique and discussions are presented in this study. The results are shown for a various shape, which often observe in daily life. The similar level for the numerical program and real model is around 75%.
Application of Video Teaching Platform Based on Internet in Guzheng Teaching in Information Age

As an ancient musical instrument loved by people, the teaching and practice of Guzheng are paid more and more attention. It is of great significance to develop students’ personality, expand their knowledge structure, improve their comprehensive quality and activate campus culture. This paper discusses the development opportunity of Guzheng art in the network era and the significance of constructing Guzheng network teaching, and puts forward the role of network public platform in Guzheng teaching. The results show that: from 2018 to 2020, the scale of online education market will increase from 12.132 million yuan to 34.154 million yuan in three years, and the growth rate of market scale will increase from 17% to 47%.
Urban Landscape Design Based on VR Immersive Interactive Experience App Platform Research and Practice

Urban landscape design is a major content of urban construction. With the development of modern science and technology, design software with different functions has emerged on the market. Based on VR technology, this article makes an idea for the development of urban landscape design App, aiming to provide a new concept for the development of urban landscape design and develop an APP that allows customers to have an immersive interactive experience. This article first expounds the concept of VR technology, then focuses on the immersive interactive experience, and then analyzes the current situation of urban landscape design, including the design principles and deficiencies of urban landscape design. Through questionnaire surveys and interviews, we also explored the market demand and functional requirements of immersive interactive experience landscape design apps. The experimental results show that 86% of people think it is necessary to develop immersive interactive experience landscape design apps, and there are 130 people. I think this App should have the function of rehearsing effects.
Image Caption Generator Using Machine Learning and Deep Neural Networks

The image caption generator’s main aim is to generate an English sentence or caption for an image automatically. The main challenge of the system is to generate correct captions for the given image. This paper proposes an image caption generator that will accept an image as an input and generate an English sentence as output by labeling the image’s content using two optimization techniques such as beam search and greedy search. The system takes the pre-trained deep learning convolutional neural network (CNN) architecture VGG16 model for learning the image features, uses long short-term memory (LSTM) for learning the text features, and combines the image’s result with an LSTM to generate a caption for the image. We use the LSTM model to generate text or sentences or captions for the given input images. This model was developed to build an image caption generator by implementing the convolutional neural network with long short-term memory. The pre-trained VGG16 is used to extract features from the given image. LSTM works as a decoder to generate sentences or captions for the images. This model is trained so that if the input image is given to the model, it will generate captions or sentences describing the image. The system is evaluated with BLEU scores to evaluate the model’s efficiency. The system is trained for 70 epochs with a batch size of 512; the proposed system achieves a performance score of 0.568.
Design, analysis and prototyping of a spherical drone for underground mines

This paper aims to provide performance optimisation of a multirotor drone that is designed for underground spaces. Currently, drones are being used efficiently for various applications in surface fieldwork projects. But using drones for underground spaces is challenging because of confined space, GPS-denied environment and various air properties in underground spaces. In this paper, first, the designed and manufactured drone is discussed. Then, to improve the drone performance for defined missions, a sensitivity analysis is done over the effective design parameters of the drone components and air density as an environmentally effective parameter. The sensitivity analysis over the flight simulations shows that drone performance increases by increasing air density, while the optimised number of propellers blades is presented. The results show that by increasing the battery capacity, the flight time increases but the overall performance of the drone decrease. Also, the drone would be able to carry about 450 grams payload and payloads more than 450 grams can significantly decrease flight time. Finally, the results of sensitivity analysis lead to optimise the drone performance efficiently for the designed mission in underground mines.
Autonomous Weapons Systems: Taking the Human out of the Loop

One of the most disturbing developments in the laws of war is the evolution of autonomous weapons systems AWS which decides on their own target on the basis of artificial intelligence, facial recognition, and big data to hit its target. At present there is no treaty of international that regulates AWS nor is there any hope of having one in the near future. Since the AWS takes humans out of the loop it poses serious challenges to international law, especially to the cardinal principles of international humanitarian law or IHL such as the principle of distinction. Since AWS is now shaping warfare, therefore, it is absolutely necessary to have it governed by binding rules of international law. It is recommended that some uses of AWS may be prohibited whereas others may be regularized.
Payload Measurement Using Suspension System in Goods Carrier

In India, the core of the logistic sector is the trucking industry. The manufacturing business ensures that their product reaches the end customer quickly and thus they outsource their logistical operations to logistic service providers. More than 60% of goods are transported by road. Due to globalization and digitalization, the demand for logistics has increased. To meet the demand most truck owners are recklessly permitting overloading to increase margins, to save time by risking the consequences of in-transit issues as well as its negative impact on the infrastructure. This paper addresses one such problem that many drivers and their customers face regularly. To reduce the time required for weighing and getting instant results a payload measurement system is developed which is an external attachment to the vehicle. The results obtained by this model are quick and accurate.
Smart University’s Risk-Resilience Modeling

The work carried out a systematic analysis of the problems of a smart university and minimizing risks in the transition to a digital society. The university is seen as a developing entity with certain systemic risks. In the work, these risks were classified. The system-synergistic aspects of evolution of university infrastructure are considered. Examples are given to illustrate system processes. The financial sustainability of the university is considered one of the main conditions for the evolution of the university. Along with abstract-algebraic and theoretical-multiple analysis, simple models and risk assessment procedures are also used, which can be practically used in situational modeling.
State of the Art on Cloud-Information Flow Control

Users lose responsibility for personal information whenever they upload their data to the Cloud. The growth of cloud computing would be hampered if indeed the cloud environment fails to produce an appropriate security mechanism to ensure data security. Throughout the intervening decades, access control and encryption methods were unable to properly restrict the spread of private tenant data within the platform. The intricacy of the cloud infrastructure limits the necessary information flow control paradigm, and protecting private data stored in the Cloud becomes challenging. A literature study is conducted in this research program as a first step toward constructing an innovative and secured information flow control in a cloud environment. In this research paper, the most exciting researchers on Cloud information flow control framed by various researchers worldwide are gathered and assessed. The articles are examined regarding security techniques and their benefits and cons. In addition, the service type, information flow direction, and performance recorded by each of the works are discussed. Furthermore, existing models’ research gaps and problems are highlighted to inspire future Cloud-based information flow control researchers.
Mmhfnet: Multi-Modal and Multi-Layer Hybrid Fusion Network for Voice Pathology Detection

Automatic voice pathology detection using non-invasive techniques plays a vital role in diagnosing and early medical intervention. In this paper, a novel deep Multi-Modal and Multi-Layer Hybrid Fusion Network (MMHFNet) is proposed to improve the performance of the non-invasive voice pathology detection systems. MMHFNet simultaneously incorporates complementary information of different modalities (speech and EGG signals). It also vertically combines the low-level features, extracted from shallow layers, and high-level features, extracted from deep layers, to take the full advantage of spatiospectral information of different layers for multi-layer fusion. The features extracted by MMHFNet are then advanced to a LSTM classification network to diagnose the voice pathology. Comprehensive experiments are conducted on the publicly available Saarbruecken Voice Database (SVD) to evaluate the performance of the proposed MMHFNet. This dataset is used in two manners; one using its all samples and the other with selected samples to form the largest balanced SVD dataset. Experimental results demonstrate that the proposed MMHFNet achieves the accuracy rates of 91% and 96.05% for datasets with all and balanced samples, respectively. These are better than reported state-of-the-art results in the literature. Moreover, the proposed MMHFNet is not only a well-suited network to be employed on voice pathology detection problem with two modalities but also easily applicable to various applications with more modalities.
Multi-view Clustering and Multi-view Models

Over the years, the development of information technology applications, in particular, Artificial Intelligence has spurred the need to collect multi-view dataMulti-view data on a large scale. Multi-view dataMulti-view data, in general, is large, heterogeneous and uncertain, but also contains a lot of knowledge to mine and apply. Some of the single-view data clustering techniques have been improved to analyze multi-view dataMulti-view data by extending the structure of the objective function or building associative models. Currently, multi-view clusteringMulti-view clustering methods are quite rich and diverse, however, each method is usually only effective for a specific group of problems. In order to provide an overview of multi-view dataMulti-view data clustering and select suitable methods for specific applications, in this chapter we will review some methods of multi-view clusteringMulti-view clustering. Simultaneously, we also select a number of multi-view dataMulti-view data clustering algorithms to present and analyze some potential knowledge extraction mechanisms in the data views. Some experimental results on the benchmark datasets are also analyzed to evaluate the performance and scalability of different multi-view clusteringMulti-view clustering algorithms.
Text Analysis and Classification for Preprocessing Phase of Automatic Text Summarization Systems

Stop word removal is a basic morphological analysis or a process used to remove those words that appear frequently or repeatedly within the document text. These words do not affect generating language expressions but appear many times in default language transformation and communication. This caused us to acquire more space in memory and the allocation of the same words caused problems with memory indexing. In the case of big data analysis and information management applications, identification of such particular words present within the document is an important task. In this paper, text analysis was carried out to identify various stop words within the text. It is used as a pre-processing operation for the development of various natural language text processing and data mining techniques. This is important in part-of-speech tagging, sentiment analysis, content classification, semantic analysis, search engines, text summarization, named entity recognition, etc. Our proposed system discusses the role of text analysis techniques and pre-processing of language by removing stop words from a given input document written in Marathi text using a linguistic and rule-based approach. The system is particularly discussed for Marathi. Very little work has been done on stop word removal using a supervised approach and evaluating its impact on the performance of information retrieval systems and summarizing the text. The result get accuracy at 32.6% to compress the text.
Remote Co-design in the Digital Age

Co-design is used to provide a wide range of stakeholder perspectives before and during the design process, but geography and transportation difficulties often prevent important stakeholders from participating as co-designers. This chapter explores a spectrum of co-designs from group to individual and describes techniques that have been successful when working with co-designers remotely. Techniques for working with individuals with mental or sensory impairments, in particular visually impaired participants, are also given. Four examples of co-designs led by the authors are given with a discussion and tools used in each session. Incorporating remote techniques into the co-design process enables stakeholders from around the globe and those with mental or sensory impairments to provide feedback and input throughout the development process.
Evaluation of a Guidance and Tutoring Recommendation System

The new digital era has forced many of the services within the university context to be offered virtually, including mentoring and tutoring. To respond to this new challenge, TIMONEL was born, a recommendation system (RS) capable of responding to the needs for the university orientation of undergraduates, postgraduates and university graduates. This study analyses the trajectory of the project from the beginning, focusing on its final evaluation phase in which users’ evaluations of the RS are considered. A total of 487 students from the Universities of Jaen and Granada (Spain) and the Polytechnic Institute of Coimbra (Portugal) participated. In the results, significant differences can be perceived with respect to gender in the different values, with the male gender being more critical. On the other hand, no significant relationship was seen in the RS evaluations with reference to participation in the Tutorial Action Plan (PAT). Finally, the fact of belonging to one or another university also influences the evaluations made of the RS. In conclusion, the overall positive evaluation suggests the RS is a good university advisory tool.
The Application of Knowledge Base in Enterprise Resource Management Strategy

According to the characteristics of the private cloud computing platform of power grid system, when the internal user program applies to use the public resource pool, in order to improve the computing efficiency of the resource pool and reduce the user's time cost, a resource load balancing scheduling algorithm based on average probability is proposed. Through calculation, the algorithm allocates appropriate virtual machines to the user&apos;s program blocks and reduces the queue of virtual machines. The simulation results show that the algorithm has good effect.
Bitcoin Is Just Like Gold Except When It Isn’t - Las Vegas Version

For many equity and fixed income investors the only real investments have cash flows. Bitcoin is an example of an "investment" that does not have cash flows and therefore it is easy for equity and fixed income investors to dismiss Bitcoin out of hand. However, it is possible to use the same ad hoc "equation of exchange" mental framework used to popularize the quantity theory of money and various versions of the equity CAPE model to explore the valuation of gold and Bitcoin. Bitcoin "halving" is an example of the triumph of hope over experience, similar to the belief among some equity investors in the existence of a "January effect". Metcalfe's Law offers some tantalizing insight into the valuation of Bitcoin, but only as a "vision thing".
Wavelet Transformation for Digital Watermarking

Due to the increasing use of internet, it has become most common to share and access information through various applications on internet. The owners of the information shared on internet need various tools for handling the copyright of their data, securing the data from theft and illegal access. To safeguard the information and data a technique known as water marking is extensively used, which provides protection from redistribution of data, illegal transfer and electronically copying the information. Watermarks are the proof of ownership of the information present on the internet and are both in visibility and invisibility mode depending on the user. The techniques used for digital watermarking on the basis of frequency domain are based on discrete cosine transform and wavelet transform. The paper discusses about the frequency domain based techniques in digital watermarking mainly focusing on the wavelet transform methods like Haar wavelet transform, Symlet, Coiflet transform.
Deep Automatic Control of Learning Rates for GANs

In this paper, we propose a method for automatically controlling the learning rate of Generative Adversarial Networks (GANs) so as to stabilize the training of GANs. In recent years, GAN has been successful in various types of image generation tasks. Since GAN trains Generators and Discriminators adversarially, it is very important to keep the balance of their learning progress. However, it is known that the adjustment of learning rate of GAN is extremely difficult compared to conventional networks. Thus, we in this paper propose a method for predicting the future training progress of GANs from the current state of Generators and Discriminators, and for automatically controlling the learning rate of GANs appropriately. The proposed method has been tested using several different GANs, and the results show the proposed method can control the learning rate of GANs appropriately for a variety of tasks.
Systematic Map of Machine Learning Based Breast Cancer Screening

Although Breast Cancer (BC) deaths have decreased over time, it is still the second largest cause of cancer death among women. With the technical revolution of Artificial Intelligence (AI), and the big healthcare data that is becoming more of a reality, many researchers have attempted to employ Machine Learning (ML) techniques to gain a better understanding of this disease. The present paper is a systematic mapping study of the application of ML techniques in Breast Cancer Screening (BCS) between the years 2011 and early 2021. Out of 129 candidate papers we retrieved from six digital libraries, a total of 66 papers were selected according to 5 criteria: year and publication venue, paper type, BCS modality, and empirical type. The results show that classification was the most used ML objective, and that mammography was the most frequent BCS modality used.
Building parsers based on syntax diagrams with multiport components

The problem of constructing parsers from syntax diagrams with multiport components (SD) is solved. An algorithm for constructing a parser based on the GLL algorithm is proposed, which results in the compact representation of the input chain parse forest. The proposed algorithm makes it possible to build parsers based on the SD of an arbitrary structure and does not require preliminary SD transformations. We introduce the concepts of “inference tree” and “parsing forest” for SD and describe the data structures used by the parser, such as a graph-structured stack, a parser descriptor, and a compact representation of the parsing forest. The algorithm for constructing parsers based on SD is described and an example of parser constructing is given.
Quantum Key Distribution

The chapter is dedicated entirely to the field of quantum key distribution, first discussing the high level taxonomy for key distribution schemes and then covering the BB84, B92 as well as the generic class of EPR-based protocols. Using only the basic concepts introduced earlier, the protocols are reconstructed in Q# code.