Reviewerly/exHarmony huggingface dataset:
Resolving Context Inconsistency Approach Based on Random Forest Tree

In modern and automated environments, billions of devices are communicating over the Internet in a robust and automated pattern. These devices used for sending information about surrounding objects and the environment, we call this information “Context". Contexts can help applications to be more context-aware (the ability of an application to perceive contexts and act automatically upon them). Sometimes context becomes inaccurate, noisy, or inconsistent due to some malfunction or transmission error. Several approaches have been suggested to solve inconsistent contexts by providing a context inconsistency resolution mechanism. This paper proposes a new approach to increase resolution accuracy by building a random forest tree model. The model is used to predicate which resolution method to use on context inconsistency based on previous knowledge. The results show that the proposed model is more effective in protecting correct contexts, removing bad contexts, and improving the functionality of context awareness.
Research on Application Scenarios of HTAP in Distribution Network

The distribution network is an important part of the smart grid, covering multiple management services such as dispatching, operation inspection, marketing, etc., with rich types of data sources and relatively high requirements for real-time data. Starting from the data application requirements of the distribution network, this paper analyzes its comprehensive requirements for transaction processing and data analysis capabilities, and then explains the basic theories and technical characteristics of Hybrid Transactional/Analytical Processing (HTAP), and integrating the mainstream HTAP products, then the application scenarios of the hybrid in the distribution network are proposed, and the application architecture design and function design of the HTAP are carried out from the perspectives of distribution network panoramic visualization, distribution Internet of things, and power outage applications. Finally, the application scenarios of the hybrid in the distribution network are briefly summarized.
Virtual currencies, fintech, blockchain, an ever-changing economic world. The social impact of financial technologies

"Nowadays, science and technology are in an elevated development and it has effects upon all levels of social life. In this context, payment systems, trading and money have gradually become further and further influenced by elements of information technology and cybernetics. Digitization is an irreversible process, it involves adapting theoretical knowledge to the updated reality, thus, the research aimed to review, through analysis of contemporary writings, key issues related to virtual currencies, Fintech and technologies that are increasingly influencing economic transactions and money. The main purpose of the paper is to assess the degree of knowledge and acceptance of people related to virtual currencies and services offered by companies in the Fintech segment, in the context of their prompt emancipation and the possibility to replace the classic options in the foreseeable future. The methodology approached is the qualitative one, and a questionnaire was conducted to substantiate the research. In conclusion, through this research, we managed to expose an overview of what fintech, blockchain and virtual currencies entail, but also of some risks, opportunities and related weaknesses, to assess the degree of knowledge and acceptance of society related to virtual currencies and Fintech services, but also their ability to influence the quality of life and implicitly the human society."
ANALYSIS OF PUPIL'S SOLUTION OF RESEARCH CHARACTER IN THE TRAINING OF FUTURE TEACHERS

In the frame of the realized research based on the constructivist approach to teaching mathematics at primary and secondary schools, among other aspects, the idea was to focus on one of the key and important activities of the teacher, such as the analysis of pupils' problem solving. In this contribution, the written form of the pupils� solutions has been focused and shown. According to the missing practical experiences in the mathematical areas, pupils investigated some properties (principles) in a series of mathematical problems bound to the mathematical research character, i.e., in the frame of solving these tasks. In these series, problems focused on the investigation and justification, the phenomenon periodical appeared by a division of pairs of natural numbers with the rational number as their result. The sample of the population was, instead of the students, who have been prepared for the professional position of the primary and secondary school math teacher. Each student received various series of problems; however, the level of the series was similar. In the frame of the research, analysis the thinking movements from the first task to the second task, from the second task to the third task in the frame of the same series, and to the fourth task and summarize the thinking movement across the whole series. The further aim is the comparison, how the thinking movements are being differed in the frame of the partial movements of each considered student. Due to the scope, we limited ourselves to comparing two students.
Quantum Networking—Design Challenges

Quantum computing has revamped and paved a new stream of research diverted away from the classical means of computation and processing. One setback in this field was the usage of existing classical Internet coupled with a machine capable of quantum computing. Hence, extensive research has been done in this domain to provide and support the incorporation of quantum Internet with quantum computing to boost the overall performance of the quantum system. This paper aims to provide possible implementations of quantum Internet which can be coupled with the quantum computer being developed by various organizations. The major advancement would be done in the link layer as well as the routing processors, as these are the layers responsible for converting signals. The routing processor should be able to cope up with the quantum qubits to re-route and perform the necessary network layer computations to direct the packages to its respective destination addresses.
Music Style Transfer Using Constant-Q Transform Spectrograms

Previous work on music generation and transformation has commonly targeted single instrument or single melody music. Here, in contrast, five music genres are used with the goal to achieve selective remixing by using domain transfer methods on spectrogram images of music. A pipeline architecture comprised of two independent generative adversarial network models was created. The first applies features from one of the genres to constant-Q transform spectrogram images to perform style transfer. The second network turns a spectrogram into a real-value tensor representation which is approximately reconstructed back into audio. The system was evaluated experimentally and through a survey. Due to the increased complexity involved in processing high sample rate music with homophonic or polyphonic audio textures, the system’s audio output was considered to be low quality, but the style transfer produced noticeable selective remixing on most of the music tracks evaluated.
Image Compression Based on Mixed Matrix Decomposition of NMF and SVD

Image compression has always been a key research hotspots in image processing. An efficient image compression approach will not only save considerable storage resources, but also exceedingly ease the communication pressure of the network transmission, which has great research significance and practical value. Since the essence of the image is matrix, so mixed matrix decomposition of NMF and SVD is introduced to perform two-level compression framework on images. The experimental results demenstrated that this approach based on mixed matrix decomposition had a CR with larger dynamic range through flexible parameter adjustment and the PSNR of the restored image is 29 dB–36 dB. It verifiy that this method is effective.
Non-destructive Testing of Metal-Polymer Laminates by Digital Shearography

This paper presents non-destructive detection of defects in metal-polymer laminates using digital shearography. A shearography setup built from of the shelf components was used together with self-written data evaluation software. Testing was performed on a polymer strap glued to a metal one of the same size, with purposely made breaks in the glue layer. Connection defects, their location, size and shape could be detected using three different defect sizes; the smallest defect was 6 mm wide. Testing required 10 s of heating; the data evaluation needed less than a minute. The temperature of the samples was raised by about 2 ℃ on the metal side and 0.5 ℃ on the polymer side. Relaxation of the samples was also observed. The small rise of temperature and the fast return to a state of equilibrium allows for multiple testing of samples without causing damage.
Clustering of Template-Generated Webpages Using DOM Tree Paths of URLs

Web templates are layouts for webpages that enable rapid and easy access to web content. Web data integration solutions use template based wrapper tools to extract product information from e-commerce websites. Given a collection of webpages, wrapper tools are used to discover the template portion of a webpage and extract data from it. These wrapper based data extraction techniques require pages created with the same template belong to the same cluster. Clustering these webpages based on their template is a significant challenge. While there are algorithms for clustering webpages based on their template, they are computationally intensive to be applied at web scale. By examining the DOM tree paths of URLs on a webpage, the proposed work presents a highly scalable methodology for clustering template-generated webpages. Further, the locality sensitive hashing (LSH) technique is used to reduce the cost of clustering. The proposed technique is found to be more precise and cost effective than the existing baseline methods when tested on three separate real-time data sets.
The research of text retrieval based on DeepCT and Conv-KNRM

Text retrieval is the main means for people to obtain information from a huge text database. Current document retrieval methods do not consider the association between words and context, and cannot effectively retrieve information based on semantics. Aiming at the semantic feature extraction of text retrieval, this paper uses Bert, DeepCT and KNRM and its derivative model to improve the construction and feature extraction of similarity matrix, and proposes a text retrieval model based on DeepCT and Conv-KNRM. The goal is to give appropriate word weights to different keywords and improve the accuracy of text retrieval. In this paper, through experimental comparison, it is proved that this model improves the accuracy of text retrieval.
Validation Technique for Zero-Knowledge-Based Transactions of Blockchain Digital Assets Using ASCII Computation Technique

The transparency of Blockchain technology causes privacy concerns because every transaction must be made public and verified by anyone. This makes the financial world reluctant to adopt Blockchain technology, hindering its proliferation. This paper study proposes a zero-knowledge-based transaction verification technique to ensure that privacy and encryption are maintained for Blockchain transactions. The proposed model uses hash-algorithms and American Standard Code for Information (ASCII) calculation techniques to encrypt personal assets or transfer details. This technology allows anyone to check the validity of a transaction while concealing details such as the transferred amount, total assets, sender, and recipients. This overcomes the disadvantage of having a small number of verifies with the authority to verify the transaction, which stratifies the authority and reduces the reliability of verification. Our technique encrypts users’ assets to effectively conceal the exact amount, authorizes every user for verifying transactions, and allows anyone to check if the sender has adequate funds for the transaction. A simplified algorithm ensures the clearness clarity of the verification and reduces the operation time. The feasibility of the technique is verified theoretically and experimentally based on zero-knowledge-proof technology. Experiments confirm that this approach enhances privacy protection.
Application Verification of Wireless Sensor Network in Nuclear Power Plant

The application of wireless sensor network in nuclear power plant can greatly reduce the workload of nuclear power plant cable wiring, and help to improve the automation and information level of nuclear power plant. However, its application also faces the constraints of strong electromagnetic interference, high temperature and other special complex nuclear environment, especially the narrow multi metal enclosed space of nuclear power plant cabin, which brings challenges to the application of wireless sensor. Based on the Lora wireless protocol, this paper studies the low-power selection of wireless sensor and MCU, sleep / wake-up low-power mechanism, dual star adaptive frequency hopping anti-interference, frequency division and time division multi node real-time communication and other aspects. On this basis, the prototype is developed, and the functional test, high and low temperature test, electromagnetic compatibility test and bench test are carried out. The feasibility of wireless sensor application in nuclear power plant is preliminarily verified, which lays a foundation for further application.
Incorporating Option and Out-of-domain Knowledge for Multi-choice Machine Reading Comprehension

Multi-choice Machine Reading Comprehension (MRC) requires the model to select the correct answer from a set of answer candidates given the corresponding passage and question. Previous studies mainly focus on complex matching networks to model the relationship among options, passage and question. However, these models obtain little improvement over the powerful Pre-trained Language Models (PLMs). In this paper, we propose a simple method to incorporate option knowledge from PLMs and introduce out-of-domain knowledge by multi-task learning skillfully. Our approach obtains state-of-the-art results on Chinese multi-choice MRC dataset ReCO and also effectively improves the performance on C3.
ECC-Based Secure and Efficient Authentication for Edge Devices and Cloud Server with Session Key Establishment

With the rapid progress in various technologies, Internet of things (IoT) considers as one of the hot topics in the research community. In modern days, more and more sensed data from IoT devices are store in cloud. However, the increasing number of cloud data security incidents guarantee that confidentiality of sharing data is still a significant challenge. This paper proposed a proficient and secure authentication scheme among IoT edge devices and cloud server based on ECC practice. We examine our proposed protocol design logic with the AVISPA tool. In addition, performance comparison results and security requirements show that the proposed scheme is secure against various known, unknown attacks, and it is also more resourceful than the existing schemes.
Automatic Code-Switched Lecture Annotation

Speech Recognition is one of the advanced technologies that has various usages in our everyday lives. By taking advantage of this technology, there is a chance to help make learning for deaf and hard-of-hearing students easier. This paper presents a lecture annotation tool that can facilitate the learning process to these students by proving post-lecture annotated slides. The system does this by utilizing the lecture slides, audio recording of the lecture and a Speech Recognition API. The system goes through two phases, the transcription phase and the slide matching phase. After recording the speech of the lecturer, the audio file is taken as an input to the transcription module. Afterwards, the transcription module transcribes it to text. The main challenge was that lecturers use code-mixed language (English/Arabic). Thus, the transcription module transcribes bilingual audio simultaneously by chunking up the audio file. Afterwards, the text and the lecture slides are given to the slide-matching module. The slide-matching module outputs a PDF file with the text aligned on the PDF slides accordingly. This PDF lecture file can later be viewed by students to help them study and understand the lecture.
Protected Fair Secret Sharing Based Bivariate Asymmetric Polynomials in Satellite Network

Verifiable secret sharing mainly solves the cheating behavior between malicious participants and the ground control center in the satellite network.The verification stage can verify the effectiveness of secret shares issued by the ground control center to each participant and verify the effectiveness of secret shares shown by participants.We use a lot of difficult assumptions based on mathematical problems in the verification stage, such as solving the difficult problem of the discrete logarithm, large integer prime factorization, and so on.Compared with other verifiable secret sharing schemes designed for difficult problems under the same security, the verifiable secret sharing scheme based on the Elliptic Curve Cryptography (ECC) system has the advantages of less computational overhead and shorter key.At present, the binary polynomial is a single secret scheme and cannot provide effective verification.Therefore, based on a Protected Verifiable Synchronous Multi Secret Sharing (PVS-MSS) scheme, this paper is designed based on bivariate asymmetric polynomials.The advanced verifiable attribute is introduced into the Protected Secret Sharing (PSS) scheme.This paper extends the protected synchronous multi-secret sharing scheme based on bivariate polynomial design.The ECC system constructs the security channel between the ground control center and participants and constructs the verification algorithm.Through the verification algorithm, any participant can verify the consistency and effectiveness of the secret shadow and secret share received from other participants or presented by the secret distribution center.Therefore, no additional key agreement protocol is required; participants do not need to negotiate the session key for encryption; the secret share polynomial can generate the session key between participants and speed up the secret reconstruction process.The verification stage has lower computational complexity than the verifiable scheme constructed by Rivest Shamir Adleman CMC, 2022, vol.72, no.3 (RSA) and other encryption methods.Chinese Remainder Theorem (CRT) is used to update the secret shadow.The secret shadow does not need to be updated with the change of the scheme shared secret, and the public value update efficiency is higher.Reduce the complexity of sharing secret updates in a synchronous multi-secret sharing scheme.
Knowledge Graph-based Network Analysis on the Elements of Autonomous Transportation System

Autonomous transportation system (ATS) is a further development on the basis of intelligent transportation system with strong ability of self-organizing operation and autonomous service. In order to intuitively display the composition and correlation of ATS, the characteristics of elements and their potential laws are deeply explored. In this paper, the entity categories are defined based on five traffic elements: technology, demand, service, function and component, and the correlations are established through their attributes. With the help of Neo4j graph database, the structured data information is stored to build a network knowledge graph of transportation system. Accordingly, the co-occurrence analysis was used to identify the potential correlation of similar elements and calculate their correlation strength. Meanwhile, the key degree of each element in the transportation system is studied by combining the correlation indicators and statistics of the correlation relationship of different elements. Finally, the “V2X based Vehicle Collision Warning for Autonomous Vehicles” scenario is taken as an example for empirical analysis. Through the knowledge graph analysis, the correlations between various elements of the autonomous transportation system are visualized, and the linkage mechanism is deeply identified. Also, this work can provide theoretical support for the construction and improvement of the system architecture in the future.
Leverage and discriminant analysis of face recognition

Now a days, security related issue plays a key role ineach and every organization. Every organization consists of internal security methods and makes it work as efficient as possible. One such security method is the online Face recognition process. In recent past days, online examinations are being held in many organizations. Due to this, there is a chance of misusing the system in many ways. One such problem we have identified is that the problem arises between the twoidentical persons / twins. To solve this problem we introduced a new technique where this system cannot be misused with thehelp of transfer learning approach using a CNN model (ResNet) for learning invariant features from the facial images. Human face recognition (HFR) approach provides more challenging issues because of the large intra class variation among the facial images and also due to the less availability of data that needs to be trained to the system for accurate detection. This paper proposes the transfer learning approach for learning invariant features from the facial images more accurately.
Clusterization of White Blood Cells on the Modified UPGMC Method

Modern methods of flow cytometry make it possible to characterize cell populations with unprecedented detail, but the traditional analysis of data using the “manual gating” method under these conditions is ineffective and unreliable. A large number of research papers have been published in recent years that describe specialized clustering algorithms for detecting and determining populations of white blood cells. However, problems related to the presence of noise and different data density remain relevant. The internal problem of cluster analysis associated with determining the preferred number of clusters, and the moment the process itself stops remains unresolved. It is proposed to use the modified UPGMC (Unweighted Pair Group Method with Centroid average) with the Markov moment of stopping the clustering process from eliminating noise and overcoming problems associated with different data density.
SIFT Based Feature Matching Algorithm for Cartoon Plagiarism Detection

The rise of digital technology has injected new vitality into the development of the animation industry. However, the problem of copyright infringement of cartoon images has also become a major obstacle to its development. The theoretical defects of the current law, the concealment of infringement forms, and the low cost of infringement are the main reasons for this dilemma. With the rapid development of Internet information and digital image processing technology, the use, acquisition, transmission and exchange of image information has become more and more convenient. Large-scale digital images will appear on the Internet and in human life all the time. This topic intends to study the extraction process and matching process based on SIFT feature descriptors, and analyze the advantages and problems of the algorithm at the same time, and finally to propose an improvement method for the lack of color information in the SIFT algorithm. Applying the image grayscale algorithm to the first step of the SIFT algorithm, the image is first converted from the RGB color space to the HSV color space, then is calculated the chromaticity difference between adjacent pixels, and finally is performed the chromaticity difference iterative optimization to obtain the final grayscale image.