Reviewerly/exHarmony huggingface dataset:
Graph Neural Networks based Resource Allocation in Heterogeneous Wireless Networks

Graph neural networks(GNNs) have been developed to solve challenging resource allocation (RA) problems, which leads to hopeful results in small and simple communication networks. Due to the inevitable heterogeneity of modern networks, it motivated researchers to develop the heterogeneous graph neural networks (HetGNNs) model for the RA problem of heterogeneous networks. However, node features and edge features are usually ignored by the most extant deep models, limited the performance when the size of the hidden layer in the network is larger than that of the node and edge features. In this paper, the power control or beamforming (PC/BF) in heterogeneous device-to-device (D2D) networks is focused, and proposed a HetGNN for the issue. Extensive simulations show that the proposed approach, matching or even outperforming state-of-the-art learning-based benchmarks.
Research on the Response to Blockchain Investment Announcements Based on Event study

Blockchain technology is a distributed, open and decentralized large-scale network accounting book. Anyone can use the same technical standards to add their own information at any time, extend the blockchain, and continue to meet various needs. Data entry required. This article build a Linear Regression Equation and uses event research and web scraping techniques to determine whether blockchain investment announcements affect the market value of listed companies, and then to determine whether and how the market will respond to investment in blockchain technology. The results show that blockchain investment announcements have had a significant positive impact on the market value of businesses in the short term, but it have trended downward over time.
privacy protection method for deep learning based on local random nodes

Deep learning algorithm needs a lot of data for training to achieve better training results, in practical applications often need to multi-partner data for training. But the centralized training does not meet the standards of privacy protection. Therefore, a cooperative training scheme is proposed. This scheme can achieve privacy protection at a small computational cost. The main idea is to transfer the training process between the first layer and the second layer of the traditional neural network to local, and make the process between them irreversible so as to guarantee the privacy of the training data. The experiment shows that the accuracy of the model can achieve good results.
Automatic lung segmentation in CT scans using guided filtering

The segmentation of the lungs in chest CT scans is a crucial step in computer-aided diagnosis. Current algorithms designed to solve this problem usually utilize a model of some form. To build a sufficiently robust model, a very large amount of diverse data is required, which is not always available. In this work, we propose a novel model-free algorithm for lung segmentation. Our segmentation pipeline consists of expert algorithms, some of which are improved versions of previously known methods, and a novel application of the guided filter method. Our system achieves an IoU (intersection over union) value of 0.9236 ± 0.0290 (mean±std) and a DSC (Dice similarity coefficient) of 0.9601 ± 0.0158 on the LCTSC dataset. These results indicate, that our segmentation pipeline can be a viable solution in certain applications.
Improving Safety in Europe - Detecting Counterfeit Certificates from FFP2 Masks: A Text-Mining Approach

At the beginning of the Corona pandemic, there was a surge in demand for medical masks, especially FFP2 masks, which could not be adequately met by existing mask manufacturers. This led to new manufacturers of FFP2 masks entering the market. However, in order for these new suppliers to be allowed to offer these products in conformity with the law, the masks must be tested by an independent body (notified body). This testing usually takes several months and is associated with a number of bureaucratic hurdles and expenses. This led to the fact that untested masks were sold and are still being sold. In order to subsequently identify untested masks, we propose a method that automatically checks a specific document from the testing, namely the type examination certificate. To enable automated evaluation, this paper presents a text-mining approach for verification. It consists of three steps: i) requirements identification of certificates, ii) data extraction and analysis and iii) evaluation of the results using a decision tree classifier. Finally, the algorithm is validated using a case study of several counterfeit FFP2 type examination certificates. The result shows that the text-mining approach is capable of distinguishing fake from real certificates. The ability to analyze certificates in an automated way will completely change the way market surveillance authorities and accredited verification bodies check documents.
Twieng: A Multi-Domain Twi-English Parallel Corpus for Machine Translation of the Twi Language, A Low-Resource African Language

A Twi-English parallel corpus is certainly an important resource for Machine Translation of Twi (ISO 639-3), a Low- Resource Language (LRL) which is mainly spoken in Ghana and Ivory Coast. Currently large-scale multidomain Twi- English parallel corpus is still unavailable partly due to the difficulties and the arduous efforts required in its design. A digital Twi lexicon curated purposely for linguistic research is also not available. In this paper, we present TWIENG – Twi English corpus, a large-scale multi-domain Twi-English parallel corpus and Twi lexicon, a digital Twi Dictionary. We discuss the data collection methodology, translation, alignment and compilation of the Twi-English parallel sentences and the technology we used to compile and host the corpus. Today’s parallel corpora are crawled from the web using web crawlers, the sentence pairs are processed, aligned, tokenized and compiled to create the corpus. We crawled English sentences from Ghanaian indigenous electronic news portals, Ghanaian Parliamentary Hansards, standard literature and also used crowdsourcing. The sentences are translated by professional translators and linguists, then aligned, tokenized and compiled. The corpus is curated using the sketch engine, a corpus manager and analysis software developed by Lexical Computing Limited. The corpus is manually evaluated by Twi professional linguists. The Corpus has 5,419 parallel sentences which were curled from local news portals, Ghana Parliament Hansard, The New Testament of the Twi Bible and through crowdsourcing via social media sites. CCS CONCEPTS • Computing Methodologies • Artificial Intelligence • Natural Language Processing
Design of Automatic Russian Language Recognition System

Language recognition is also called language recognition. Its purpose is to enable the computer to automatically recognize the language type according to an input speech. Language identification is widely used in the fields of network security, authentication, multilingual information services and so on. Nowadays, the increasingly open international environment and the rapid development of the Internet have brought new challenges to language recognition. Based on the previous research results, this paper designs and implements a language recognition system based on Gaussian mixture model unified background model (GMM UBM), mainly from the feature extraction module, UBM model training module, language model training module and language recognition module.
On the Construction of Knowledge-Communication-Oriented Computer Spoken Corpus for Endangered Languages

This Corpus/corpora is a computer database that stores language materials. Corpus in the world is dominated by lingua franca corpus. However, these corpora have limited samples and are difficult to meet the needs of machine artificial intelligence. The paper proposes to build a Knowledge-Communication-Oriented spoken corpus for Endangered Languages to enrich the content of corpus research. A Knowledge-Communication-Oriented Spoken Corpus was divided into three sub-corporas, the words sub-corpu, the sentences sub-corpus, and the narrative discourses sub-corpus. This paper mainly introduces the methods of constructing the spoken corpus of endangered languages from the aspects of corpus collection, corpus arrangement and corpus annotation.
Advanced Mathematics’ Quality Evaluation Model Based on Grey Correlation Analysis

In order to establish and improve the quality evaluation system of advanced mathematics in colleges and universities, promote the updating of teaching concepts, guide teachers to carry out teaching reform practices, explore new teaching methods, and improve classroom teaching effects, it is necessary to collect the feedback for better teaching effects and learning outcomes. Taking the characteristics of advanced mathematics course into consideration, this paper proposes a teaching evaluation model, which uses the analytic hierarchy process to determine the weights and uses the grey relational analysis method to establish an evaluation model. By using Matlab, the software implements AHP under the condition that each level index in different levels has influence on all subordinate indexes, all of which are generated in the analysis. This model provides a reference for teaching quality evaluation and improving the overall teaching quality of advanced mathematics in colleges and universities.
Design and Implementation of Spoken English System Based on Artificial Intelligence

With the deep integration of technology and education, we are ushering in an era of intelligent education. The combination of technology and English teaching has become a trend, and the traditional teaching methods are being changed by technology. The contradiction between traditional teaching methods and modern oral English teaching needs is becoming increasingly prominent. The development of artificial intelligence technology will help to solve this contradiction. Therefore, the oral English teaching assistant system based on artificial intelligence is designed. The general design part of the system adopts the idea of hierarchical architecture for the overall design, system functional structure design, oral evaluation model design, data storage and use, etc. in the functional structure design, combined with the needs analysis, the account system module, class management module, teaching management module, learning statistics module, notification module and use help are designed respectively, Each functional module complements each other. By means of engineering, combining AI abilities such as oral English evaluation and image retrieval with oral English teaching, we can solve the disadvantages of traditional oral English teaching and establish a new teaching form for oral English Teaching in primary schools.
Construction of Digital Construction and Resource Sharing Platform of University Library Based on "Internet +"

With the development of the Internet, all kinds of systems have basically realized digitalization. The digital construction of university libraries is relatively necessary, and the digital construction will increase the frequency of college students reading books in the library. This will also increase the frequency of reading. Based on the construction of digital construction and resource-sharing platform of "Internet +", this paper discusses the main advantages of "Internet +" technology in the construction of digital construction and resource-sharing platform from the perspective of digital construction formed by various technologies of "Internet +". In the end, it also makes a corresponding plan for the digital construction of efficient library and the technical characteristics of the construction of sharing platform under the "Internet +" technology, and its advantages and application effects.
Analysis and Management System Design of Liberal Arts Courses Based on Decision Tree Algorithm

The goal of teaching in colleges and universities is to cultivate talents with comprehensive development, which is inseparable from liberal arts education aiming at comprehensive development, physical and mental harmony and sound personality. With the improvement of information construction in colleges and universities, more and more teaching information resources are generated in the teaching management of liberal arts courses. Therefore, in this paper, in order to dig deeper into the information data and promote the improvement of the quality of liberal arts teaching, the decision tree algorithm was used as the basic basis to design the analysis and management system of liberal arts courses. The article first analyzed the decision tree algorithm, then presented the framework of the course analysis and management system, designs the database, and finally implemented the system using ASP.NET development language. The results showed that this system performed well in terms of accuracy and running time. It can provide some data support for improving the quality and efficiency of liberal arts course management.
A Study on the Use of Electronic Library at Vietnamese Public Universities

The strong explosion of science and technology around the world has created conditions for information technology to be applied in many fields. The 21st century is known for the 4.0 technology revolution when digital becomes ubiquitous and virtual connection becomes essential. The education must change to adapt to new circumstances. Applying information technology, digitizing teaching organization becomes the top concern of modern education. Universities are the home of nurturing and developing research in many fields whose the results will change many things in life. The need for a library system, which contains hundreds of thousands of documents for these studies, is undeniable. In order to catch up with the modern education trend in the 4.0 technology era, the electronic library system has begun to be applied at universities around the world, including Vietnam's public universities.
Low complexity, diversity preserving hard decision decoder for CRC codes with IoT applications

As Cyclic Redundancy Check (CRC) codes offer the advantages of low power transmission and variable payload length, they have found applications in IoT standards like IEEE 802.15.4 and Bluetooth low energy (BLE). Despite having redundant bits, CRC codes are merely used as error-detecting codes due to the unavailability of a suitable decoder. Several efforts have been made to design a decoder for CRC to use as error-correcting code. The recently proposed GRAND algorithm serves as an error-correcting algorithm for CRC but has huge complexity. In this paper, a low complexity hard decision decoder is proposed for CRC with $g(x)=1+x^{5}+x^{12}+x^{16}$ which is used in IEEE 802.15.4 for IoT applications. The proposed decoder utilizes channel state information (CSI) for decoding in a Rayleigh fading channel and attained fourth-order diversity with very low complexity. The proposed decoder is especially effective at short block lengths; hence it serves as a sound decoder catering to IoT and URLLC services.
A Blockchain Based Platform for Network Flexibility Management in Power Distribution: A Real Application Scenario

In the latest years, the huge increase of renewable energy sources introduces new challenges for smart grid management, which is changing is traditional and consolidated “centralized” approach, where system operators are the managers of the grid and the power flow is unidirectional, in a “decentralized” one, where customers become active players and there is a need to draw energy from multiple localized energy networks. This energy transition requires the development of decentralized solutions and active participation of prosumer and their own assets. Local energy flexibility markets can help in monitoring energy flows, incentivize prosumers' energy supply and demand, support the DSOs in the management of the local network issues and in the optimization of electricity flows. In this paper, we propose a blockchain-based full stack solution for the implementation of an open and fair flexibility market and a secure smart grid data management, in which the entire architecture leverages on blockchain technologies and smart contracts for the certification of the market actions and network measurements, as well as the incentivization during the settlement phase. In addition, the proposed solution includes a smart device named Light Node, which includes a blockchain node and can reads, arranges, certifies in blockchain, and sends the measurement and data provided in a secure and efficient way. The entire solution, implemented in the context of the H2020 Platone project, was already deployed, integrated, and tested on the field in a real scenario in Rome (Italy).
Impact of Specific Language Impairment (SLI) in 6-Year-Old Children

This study focuses on the discipline of psycholinguistics regarding language disordersin children, to be precise of Specific Language Impairment (SLI). This article analyzestwo 6-year-old children who show the characteristics of having SLI in languagedisorders during the COVID-19 pandemic. In order to carry out this study, theresearcher used a qualitative descriptive research method. The method aims to describein a systematic, actual, and accurate way. This research is an analytical study of officialdocuments such as books, articles, journals, and the internet. The accuracy of the data isobtained from the observations of the researcher herself. This study reports that childrenwith SLI often stay until school age and can lead to a lack of academic ability and causevarious psychosocial problems. Some of the causes of SLI in children include genetic,environmental, and social factors, pre and perinatal, gender, prematurity, weight, socio-economic status, maternal mental status, mother's education, and vocabulary, and history of speech-language difficulties.&#x0D;
Pembimbingan Kemampuan Public Speaking pada Organisasi Kesiswaan SMKN 4 Dumai

Public Speaking is used to convey information to other people, the delivery of this information can be in the form of face-to-face presentations, speeches, meeting activities, MC or informal meetings. Physical education and comprehensive higher education do not fully influence individuals to appear in public. In communicating activities, of course, a strategy is needed so that the message conveyed to the interlocutor can be received and understood properly. The ability to speak in public will be a psychological illness for some people who are not used to speaking in public. Based on the results of observations made, the confidence level of the students of SMK N 4 Dumai is quite good, but the ability to speak in public is still lacking. This can be seen from the lack of student motivation in speaking in front of their friends and the general public. This can be seen when students are asked to come forward, in speaking they still falter because of the lack of mastery of public speaking. This is what motivated our PKM team to conduct public speaking training at SMK N 4 Dumai in order to increase the motivation and confidence of the students to dare to speak in public. The implementation method used in this service is in the form of introduction, guidance, training and practice in public speaking
Software Quality Measurement Analysis based on Techniques, Criteria, Metrics, Models, and Datasets

Software quality measurement considers a very crucial process in developing software. Achieving high-quality software leads to produce software with high efficiency, and reliability and to reduce the software cost. This study intends to explain the most important factors that have direct effects on the software quality measurement process. In addition, this study summarized the software quality models; where each model could be classified into various scopes such as reliability, functionality, usability, portability, and maintainability. Moreover, the most recent techniques, which are used in software quality measurement, are concluded. Furthermore, the vital metrics that play an important role in the measurement process have been determined. Last but not least, the most commonly used datasets in software quality measurement in many aspects and scopes have been presented. As a result, this paper drew the road map for future work in the field of software engineering to accomplish more research work in developing novel software quality measurement approaches based on machine learning techniques such as deep learning methods and ensemble techniques that may give promising results.
Deep Feature Selection Model Based on Convolutional Neural Network and Binary Marine Predator Algorithm

most image datasets in classification have several features that negatively affect classification accuracy. Dimensions are reduced and features are selected through the use of several metaheuristic algorithms such as the Marine Predators Algorithm (MPA) after they are converted from a connected region to a discrete region. In this study, We first obtain the features from the dataset images using ResNet-18, a powerful convolution neural networks (CNN) architecture, pretrained on ImageNet. Then the features are taken from fully connected layers (FC 1000) in ResNet-18 and input into the binary marine predator (BMPA) algorithm, where the inspired algorithm identifies the most pertinent features and removes the less pertinent and noisy features to improve the classification process, then the features are input into the support vector machine (SVM) classifier. The proposed approach has high efficiency and the ability to obtain higher classification accuracy and few advantages.
Improve Compression Data Using Serial Combination Techniques

Data Compression (DC) is an important issue in terms of digital communications systems because it places an important rule and reduces the complexity and power consumption. Various techniques were used for this task such as; Huffman, Shannon-Fano, LZW, and LZ4. In order to improve compression ratio (CR) which leads to more capacity saving and reduces the required bandwidth in terms of the communications system, this paper proposed a combination system which consists of a serial two techniques. The combination system achieve a decrease in the file size by 78.832%, 76.68%, and 78.139%, respectively when compared to Huffman, Shannon-Fano, and LZW. The required payment of the proposed system is to increase the execution time (ET) by 1.2 over traditional techniques. The proposed combination system is very important in modern communications systems with a huge data transferring and saving.
Incoming and Outgoing Mail Tracking System using Barcode

Despite the importance of reliability when exchanging official books between the different state departments, the traditional paper-based methods used did not pay much attention to them, as they suffered from some limitations related to manual work errors as well as the attendant costs of transportation. It was realized that the process of sending and receiving unreliable information leads to a decrease in administrative work in terms of the lack of completion of official books. To provide a satisfactory completion rate by avoiding the unreliable method of sending and receiving, it has become necessary to propose a smart electronic system. It can be used to estimate the reliability of delivery under various sending and receiving conditions. The main objective of this study is to provide an efficient, accurate and easy-to-use system for the employees of the educational institution or any other organizations to ensure that all data sent and received is documented and easy to retrieve at any time and from anywhere. This can be achieved by proposing a smart and reliable electronic web-based system based on barcode technology to meet all employee needs. Interfaces for incoming and outgoing books and recipient identification are designed using programming languages. The system can improve administrative performance by overcoming many of the obstacles and problems associated with the traditional system with minimal effort and cost.