Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Channel Estimation for 5g And beyond

As the demand for high-speed and reliable wireless communication continues to grow, 5G technology has emerged as a promising solution to meet these requirements. However, accurate channel estimation is essential for reliable and efficient data transmission in 5G and beyond. Traditional channel estimation techniques face challenges as a result of the time-varying nature of channels for wireless communication, frequency-selective fading, and interference from neighboring subcarriers. To address these challenges, deep learning models have emerged as promising solutions for channel estimation in 5G systems and beyond. Leveraging the powerful representation learning capabilities of neural networks approach have been adopted to learn the underlying channel characteristics directly from the received signals, without relying on explicit mathematical models. This approach offers several advantages, including improved estimation accuracy, reduced computational complexity, and enhanced robustness against channel variations. In this paper, a DL network for Long- Short- Term - Memory (LSTM) is utilized in channel estimation approach and compare the results with traditional approaches like Minimum- Mean- Square- Error (MMSE) and Least Squares (LS). Results demonstrate that deep learning models can achieve superior estimation accuracy, especially with low number of pilots leading to increased spectral efficiency, enhanced system capacity, and reduce the latency, even in challenging channel conditions, which is the main requirements in 5G and beyond.
Future Human Activity Prediction Using Wavelet And Lstm

Future Human Activity Prediction holds significant importance as it enables early detection and monitoring of various aspects, such as elderly care, early fall detection systems, smart-home applications, and E-health monitoring.
Improving Public Record Management System at Government Ministries and Agencies: Factors to Consider and Action Steps to Take

The way in which Government ministries and agencies managed and preserved its records is crucial for its survival in a rapidly changing society. The study concentrated on exploring factors posing challenges to the management and preservation of public records as well as determine action steps needed for improving public record management systems at government ministries and agencies in Liberia, where it was expected to cover the three main specific objectives: record keeping system, policies kept in keeping records and ways to improved public records management system. The researcher employed a cross-sectional descriptive survey in order to quantitatively examine the challenges faced by government institutions. A researcher-administered opened ended questionnaire was used as a data collection tool to study a stratified sample of Seven (7) members from an estimated population of twenty seven (27). The data were collected from the fields both primary and secondary data, the collected data and findings were analyzed and computed both qualitatively and quantitatively to get reliable and valid data using tables, charts and explanation. The data collected was from five records departments of the Government ministries and agencies whose population formed the sample size and the targeted sample. Findings on the type of systems used in record keeping revealed that most problems are experienced when using the manual system as it is not much efficient in keeping mostly for a long time period. Records are lost easily, mis-recording of the transactions and fail to correct due to bureaucracy in correcting. Although there were sporadic cases of record keeping, a greater number of respondents revealed that government entities lack the facilities, equipment, education and trained/skilled personnel to apply correct records management procedures in their daily administration. The findings on the policies revealed that most 60% percent of Government entities do not have written policy on records management and preservation while 40% percent of some Government entities agreed that they have records policy. Some workers are aware about the few written policies which are kept but fail to follow them. This is due to that activities are made in a usual manner even with no policies as each one sees. The aspect of record keeping is a reluctant procedure. Procedures are present also but not well identified by most of the respondents. The findings also revealed patterns and trends of non-compliance with records management standards and guidelines.
Inventiveness Aided by Artificial Intelligence and Patent Protection in Polish and European Law. A Comparative Perspective

The article scrutinises the intricate interplay between artificial intelligence (AI) and patent jurisprudence. According to the authors, while inventions conventionally qualify for patent protection, those predominantly or entirely conceived by AI fall outside the purview of extant Polish and European legislative frameworks. The article delineates the parameters of AI and explicates the criteria for patentability, accentuating that inventions engendered by AI present a conundrum for the prevailing legal paradigms.
The Impact of Smartphones and Fixed Phones on Financial Development: A Case Study on Jordan During the Period 2008-2021

This study aims to analyze the impact of smartphones and fixed phones on the financial development of Jordan, and the independent variables (smartphones and fixed phones). The current study relied on data provided by the Department of Statistics. Indicators of financial development were sourced from the CBJ data. Statistical analysis was performed using electronic opinion software. Results showed a statistically significant positive relationship between individuals’ smartphones and fixed phones and the financial development index (i.e., ratio of money supply to GDP). Moreover, there was a statistically significant positive relationship between smartphones and the financial development index (i.e., deposits to GDP), but fixed phones have no effect on the financial development index (i.e., deposits to GDP). Individuals owning smartphones have a strong positive impact on financial development. The reasons include the high-tech nature of mobile phones and their general wide use in financial applications and financial technologies One of the key recommendations of the study is that financial and economic authorities should collaborate with the Ministry of Digital Economy to enhance the use of smartphones in the banking system, improve digital infrastructure, and provide comprehensive coverage for smartphones and the Internet.
Evaluating the Effectiveness of Text Tokenization Methods

Text tokenization methods that use a dictionary of tokens and do without it are considered. Tokenization and token dictionary producing algorithms are given. The effectiveness of tokenization methods is evaluated as applied to the parts of speech tagging (POST), documents classification (DC) and punctuation restoration (PR) tasks. The POST and PR tasks are reduced to the n-gram classification problem (respectively, n = 3 and n = 5). A neural network was taken as a classifier in the form of a multilayer perceptron that receives at its input vector representations of documents in the case of a DC, or vector representations of n-grams if a POST or a PR task is solved. Vector representations are produced using the word2vec model. The experiments performed allow the comparative effectiveness of seven tokenization methods to be estimated. Three of them, which form the G1 group, translate text elements into tokens by selecting them from pre-created dictionaries. The dictionaries are built using the BPE, BPE-dropout, and Kudo algorithms. The other four members of the G2 group perform tokenization without accessing the token dictionary – these are methods that generate tokens in the form of initial word forms, initial forms of word forms, word form bases, and word form bases and affixes. The dictionaries and vector representations of tokens are produced by Python procedures based on the text corpus that combines the corpora of the DC and PR tasks. The used corpora contain texts in Russian. In the tasks being solved, the applied tokenization methods demonstrate different efficiency: the growth of F1 when using the best group G2 method instead of the best group G1 method was found to be 4.04, 2.29, and 3.34%, respectively in the POST, DC, and PR problems.
The Mathematical Model of a Visual System for Object Recognition Against Equally Bright Backgrounds

Currently, there is a growing interest in the problem of object recognition in various practical applications. Despite the obvious ease with which a human recognizes the objects surrounding him or her, no visual system models have hitherto been developed that would solve the recognition problem in the same general scope and with the same accuracy. There are various approaches to solving the object recognition problem. One of possible approaches is to build a mathematical model that would adequately describe the human response to the objects observed. The complexity of applying a mathematical model to the human eye lies in the fact that the algorithm according to which a human recognizes objects is still unknown. In the study presented, the main hypothesis is the assumption that under threshold recognition conditions, an observer, as in the case of detecting objects, makes the maximum use of a priori and a posteriori information, trying to solve the problem assigned to him or her in the best way. This allows an assumption to be made that the observer characteristics should be close to those of an optimal image receiver. The article discusses the operation of an optimal image receiver in regard of modeling the characteristics of the human visual system in solving the problem of recognizing objects against equally bright backgrounds. Two cases are considered: recognition of pairs of objects and recognition of any object from a set of other objects known to the observer. The main calculation relations describing the response of observers to the images presented are obtained, and the further line of work is indicated.
INTERACTIVE TECHNOLOGIES IN THE CONTEXT OF AN INTERDISCIPLINARY APPROACH TO EDUCATION AS A PREREQUISITE FOR SUCCESS IN THE FUTURE PROFESSIONAL ACTIVITIES OF SPECIALISTS

The article considers the need to solve the problem related to the search for pedagogical concepts and technologies for the implementation of an effective and interactive interdisciplinary approach to education as a prerequisite for the success of the future professional activity of specialists. The importance of the use of interactive technologies in the conditions of the European educational space is em-phasized, which opens up new opportunities not only for obtaining an education, but also for the development of the European labor market. The question of the use of interactive technologies as the main component of the professional growth of future specialists is considered. It was determined that to improve the quality of edu-cation, it is necessary to involve interactive technologies in the educational process. It is proven that as a result of the optimal use of various interactive teaching methods, the positions of the teacher and students in the educational process change. The opinions of scientists regarding the need to intro-duce interactive technologies into the educational process in the context of an interdisciplinary approach are consid-ered. It is analyzed how the interaction of various disci-plines can contribute to the formation of a comprehensive understanding of the subject area and the development of key skills in students. It is substantiated that the use of interactive technologies in combination with an interdisci-plinary approach can help create flexibility of thinking in students, the ability to work in a team and develop crea-tive approaches to problem solving.
Application and Development of Embedded Systems with IoT Components: Aspect of Safety and Reliability

We will present the possibilities of application and development, especially in the field of embedded systems, its interaction with other IoT components, the security aspects of individual components, as well as the domain of their interaction. In embedded systems, we will present new technologies such as fuzzy logic, application possibilities in embedded systems, and machine learning, as application possibilities through the implementation of machine learning. Then we will describe some more examples of the application of fuzzy logic, the automatic control of certain functions in cameras, as well as the defuzzification process, and the possibility of application in security cameras. In embedded systems, we will present the basic aspects of optimization and security, both in everyday applications and in interaction with other components of IoT technologies. The paper shows how security, reliability, and cost estimates affect the implementation phases and the final use of embedded systems, through examples of their application in industry. Security and data protection is shown through the construction of the mentioned devices, their application, but also different encryption methods, permissions, security devices at the network level, as well as implemented IoT technologies. Application examples are focused on the real segment, both in the field of transport, multimedia, design, and in the field of industrial application possibilities.
The Utilization and Practicality of Learning Media Based Augmented Reality

According to the skills and interests of the students, an effective learning process should be dynamic, enjoyable, demanding, motivating, and provide them more room to develop their creativity and independence. Theory is not as important in vocational high school as practice is. The following factors should be taken into account while choosing media: learning objectives, effectiveness, accessibility, students, usage, cost, and quality. One of the advancements in educational media that is still in use today, the new one uses augmented reality to learn media. Vuforia and Unity 3D are tools that can be used to create augmented reality. An interactive learning tool with augmented reality is the end product. The created learning media's validity was analyzed, and the average result fell into the "Valid" category at 3.21. In the "very practical" category, augmented reality learning materials have an average practicality of 88.9%. The conclusion of the research is digital technology that can be used by teachers in improving the quality of the learning process including augmented reality technology. Utilization of digital technology in the process learning can increase interest and motivation student learning, the quality of the learning process, and results learning.
Experiential Mobile Learning: QR Code Driven Strategy in Teaching Thermodynamics in Science 12

The purpose of this study was to examine whether QR code driven strategy would be effective in teaching thermodynamics in science 12. Experimental design was used to test the effectiveness of the strategy. Further, pretest-post- test design was employed wherein one section from grade 12 STEM strand was exposed to the intervention while the other section was exposed to traditional approach. Both groups took the same pretest-post-test which was composed of 50 multiple choice questions. The test questionnaire used was validated using Zipgrade application to determine the discrimination and difficulty index. Moreover, test scores were subjected to Cronbach’s Alpha for reliability test. Weighted mean was computed to describe the test scores of both controlled and experimental groups. T-test of difference between means of independent samples were used to determine the difference between the means of two groups and were tested at 0.05 level of significance. Results revealed that the two groups under study did not differ significantly in their pretest, which means that the students’ knowledge before the intervention were about at the same level. In contrast, based on the statistical analysis, the t-test significantly differentiated the two groups in favor of the experimental group, which registered higher means in the posttest. This means that after the learning units using the QR code driven strategy, the experimental group was greatly improved in the posttest. Together, these findings also suggest that the two groups exhibited significant difference in their pretest and posttest but the QR code driven strategy effects to be predominant.
Enhancing Software Development Projects: The Impact of Delegation And Leadership in Software Management

Delegation and leadership are critical components of software management, as they play a crucial role in determining the success of software development projects. This study examined the relationship between delegation and leadership in software management and the impact of these factors on project outcomes. Results showed that effective delegation and transformational leadership styles can improve workflow, enhance team motivation and productivity, and ultimately lead to successful software development projects. The findings of this study have important implications for software management practices, as they suggest that organizations and software managers should prioritize the development of effective delegation and leadership practices to ensure the success of their software development initiatives. Further research is needed to explore the complex interplay between delegation and leadership in software management, and to identify best practices for improving these processes.
Systems of Singular Differential Equations as the Basis for Neural Network Modeling of Chaotic Processes

Currently, systems of neural ordinary differential equations (ODEs) have become widespread for modeling various dynamic processes. However, in forecasting tasks, priority remains with the classical neural network approach to building a model. This is due to the fact that by choosing the neural network architecture, a more accurate approximation of the trajectories of a dynamic system can be achieved. It is known that the accuracy of the mentioned approximation significantly depends on the settings of the neural network parameters and their initial values. In this regard, the main idea of the article is that the initial values of the neural network parameters are taken to be equal to the parameters of the neural ODE system obtained by modeling the same process, which will then be simulated using a neural network. Subsequently, the singular ODE system was used to adjust the parameters of the LSTM (Long Short Term Memory) neural network. The results obtained were used to model the process of epilepsy.
Heralded and robust <i>W</i>-state generation for distant superconducting qubits with practical microwave pulse scattering

Nonlocal multipartite entanglement holds significant potential for both fundamental physics and practical applications. We present a robust protocol for generating remote entanglement, specifically W states, among multiple nonlocal superconducting qubits. Our protocol employs practical microwave pulse scattering through a microwave cavity, which is coupled to a far-detuned superconducting transmon qubit, facilitating a CNOT-like operation. Our approach can be implemented without the need for single microwave-photon sources and its completion is heralded by microwave photon measurements. Furthermore, our protocol can generate faithful nonlocal W states of distant superconducting qubits with practical detector units, each cascading two quantum non-demolition detectors, rather than ideal single-photon detectors. These distinct features make our protocol particularly useful for large quantum secure communication networks.
Automatic Topic-Based Web Page Classification Using Deep Learning

The internet is frequently surfed by people by using smartphones, laptops, or computers in order to search information online in the web. The increase of information in the web has made the web pages grow day by day. The automatic topic-based web page classification is used to manage the excessive amount of web pages by classifying them to different categories based on the web page content. Different machine learning algorithms have been employed as web page classifiers to categorise the web pages. However, there is lack of study that review classification of web pages using deep learning. In this study, the automatic topic-based classification of web pages utilising deep learning that has been proposed by many key researchers are reviewed. The relevant research papers are selected from reputable research databases. The review process looked at the dataset, features, algorithm, pre-processing used in classification of web pages, document representation technique and performance of the web page classification model. The document representation technique used to represent the web page features is an important aspect in the classification of web pages as it affects the performance of the web page classification model. The integral web page feature is the textual content. Based on the review, it was found that the image based web page classification showed higher performance compared to the text based web page classification. Due to lack of matrix representation that can effectively handle long web page text content, a new document representation technique which is word cloud image can be used to visualize the words that have been extracted from the text content web page.
Survey of Cost Estimating Software Development Using Machine Learning

The estimate of software project costs and efforts is a critical step. Meeting a plethora of diverse criteria, such as resource allocation, cost estimate, effort estimation, time estimation, and the shifting expectations of software customers, is a component of software estimating. Software Engineering Models assist project managers in estimating the cost, delivery time, and personnel that were crucial for software development. SDCE (Software Development Cost Estimation) has long been an exciting and developing area. There are several intelligent models used to predict or estimate the cost of software. The aim of this research is to highlight the algorithms that are used for cost estimation. However, there are no models that work in different circumstances that each practitioner or researcher chooses there are models that use algorithms and those that don't. This makes it easier to determine how much it will cost to build software. Comparing machine learning approaches to traditional software estimating, the ability to anticipate program expense with a high rate of accuracy is possible.
Training of Artificial Intelligence Applications for English Students

This community service is motivated by students in Bengkulu who are considered by many to be untouched by the updated technology used in teaching English. Therefore, it is necessary to provide knowledge about learning technology through training. The purpose of this service activity is to train and realize habituation of English students at Fatmawati Sukarno State Islamic University (UIN FAS) Bengkulu so they are able to apply Artificial Intelligence applications in learning English. In carrying out this activity, the service method is carried out in the form of socialization and training and evaluation. This trial activity is related to UIN FAS. UIN FAS is an institution that has the same vision as the team implementing this community service activity, namely to educate the life of the Indonesian nation. Evaluation activities in community service are related to all things that occur in the process of implementing activities. This starts from the initial stage of preparation which includes field observations to training evaluation. The result of this dedication is that AI applications can be used by TBI UIN FAS students. They were very enthusiastic to take part in the training. In addition, they provide a good perception of the implementation of this AI training to support the English learning process.