Reviewerly/exHarmony huggingface dataset:
A path planning algorithm fusion of obstacle avoidance and memory functions

Abstract In this study, to address the issues of sluggish convergence and poor learning efficiency at the initial stages of training, the authors improve and optimise the Deep Deterministic Policy Gradient (DDPG) algorithm. First, inspired by the Artificial Potential Field method, the selection strategy of DDPG has been improved to accelerate the convergence speed during the early stages of training and reduce the time it takes for the mobile robot to reach the target point. Then, optimising the neural network structure of the DDPG algorithm based on the Long Short‐Term Memory accelerates the algorithm's convergence speed in complex dynamic scenes. Static and dynamic scene simulation experiments of mobile robots are carried out in ROS. Test findings demonstrate that the Artificial Potential Field method‐Long Short Term Memory Deep Deterministic Policy Gradient (APF‐LSTM DDPG) algorithm converges significantly faster in complex dynamic scenes. The success rate is improved by 7.3% and 3.6% in contrast to the DDPG and LSTM‐DDPG algorithms. Finally, the usefulness of the method provided in this study is similarly demonstrated in real situations using real mobile robot platforms, laying the foundation for the path planning of mobile robots in complex changing conditions.
Graph-Based Keyphrase Extraction for Software Traceability in Source Code and Documentation Mapping

Natural Language Processing (NLP) forms the basis of several computational tasks. However, when applied to the software system’s, NLP provides several irrelevant features and the noise gets mixed up while extracting features. As the scale of software system’s increases, different metrics are needed to assess these systems. Diagrammatic and visual representation of the SE projects code forms an essential component of Source Code Analysis (SCA). These SE projects cannot be analyzed by traditional source code analysis methods nor can they be analyzed by traditional diagrammatic representation. Hence, there is a need to modify the traditional approaches in lieu of changing environments to reduce learning gap for the developers and traceability engineers. The traditional approaches fall short in addressing specific metrics in terms of document similarity and graph dependency approaches. In terms of source code analysis, the graph dependency graph can be used for finding the relevant key-terms and keyphrases as they occur not just intra-document but also inter-document. In this work, a similarity measure based on context is proposed which can be employed to find a traceability link between the source code metrics and API documents present in a package. Probabilistic graph-based keyphrase extraction approach is used for searching across the different project files.
A Deep CNN Framework for UAV Intrusion Detection in Intelligent Systems

Unmanned Ariel Vehicle (UAV) s are dealing with several safety and protection issues including internal hardware/software and potential attacks. In addition, detecting UAV anomalies will be a crucial responsibility to defend against hostile enemies and prevent accidents. In this research, we present a UAV and an Automatic Dependent (AD) system using surveillance and Machine Learning (ML) algorithms to analyze data from their detectors in real-time. Proposed Improved Region based Convolutional Neural Network (IRCNN) model used to generate and acquire the characteristics of untreated sensor information and characteristics to facilitate AD. The proposed model creating an Inertial Measurement Unit (IMU) &amp; UAV sensors dataset using cyber security simulation system and Active Learning (AL) identifies aggressions based on the least probable interrogation method. This proposed model enables the identification to efficiently improve the occurrences of unexplained aggressions discovered of IRCNN at reduced labeling cost. A thorough trial showed that IRCNN-AL is effective at detecting unknown threats with frequency improvements of between 9% and 30% on comparison approaches. The AL methodology presented with as few as 1% of a labeled unexpected aggressions.
Secure Smart Grid Data Aggregation Based on Fog Computing and Blockchain Technology

As part of the fourth industrial revolution, it is projected that a huge number of Internet of Things (IoT) devices would be supplied and employed. However, the preexisting Internet may not appropriately address concerns about information security in an unfamiliar setting. Therefore, for the smart grid application, we propose a privacy-preserving data aggregation scheme under a permissioned blockchain based fog system architecture. First, the user data is acquired with a specific frequency through the IoT meters deployed at the industrial, commercial and residential locations. Second, the users leverage the modified ElGamal homomorphic cryptosystem to enable the additive computability and secrecy of their extracted energy information. Here, the encryption is performed using the public key of the district aggregator fog node as well as the user secret share. Fog nodes employ a private enterprise blockchain framework called Hyperledger Fabric to facilitate the user transaction record consistency, validation and traceability for their respective districts. Then, the resulting electricity consumption ciphertext is progressively aggregated throughout the day as it journeys from the end-user grid edge device to the power supply company (PS) in the cloud via the fog layer. We ensure robustness towards major cyberattacks and integrity of the data while in transit through pairing-based modified Boneh-Lynn-Shacham short signature, time-stamping and hashing function (SHA-256). Lastly, demand response is vital to maximize the benefits of diverse parties by promoting a shift of the energy consumption from higher-priced peak hours of the day with maximum power demand to leaner demand periods when power supplies can be relatively inexpensive. It is essential on a broad scale to minimize the blackout risk when demand threatens to surpass supply and trip the power lines. Therefore, our advocated fog cryptosystem premised on a modular blockchain ensures secure and tamperproof data processing from multiple participants in a decentralized setup.
MOOA-CSF: A Multi-Objective Optimization Approach for Cloud Services Finding

Cloud computing performance optimization is the process of increasing the performance of cloud services at minimum cost, based on various features. In this paper, we present a new approach called MOOA-CSF (Multi-Objective Optimization Approach for Cloud Services Finding), which uses supervised learning and multi-criteria decision techniques to optimize price and performance in cloud computing. Our system uses an artificial neural network (ANN) to classify a set of cloud services. The inputs of the ANN are service features, and the classification results are three classes of cloud services: one that is favorable to the client, one that is favorable to the system, and one that is common between the client and system classes. The ELECTRE (ÉLimination Et Choix Traduisant la REalité) method is used to order the services of the three classes. We modified the genetic algorithm (GA) to make it adaptive to our system. Thus, the result of the GA is a hybrid cloud service that theoretically exists, but practically does not. To this end, we use similarity tests to calculate the level of similarity between the hybrid service and the other benefits in both classes. MOOA-CSF performance is evaluated using different scenarios. Simulation results prove the efficiency of our approach.
Application of data models in power grids for loss reduction and disaster anticipation

This paper addresses the critical objective of optimizing power flow within a region, particularly focusing on the Mangystau region, amidst evolving energy demands and the integration of renewable resources. The escalating challenges associated with maintaining both system stability and economic viability underscore the significance of this research, as suboptimal power flow conditions can exacerbate climate change. To expedite the solution to the optimal power flow problem, machine learning algorithms are explored. Initially, load data from the region is analyzed, and various supervised learning algorithms are tested using simulation data to predict power flow patterns. The primary concern in the Mangystau region lies in the aging infrastructure of oil companies, which operates under suboptimal conditions. This study employs neural networks in Matlab to simulate the electrical system’s parameters, unveiling the intricate relationship between optimal system parameters and those of the examined system. Comparing these results with analytical grid modeling, the study reveals that system optimization aligns with target values, particularly concerning optimal receiver replacement schemes.
Inclusive education – a means of promoting social equality and development of people with SEN in higher education institutions

PROBLEMS AND PROSPECTS OF TRAINING IN HIGHER SCHOOL: PEDAGOGICAL, PHILOLOGICAL, PSYCHOLOGICAL AND INTERCULTURAL ASPECTS The scientific section is aimed at analyzing the implementation of inclusive education in the Educational and rehabilitation higher education institution Kamianets-Podilskyi State Institute, as well as the factors of successful socialization and adaptation of students with special educational needs. The study analyzed various approaches to inclusive education, considered the issues of organizing the educational process, supporting students with special needs and developing their socialization skills and adaptation to learning conditions, inclusive culture in higher education institutions, student-centered learning, formation of an individual educational trajectory, and employment. Scientific research shows that successful inclusive education is based on an individual approach to each student, taking into account its special needs, promoting its socialization and adaptation to learning conditions. This approach ensures the effectiveness of the educational process and the training of qualified specialists with special features in working with people with various needs.
A study on how to improve the accuracy of auto error correction from voice to text

Voice recognition application has been widely used in peoples daily lives. Usually, if people find it inconvenient to directly play the audio in public, or it is too noisy for them to listen to an audio message, they would probably use the voice recognition application in their mobile devices to translate the voice into text, so that they can understand the message clearly. However, there are still various errors and problems occurring during the usage of this common application, for instance, it can be hard sometimes to translate the sound into words correctly. This usually happens when the speaker speaks too fast and pronounces unclearly. Besides, some other factors, such as environmental noise, transmission channel quality, and the radio equipment, would also cause this problem. This paper mainly analyzes the causes of the inaccurate translation problem and some potential improvements to make this function more perfect. In conclusion, to solve this problem, the parity check matrix is a good way, since it can check whether the digits behind each word are still correct. After doing so, the digits will be changed into the correct words syntactically. However, even though the words are syntactically correct after applying the parity check matrix, they might not be semantically correct. Therefore, Levenshtin distance and Latent Semantic Analysis can be used to analyze the hidden meanings of words and sentences, so as to find the best suitable words to change.
Design and Development of Management Information System on Website-Based Jempol Optical Shop Using the Laravel Framework

The management of inventory data and reports at the Jempol Optical shop is still manual, starting with the input of goods data relating to glasses, frames, lenses, and other optical accessories data. In addition, the preparation of sales reports is also still manual. It takes a system to collect data on optical goods and to prepare sales reports. This study aims to create a software system that can provide solutions with a website-based management information system using Laravel. The method used in this study uses SDLC (System Development Life Cycle), including literature studies to get references related to the system to be made, needs analysis of the Jempol Optical system to determine system requirements, design of the Jempol Optical system, manufacture of the Jempol Optical system, and system testing of Jempol Optics Website used for black box testing and usability testing. The result of the system created is a management information system with 2 actors (admin and employees) which has 18 features. In black box testing, the system can be used smoothly, and all features can be used according to their functions with a percentage of 100% success. The results of usability testing carried out by shop owners and employees of the Jempol Optical shop, the application is good from the results of tests carried out with a result of 4.2 out of a maximum value of 5. From the test results by giving several statements, it can be concluded that the user agrees with the system that has been made.
Analysis of Academic Social Networks in Indonesia

Social network analysis to detect communities in social networks is a complex problem, this is due to differences in community definitions and the complexity of social networks. One of the social networks for researchers is the academic social network (ASN). We define the relationships between nodes in ASN into two forms, namely interconnection relationships and interaction relationships. Interconnection relationships are researchers' social relationships that are formed from similarities in discipline between researchers, while interaction relationships are researchers' social relationships that are formed through interactions carried out regarding joint article publications. This research aims to measure the social interactions and social interconnections of researchers in Indonesia using the social network analysis method. The ASN data used in this research comes from the academic social network Researchgate. This research produces information on the social networks of scientific groups in Indonesia and a framework for analyzing researchers' social networks using dual identification community mode which has been able to find and understand the structure of the research community based on records of interactions and interconnections with ASN with similarity values in both forms of network connections 85.9%.
Implementation of Image Denoising based on Backward Stochastic Differential Equations

In this paper, we give the implementation of an image denoising algorithm based on backward stochastic differential equations. In our algorithm, we consider two stochastic processes. One of them has values in the image domain and determines pixels that will be involved in the reconstruction, the second one has values in the image codomain and gives weights to values of pixels. The reconstructed image is characterized by smoothing noisy pixels and at the same time enhancing edges. Our experiments show that the new approach gives very good results and can be successfully used to reconstruct images.
The Effect Of Problem-Based Learning Model Assisted By Google Sites On Students' Scientific Literacy Skills At Elementary School

This study aims to analyze the effect of the Problem-Based Learning model assisted by Google Sites on students' scientific literacy skills by comparing the scientific literacy skills of experimental and control classes at SDN 067251 Medan Deli. This type of research is a quasi-experimental design with Non equivalent Pretest-Posttest Control Group Design. In this study, The population was all fifth-grade students, 40 students in total. The instrument used in this study is a science literacy test. The results showed the sig. (2-tailed) 0.00 &lt; 0.05, it means Problem-Based Learning assisted by Google Sites affects students' scientific literacy skills.Keywords: Problem-Based Leraning, Google Sites, Scientific Literacy.
The Rise of Metaverse: A Comprehensive Review of Trends, Challenges and Future Prospects

The metaverse, an immersive virtual universe in which physical and digital realities converge, has emerged as a transformative pressure reshaping human interaction, era, and society. This review paper delves into the evolution of the metaverse, tracing its origins and pivotal tendencies
Human Aspects in Software Engineering: Challenges, best practices and future direction

Software engineering, at its core, is a collaborative effort pushed by way of human ingenuity, verbal exchange, and innovation. This overview paper gives a comprehensive exploration of the pivotal position played by using human factors in software program engineering approaches.
Augmented Reality for Enhanced Learning Experience: A comprehensive review

In the area of education, Augmented Reality (AR) era has emerged as a transformative force, reshaping traditional mastering methods and considerably improving the academic experience. This complete evaluate paper delves into the multifaceted landscape of augmented reality in schooling, presenting valuable insights for educators, researchers, and policymakers
Hyperparameter Optimization for Convolutional Neural Networks using the Salp Swarm Algorithm

Convolutional neural networks (CNNs) have exceptionally performed across various computer vision tasks. However, their effectiveness depends heavily on the careful selection of hyperparameters. Optimizing these hyperparameters can be challenging and time-consuming, especially when working with large datasets and complex network architectures. In response, we propose a novel approach for hyperparameter optimization in CNNs using the Salp Swarm Algorithm (SSA). Based on the natural behavior of mollusks, SSA mimics the collective intelligence that governs feeding and navigation. Taking advantage of SSA's unique properties, our research thoroughly explores the hyperparameter space. This exploration aims to identify the algorithm that maximizes CNNs performance. This paper presents the architecture of the SSA-based framework for hyperparameter optimization and compares it to other established optimization techniques, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). We also present experimental results using the MNIST dataset, achieving an impressive classification accuracy of 99.46%. This case study not only contributes to the fields of deep learning and hyperparameter optimization by demonstrating the effectiveness of SSA in optimizing CNNs, but it also provides benefits to researchers and practitioners who are looking for optimal hyperparameter configurations for CNNs in a variety of computer vision applications. We also evaluate the scalability and robustness of our proposed method in the context of different CNNs structures. The insights we gained highlight SSA's potential for addressing challenges related to hyperparameter optimization.
ANALISIS TATA KELOLA KEUANGAN SMAIT BUAHATI ISLAMIC SCHOOL JAKARTA BERDASARKAN PERATURAN PEMERINTAH REPUBLIK INDONESIA NOMOR 48 TAHUN 2008

This study aims to understand the process of preparing the School Budget Activity Plan (RKAS), budget realization, preparing financial reports at SMAIT Buahati Islamic School Jakarta, and understanding the financial governance of SMAIT Buahati Islamic School Jakarta. The research method used is qualitative with a descriptive approach. The data collection techniques used are interviews, observation, and documentation. The results showed that the planning process of school financial governance sourced from school fund finances and BOS funds begins with holding a limited meeting with the process of preparing RKAS and RAB, based on the plans needed by the school and the technical instructions for school BOS. The realization of financial governance at SMAIT Buahati Islamic School Jakarta, the principle of efficiency has not been followed properly. The preparation of financial reports is carried out routinely by making an Accountability Sheet (LPJ) for each activity that has been carried out.
Aplikasi Canva Sebagai Media Pembelajaran yang Menarik di Era Digital

In the current digital era, all elements are required to adapt to technological developments, one of which is in the world of education. The success of education cannot be separated from interesting learning media. Especially in the current era, there are many applications that can support the learning process. One of them is the Canva application as explained in this article. The aim of writing this article is to make educators more aware of technological advances, especially the use of the Canva application in learning in the current digital era. In this article the author uses a library research method, namely by collecting data through various library sources such as journals, books and other scientific sources that are relevant to the topic discussed in this article. The results of this research are that the Canva application is an interesting application to use as a learning medium. Because in the Canva application there are various interesting features that support the learning process.
Analisis Dan Perancangan Aplikasi Monitoring On The Job Training (OJT) Smk Negeri 1 Boyolali Menggunakan Framework CodeIgniter

On Job Training (OJT) is the application of knowledge and skills acquired, and seeing their relevance in the world of work is an educational activity carried out by the company. To train students to improve their quality and be able to know the situation of the world of work is the goal of OJT. OJT activities are beneficial for agencies and students implementing OJT; meeting the needs of non-permanent employees with academic insight is one of them. PIECES analysis was carried out to identify problems at SMK NEGERI 1 BOYOLALI. Several conclusions can be drawn after going through the testing phase of the Monitoring on the Job Training (OJT) Application at SMK NEGERI 1 BOYOLALI. This system can assist field supervisors and school supervisors in carrying out all management, monitoring, reporting and assessment activities on the Job Training activities (OJT) and helping students manage attendance data and report on the Job Training (OJT) activities. Based on the explanation above, this research's focus is to build a website-based OJT using the CodeIgniter framework.