Reviewerly/exHarmony huggingface dataset:
An innovative cluster-based prediction approach for mass solar site management

The scarcity of energy resources and global warming over the past few decades have prompted the widespread adoption of renewable energy sources. Among the potential renewable energy sources, solar energy has emerged as one of the most promising renewable energy sources. However, the uncertainty and fluctuations of solar power generation create negative impacts on the stability and reliability of the electric grid, planning of operation, economic feasibility, and investment. Therefore, accurate prediction of solar power generation is crucial to ensure the stability of the power grid and promote a large-scale investment in a solar energy system. A large number of research studies have been conducted on predicting solar power generation under different perspectives. However, no existing study analyses and predicts power generation of multi-solar energy sites by only one prediction model. The integration of multiple sites into one predictive model will reduce the number of required models for each site, thereby saving the computing resources and required calculation time. This paper proposes a novel methodology to group multiple solar sites and develop an integrated model by using a machine learning algorithm to predict power generation of each group. Firstly, the K-means clustering algorithm is utilized to cluster multiple solar sites which have similar power generation properties into one group. Then, a machine learning algorithm has been developed to predict power generation in a computationally fast and reliable manner. The proposed approach is verified by the real data of 223 solar sites in Taiwan. The experimental results show that the training time can be reduced by 93.2% without reducing the accuracy of the prediction model. Therefore, the cluster-based prediction approach gives better performance as compared with existing models.
Detection of Malaria Parasites in Blood Samples Using Image Processing Techniques

Malaria, the most lethal disease brought on by mosquito bites, used to impact a lot of people. To identify malaria, blood samples from the patient are collected and sent to a lab, where they are inspected under a microscope and tested for different chemicals. Before the patient receives the necessary treatment for their health problems, which will lead to their death, the test sample result will be older than a week. Despite technological advances, this illness still affects residents of rural areas, and it still takes time for testing and results to be returned. In this work, Image analysis is the most effective method for quickly diagnosing illness. Images of thin, discoloured blood smears were created for this work. Along with features that are similar to features used by human technicians and are based on prior knowledge of the classification problem, the geometry, texture and colour of the cells are the image features and based on the image features the parasites are also created after they have been obtained, pre-processed, and applicable features removed. It separates true positives before determining the infection type.
Construction of quantum MDS codes from generalized Reed–Solomon codes

Quantum maximum-distance-separable (MDS) codes play an important role in the quantum codes. The previous quantum MDS codes had been constructed according to [Formula: see text] is odd or even. However, such classification omits to consider some special categories of quantum MDS codes. Because of this, we will discuss the other classifications of [Formula: see text]. In this paper, we construct some new [Formula: see text]-ary quantum MDS codes from generalized Reed–Solomon codes by using Hermitian construction, and prove that these quantum MDS codes have minimum distance greater than [Formula: see text], where [Formula: see text].
IN MEMORIAM: Reconstruction of the Creative Heritage of Sergei Fedorovich Denisov

The article is devoted to the reconstruction of the creative heritage of Doctor of Philosophical Sciences, Professor Sergei Fedorovich Denisov, with an emphasis on his works on the philosophy of science. The study also includes individual biographical information that is significant for understanding some aspects of the scientist’s personality that influenced the formation and development of his philosophical ideas. The article reveals the main provisions of his concept of the unity of the philosophy of science and philosophical anthropology, the role of science in culture and its significance in human existence. The development of the central idea of all creativity is monitored — the saving role of science in unity with philosophy and other areas of culture for the future of mankind.
Comparison of Learning Results for Solving Mathematical Problems in Hybrid Mode (With and Without a Computer)

In the context of blended learning, it becomes relevant to search for the optimal ratio of theoretical and practical training using computer simulations and manual calculations. The author gives the results of the corresponding analysis on the example of studying the academic course of probability theory and mathematical statistics. The target audience for this article are university teachers and moderators of online courses, although high school teachers and graduates of pedagogical universities may also be interested in reading due to the appearance of elements of probability theory and mathematical statistics in the school mathematics course.
Assessment of the Justification Degree of Scientific Facts in the School Physics Course

The problem of the justification (validity) degree of scientific facts in the school physics course is analysed, the relevance of which is due to the need for the formation of evidence-based thinking among schoolchildren. More than 150 facts of the existence of objects, phenomena and functional dependencies expressed using formulas are written out. It is shown that the more convincing the substantiation of the factual statement is, the simpler and more accessible it is for the student. A methodology for assessing the credibility of experimental and logical-mathematical justification is proposed, and appropriate scales are developed. The method of assessing the semantic complexity of scientific facts by summing up the complexities of terms that make up the corresponding factual statement is considered. The semantic complexity, persuasiveness (or simplicity) of experimental and logical-mathematical substantiation of the selected facts are evaluated. It is established that: 1) an increase in the persuasiveness of the experimental substantiation of facts is accompanied by a decrease in their semantic complexity; 2) an increase in the persuasiveness of the experimental substantiation of formulas is associated with an increase in the persuasiveness of their logical and mathematical justification; 3) for the average values calculated for each section of physics, an increase in the persuasiveness of the experimental justification of facts leads to an increase in the persuasiveness of the logical and mathematical substantiation and a decrease in semantic complexity.
An Improved Chaos-Based Particle Swarm Optimization Algorithm

Particle swarm optimization (PSO) is a well-known swarm intelligence algorithm widely used to solve various numerical optimization problems. However, the PSO can easily fall into local optimum when it solves complex optimization problems. An improved chaotic particle swarm optimization algorithm (ICPSO) is proposed to address this problem. A chaotic perturbation strategy is used to enhance the algorithm's ability to explore the global. Besides, an escape strategy is utilized to increase the diversity of the particle population. The performance of the ICPSO algorithm is verified by testing five benchmark functions, and the results show that the algorithm has good global exploration and convergence ability.
DETECTION OF ANOMALOUS BEHAVIOUR IN AN EXAMINATION HALL TOWARDS AUTOMATED PROCTORING

The anomalous behaviour is hard to be detected simultaneously in a complex scene such as detecting abnormal movements of examinees in examination rooms.Modelling activities of moving objects and classifying them as normal or anomalous is a major research problem in video analysis.In this paper, we make use of the of neural networks and Gaussian distribution to help solve this problem by building a prototype of a monitoring system that consists of three stages; face detection using haar cascade detector, suspicious state detection using a neural network and lastly anomaly detection based on the Gaussian distribution.The main idea is to decide on whether the student is in a suspicious state or not using a trained neural network and then decide that a student performs an anomalous behaviour based on how many times he was found in a suspicious state in a defined time duration.The complete system has been tested on a proprietary data set achieving 97% accuracy with 3% false negative rate.
BEACON: THE DESKTOP VOICE ASSISTANT

In the new technology of artificial intelligence, we can do things that we couldn't do before.It has become a blessing for everyone in this 21 st century.This is the voice assistant which can accept the voice command and perform the task on it.This project is very user-friendly.That means this system is easy to operate.This technology can interact on machines and rapidly spread in laptops, computers ,etc.Speech Recognition, Pywhatkit, Datetime, Web browser, Os, yagmail, googletrans and pyttsx3 are the Python packages used in this project.Another advantage of this software is that it's time efficient, the user can get desired output in less span of time.It works continuously.It can accept the command not only in English language but also in Hindi language so it is a multilingual system.
WEB APP FOR COLLEGE CAMPUS PLACEMENT SYSTEM

The "WEB APP FOR COLLEGE CAMPUS PLACEMENT SYSTEM", is a web-based system designed to provide information to the Training and Placement (T&P) department of a college.The main objective of this project is to develop a web application that supports the T&P department in managing their tasks.Traditionally, most of the work carried out by the T&P department is done manually, and this project aims to automate these processes to make things easier.The web application will be accessible to students, faculty, and the T&P department with proper login credentials.The primary objective of this application is to facilitate the management of student information related to placement recruitment.It enables students to check their eligibility for upcoming placement drives based on their CGPA.The application also provides access to question papers from various companies and technical materials.The main advantage of this project is that it simplifies the management of the T&P department and makes it easier for students to access relevant information regarding placements.By using a database, this web application will ensure that information is accessible from anywhere, both within and outside the organization.
Trajectory planning for complex shaped spacecraft proximity based on critical safety curve and disturbed fluid

Recent research on on-orbit service rarely considers the complex shapes of servicing spacecraft (chaser) and the target simultaneously, which could cause collision problems in ultra-close-range proximity. This paper presents a novel trajectory planning method via critical safety curve (TPCSC). The core of the framework is the construction of collision avoidance constraints called critical safety curve (CSC) using geometrized mathematical methods. CSC has good mathematical properties and innovatively combines the complex shape and relative attitude constraints of two spacecraft. Subsequently, TPCSC proposes an improved disturbed fluid method based on CSC that can handle non-convex constraints to generate smooth approach trajectories. Finally, the framework can achieve ultra-close-range proximity between spacecraft with complex shapes under complex non-convex constraints. The simulation results indicate that the planning of TPCSC is rapid, and the formed trajectory is safe, smooth, and easy to track.
An Non-Local Means based denoising methods for enhanced spatial resolution products of Microvave Radiometer

During data collection, the Microwave Radiation Imager (MWRI) on the FY3D satellite has several factors that reduce the spatial resolution of the data. However, in complex degradation situations, established deconvolution techniques such as wiener filtering and Backus-Gilbert improve spatial resolution at the expense of noise amplification. In this study, The Non-Local Means(NLM) technique is used to mitigate the amplification noise introduced during the deconvolution process. Experimental results on simulated and actual MWRI images have demonstrated the validity and effectiveness of the method.
FLOW MEASUREMENTS USING VISUALIZED COLOR IMAGES

This paper presents some flow measurement systems based on color image information. The first one is a quantitative thermal flow visualization system to obtain simultaneously both velocity and temperature distributions. Flow visualization is accomplished by the use of thermo-sensitive liquid crystal tracers. A correlation method gives the 2-D velocity vector distribution. An algorithm for the color-to-temperature calibration is presented to obtain the temperature distribution.<br> The second one is a 3-D flow velocity vector measurement system using tracer images visualized by a color spectrum. In this system, tracer particles are visualized by a color spectrum slit method using a prism. The color of a tracer particle can determine its position in one direction. In the other two directions, the tracer particle can be tracked in consecutive frames of the 2-D tracer image by using a correlation and a particle tracking method. A 3-D velocity vector is consequently obtained by a combination of the color spectrum with the methods for 2-D velocity vector measurement.<br> The last one is another 3-D flow velocity vector measurement system using a spatio-temporal correlation method. The method needs two tracer particle color images observed at a short distance in the flow direction. Each of the images is a time series. The spatial correlation gives 2-D information on a 3-D velocity vector. In addition, the temporal correlation gives information on the third dimension. A 3-D velocity vector is consequently obtained by calculating the spatio-temporal correlation between the two images.
Advances in Tangible and Embodied Interaction for Virtual and Augmented Reality

Virtual Reality (VR) and Augmented Reality (AR) technologies have the potential to revolutionise the way we interact with digital content [...]
MEDIA TRANSMISSION USING LI-FI

Li-Fi is a wireless communication technology that uses visible light to transmit data, including media such as audio and video.This technology offers several advantages over traditional RF communication technologies, including higher bandwidth, greater security, and reduced interference.Li-Fi uses LEDs to transmit modulated data through the flickering of light, providing high-speed data transfer rates of up to several gigabits per second while avoiding potential health concerns and radio interference issues associated with RF technologies.Li-Fi technology has applications in various fields, including smart lighting, automotive, healthcare, and entertainment industries.Li-Fi-enabled smart lighting systems can be used to transmit data, providing enhanced indoor navigation and personalized services such as location-based advertisements.Li-Fi can be used to provide high-speed internet connectivity to vehicles, enabling real-time traffic information and advanced driver assistance systems.In healthcare, Li-Fi technology can be used to provide secure and highspeed data transmission between medical devices and healthcare professionals, enabling remote consultations and telemedicine.In the entertainment industry, Li-Fi can be used to provide high-quality audio and video streaming, enabling immersive experiences such as augmented reality and virtual reality.
A Systematic Mapping Study of ISO/IEC 29110 and Software Engineering Education

This article presents a study of the publications made on the ISO/IEC 29110 standard in the university context, especially from the perspective of software engineering education. ISO 29110 is a life cycle profiles for very small entities on systems and software engineering standard, published in many parts. ISO 29110, since its publication in 2011 and its continuous evolution to these days, is the subject of study in different contexts, with education being a relevant axis. Considering, that software engineering education has implications in the software industry in emerging countries, it is necessary to identify and consolidate the work done in this context. In this study, the main research question was what researches have been done at ISO 29110 in the training of software engineers? To answer this question, a systematic mapping study (SMS) was performed. In the SMS, 241 articles were obtained with search string and 17 of them became as primary study after a process selection. Based on these studies, it was possible to determine that the software engineering Basic profile of ISO 29110 and its processes (Project Management and Software Implementation) have been the most studied. Besides, it was identified that project-oriented learning and gamification techniques have been the most used ISO 29110 learning strategies in the training of future software industry professionals.
A Systematic Mapping Study on Process Improvement in Software Requirements Engineering

Software analysis is the process carried out to obtain requirements that reflects the needs of a client's stakeholders and that allows the construction of a software product that meets their expectations. However, it is also known as a process where many defects are injected. In this context, although process improvement has contributed to the software industry, in the case of software requirements it needs to be studied to determine the improvements obtained and established models. In the literature reviewed, a similar mapping study with 4 research question was identified and used as a reference. The objective of this work is to structure the available literature on process improvement in the software requirements engineering (SRE) domain to identify the improvement phases, paradigms, principles, and established models. For this purpose, a systematic mapping study (SMS) was carried out in the most recognized digital databases. The mapping carried out recovered a total of 1,495 studies, and after the process, 86 primary studies were obtained. In this SMS had established and answered 13 research questions. The different models that are applied throughout the software requirements engineering process were identified, and accepted studies were classified and findings on SRE process improvement were collected. The most used models are CMMI, Requirements Engineering Good Practice Guide (REGPG), and ISO/IEC 15504. Also, 62% of accepted studies are of the proposal and evaluation type; that is, they propose a framework and study the implementation of a proposal in one or more case studies respectively. On the other hand, it was found that most of the studies focused on the process improvement analysis phase. Likewise, in contrast with a previous study, proposal and validation type of studies increased in 9 papers each one from 2014 to date. This shows the interest of the scientific community in this domain.
Sentiment Analysis Online Shop Social Media On Tik-Tok App With Naïve Bayes Classifier ( NBC )Method

The rise of Social Media gets all the conveniences in communicating like in real life.The activities that users of social media can engage in also vary, ranging from searching for information about products, comparing them with other products to making online purchases.Online stores are widely used by consumers because they are easy and fast in the transaction process, including the TikTok application which has a new feature, TikTok Shop released last year on April 17, 2021.This application is widely used among teenagers.Sentiment analysis is a technique that can extract text data to obtain information about positive or negative sentiments.This study aims to create an application that can classify sentiments into positive or negative sentiments to determine how interested Indonesian people are in using the TikTok application when doing business or when interacting and dealing with slang in TikTok Shop sentiment analysis.The opinion data of TikTok stores in this study was taken from responses on Twitter.This research was conducted using the Naïve Bayes Classifier (NBC) method.The number of tweets examined in this study was 1230 tweets.With the number of negative words as many as 567 data and positive as many as 663 data.Testing is performed by implementing the Python programming language.The results of classification using Naïve Bayes Classifier algorithm found an accuracy value of 74.32%.
IMPLEMENTING WELLNESS PROGRAMS IN STRATEGIC MANAGEMENT OF HUMAN RESOURCES

Recently, several studies have appeared looking at health and wellness programs and their impact on the health of workers and their contribution to human resource management strategies, but these studies are still limited, especially in Jordan.These programs improve the job performance of workers in Jordanian companies.This study aims to examine how strategic human resource management can respond to environmental changes using corporate wellness programs.For that, this study conducted a systematic review based on answering the question: How can human resource management strategies use wellness programs?To answer this question, it was necessary to refer to relevant studies and gather evidence of the efficiency of health and wellness programs used in companies.The study reached the following conclusion: health and wellness programs have great potential and effects in improving the performance of employees in companies, and these capabilities are more effective when developing them within a strategic framework for human resources because human resource management strategies link each of the employee's interest with the interest of the company.
COMBINING DIGITAL FORENSICS WITH TRADITIONAL SAFETY MEASURES TO IMPROVE DATA SECURITY IN BANKING

This study aims to investigate a new method of forensic cybersecurity to improve data protection in the banking industry.The document is based on several experiments that demonstrate the effectiveness of digital forensics, combined with traditional banking security measures, as a reliable data security method.Banking data is a prime target for most cyber attackers, and securing this data has always been one of the most critical challenges in the banking industry, especially in modern society, where numerous money transfer systems have emerged.This research suggests and explains a more specialized security system that employs disk imaging, data carving, memory analysis, and network forensics to detect, investigate, and counteract cybercrimes against banking data.The authors illustrate how these techniques can combine with traditional security measures to improve banking data security and enhance forensic investigation processes.The method allows banking firms to improve the efficiency of incidence response, protect confidential information, improve risk management and adhere to government regulations.The researchers employed standardized scientific experiments that proved the effectiveness of this security enhancement method.Result analysis indicates that by implementing this method of forensic cybersecurity, bankers can reduce losses due to cyberattacks by at least 40%.They can also improve their customer"s trust, consequently maintaining a high client retention rate, helping firms boost profits by up to 30%.Besides its benefits to financial institutions, this research project contributes to the scientific literature on digital forensics and cybersecurity.Additionally, it provides practical solutions and suggestions that can be used in other data security subsectors.