Reviewerly/exHarmony huggingface dataset:
Improvement Deep Leaning Network Performance by Increasing Its Complexity

Performance evaluation is an important aspect of the machine learning process. However, it is a complex task. Therefore, it needs to be conducted carefully for the application of machine learning to image classification or other domains to be reliable. In this work, we have proposed a CNN (Convolutional Neural Network) classifier from the results of the current state of the art using a certain ML (Machine Learning) algorithm applied to the problems of classification of images into components of the results of several different models, going from the simplest to the most complicated possible. And at each stage, we analyze the faults by observing the behavior of each model and increasing the complexity of our classifier by integrating several parameters, such as: changing the learning algorithm, adding a layer, dropout, batch normalization, and global average pooling. After finding the architecture that suits us best, we get the maximum performance. The application of our classifier on the Cifar-10 and Cifar-100 datasets shows us the robustness of our approach since it achieves one of the best performances recorded on these datasets.
The Role of Blockchain Technology in the Management of Waqf

Waqf has historically been a vital institution in Islam in fulfilling religious needs and improving social welfare. Its effectiveness in medieval and modern Islam is evident. However, this institution is seemingly not without inefficiencies. Public trust and contributions are also low due to the opacity of the accounting and accountability system. Scholars have proposed adopting the blockchain technology to mitigate these issues. The implementation of the blockchain technology is to provide a solution for waqf organisations towards offering transparency to their donors and building trust to gain more support towards the waqf organisation’s mission and work. The blockchain technology manages information within the waqf system by enhancing the aspects of transparency and reliability in how funds are collected, managed, and distributed. This study has two objectives: first, to look into the technological role of blockchain in managing waqf assets in ensuring the most efficient way of financing waqf projects. Second, this research is aimed at exploring and understanding the blockchain technology regarding the issues of trust and accountability in waqf organisations in Malaysia. The study methods consisted of content analysis and an interview. The content analysis focused on publicly accessible information online such as home pages, websites, user interface pages, and annual and financial reports. The semi-structured interview was carried out with a representative of a waqf organisation who has extensive knowledge of these technologies. This article is an effort to contribute to the body of knowledge by exploring blockchain technology’s potential applications and their limits.
Sampling-Based Path Planning Algorithm Considering Moving Obstacles Based on RRT*

Path planning is an essential part of planning in robotics systems. It generates robot trajectory that avoids collision with obstacles. Besides static obstacles, we need to also consider moving obstacles in a dynamic environment. In multi-agent systems, these obstacles often have known trajectory that must be avoided. Related works in sampling-based algorithms proposed a re-planning mechanism to consider the moving obstacle. However, those algorithms assume that the obstacles are fixed in each re-planning step. The paper proposed a novel algorithm based on RRT* that considers moving obstacles that consider known spatial-temporal trajectory of moving obstacles. The simulation result shows that the proposed algorithm can generate collision-free trajectory with comparable performance with baseline RRT*.
Ensemble of Fully Convolutional Neural Networks with End-to-End Learning for Small Object Semantic Segmentation

Small object segmentation is important due to its vast application potential for biomedical and surveillance. This paper describes several ensemble methods that combine different fully convolutional neural networks (FCNNs) for accurate semantic segmentation of aerial images. One problem with a single model for accurate small object segmentation is that the small object classes are suppressed by large object classes, which can increase overfitting problems and hinder generalization performance. Furthermore, using the single model for segmentation has particular strengths and weaknesses, and the segmentation accuracy changes with the different features used for training. Thus, this paper describes several ensemble methods that combine multiple fully convolutional networks for accurate semantic segmentation of unmanned aerial vehicles (UAVs) images. The proposed method starts with performing object localization for each single FCNN model. After that, each model’s channels of feature map outputs are combined to guide the training of the ensemble segmentation network. We have performed experiments on UAVid benchmark dataset. The results show that the proposed ensemble approach outperforms the single model that directly uses the channels of the single model’s feature map outputs.
Using Artificial Intelligence (AI) in the Management Process

This study examines the use of artificial intelligence (AI) in administrative processes. The results reveal that the adoption of AI mechanisms within organizations is worth investing in, as the use of information technology benefits the management of administrative functions and relationships. Using AI enhances decision-making at all levels within an organization, and the results demonstrate that expert systems are the embodiment of AI that can serve the quality and effectiveness of administrative management. This study recommends the adoption of expert systems and AI models in various business organizations and public bodies to improve the decision-making process.
The Simulation of Adaptive Coverage Path Planning Policy for an Underwater Desilting Robot Using Deep Reinforcement Learning

Sewer sedimentation tanks accumulate a lot of sludge all year round and require a lot of labor and resources to clean up. Such underwater cleaning has many difficulties in cleaning works. Desilting robots are widely used in sludge cleaning to reduce labor. However, since this method is usually planned based on manual experience, the operation is inefficient, not intelligent, and requires a certain amount of labor. To enable the desilting robot to intelligently plan rational cleaning paths, it uses the complete coverage path planning (CCPP) algorithm. Existing CCPP cannot meet the requirements of various environments. Therefore, in this paper, we propose to use the deep reinforcement learning (DRL) algorithm to learn the sewer sedimentation tank environment and to find the optimal cleaning path. Experiments show that 2000 episodes are trained in a simplified simulation environment using deep Q network (DQN) and double DQN (DDQN), respectively. DQN only got a cumulative reward of 1000 in the 1228th episode, while DDQN got a cumulative reward of 8000 in the 343rd episode and completed the task. Therefore, the slag removal robot using DDQN as the control strategy can adaptively complete the CCPP problem.
Forecasting Demand in the Commodity Market of Food Products Using Neural Networks

When opening a new commercial enterprise, a correct predictive estimate of the demand for the goods of this firm should be given. This work is devoted to the use of neural networks for demand forecasting, by determining the demand elasticity coefficient, which will allow commercial enterprises to give a correct predictive estimate of the demand for their goods in the commodity market. It is known that forecasting is a probabilistic scientifically substantiated judgment about the trends of the phenomenon under study in the future. On the other hand, it should be noted that: - demand is an important factor in the study of the commodity market; - when opening a new commercial enterprise, a correct predictive assessment of the demand for the goods of a given company in the commodity market should be given; - demand and supply appear on the commodity market not spontaneously, but are formed and act according to the relevant laws; relevant laws; - human experience and intuition are among the main sources of information for forecasting demand and future market characteristics; - it is impossible to obtain statistical data in advance for a certain period of time. The paper considers the features of forecasting demand in the commodity market and substantiates the relevance of using neural networks (which is more adequate to the market model in the presence of uncertainty factors) for their forecasting than the methods of extrapolation of trends that are actively used in solving economic problems. The issue of choosing the type of neural network for approximating and predicting the elasticity coefficient of demand for food and non-food products is considered.
Simulation of Fractal Kinetics of Thermooxidation of Polymer Melts Based on Fractional Differential Equations

The basis for applying the theory of fractional derivatives to describe the thermo-oxidative destruction of polymer melts in fractal analysis is the property of a fractal object (macromolecular tangle) in Euclidean space with a dimension of greater unit, consisting in the fact that the fractional part of its fractal dimension coincides with the fraction of the (fractal) fractal retained during evolution (destruction). In this connection, the paper proposes equations of fractal kinetics of the thermooxidation reaction of polymers in the form of differential equations of fractional order equal to the fraction νd of part of the reactive groups of the polymer that are not subjected to degradation, with reaction order n > 1. Small νd has an auto-slow mode (at a rate less than the time derivative of the first order), and near-one values of νd is an auto-accelerated oxidation mode.
The effect of antioxidant aloe vera as a screen protector to decrease light intensity

The emergence of the Covid-19 pandemic had a major impact on education, resulting in an increase in the use of laptops. In order to minimize the acceleration of macular degeneration, it is necessary to develop technology to protect the surface of the screen, namely using the antioxidant aloe vera. It contains lutein and zeaxanthin. Aloe vera have anti-inflammatory effects and are found in the lens of the human eye. The purpose of this study was to prevent the occurrence of macular degeneration. The simple method used was conducted by aloe vera sonication following by mixing the aloe vera using TPU (thermoplastic polyurethane). All experiment was carried out through factorial design with two variables, i.e weight ratio of TPU and aloe vera and curing temperature. This research was statistically carried out by varying the weight ratio thermoplastic polyurethane/aloe vera (1-4) and temperature (30-40oC). The result showed the model was released significant in ANOVA assesment. Furthermore, testing the luxmeter as many as 13 screen protector variables then processed with a design expert application, obtained F-value data of 6.83 and P-value of 0.0127 (less than 0.0500).
Toward Z-Number-Based Classification of Dataset

Nowadays, a lot of classification techniques including probabilistic and fuzzy methods exist. The works devoted to dealing with fusion of probabilistic and fuzzy uncertainties of information are scarce. In view of this, partial reliability of information that stems from uncertainty and complexity of real datasets is of interest. Prof. Zadeh introduced a concept of Z-number to describe reliability of information under fuzziness and probabilistic uncertainty. In this work, an approach to Z-number-valued classification of dataset is outlined. The is aim is to describe partial reliability of knowledge expressed by classification. A benchmark data set is used is considered to illustrate the proposed approach.
Decision Making on Students’ Performance Estimation

A frequent problem in the awarding of scholarships is the identification and proper selection of students who are eligible for scholarships due to the limited quota. In the process of determining scholarship, we use the criteria which evaluated subjectively and some students who have the ability or value that is not so different. In this case, the application of fuzzy logic theory is an effective tool. Thus, fuzzy logic allows to describe the knowledge of experts. This paper devoted to the usage of fuzzy logic on scholarship evaluation. For this purpose, student evaluations of 4 experts on 5 criteria are used and the fuzzy approach is applied proposed by author. The basic of represented method is Zadeh’s fuzzy set theory for aggregation of properties. The objective of this study is to detect the chances of students to get a scholarship and to rank the students according to the evaluation of 5 criteria.
Appling Fuzzy Inference Logic System to Dynamic Model of Gross Domestic Product (in Case of Azerbaijan)

The main criteria and source of economic development is economic growth. Economic growth is a sustainable increasing tendency of the main indicators of national theory production – gross national income (GNI) and gross domestic product (GDP). Furthermore, absolute value and per capita growth are also considered. In economics and statistics, various indicators are used to measure the amount of national production. The most important of these is gross domestic product. GDP is expressed by monetary unit of the final products and services produced in the economy. Here, it should be taken into account that GDP is comprised of final products and services produced within the particular country. In this work, fuzzy inference logic system was applied to the dynamic model that demonstrates the dependence of GDP on currency and oil price in case of Azerbaijan economy. To solve this dynamic problem some coefficients, need to be defined. Fuzzy inference logic method was suggested to define these coefficients. Fuzzy inference logic method was realized by MATLAB Software Package.
Healthy Weight Estimation by Using Fuzzy Concept

The human brain encodes the information imprecisely, as information about the normal weight. Linguistic hedges and fuzzy sets can be helpful for description of approximate knowledge of primary data and verify it by the proposed fuzzy systems. In this work, linguistic terms and fuzzy sets are used to describe influential factors of healthy weight estimation in the example of fuzzy IF-THEN model. Experimental analysis is illustrated with a numerical example by using fuzzy concept.
Tableaux Deduction System for Fuzzy Logic with Estimates of Fuzziness Values

Let LZ be Zadeh’s fuzzy propositional logic determined by the norm min{x, y} with implication φ → ψ considered as the shorthand ~ φ ∨ ψ. A simple estimate for a formula φ has he forms φ ≥ a, φ > a, φ ≤ a and φ < a where φ is a formula of the logic LZ. An composite estimate is a propositional combination of simple estimates. We call the logic of estimates LE the set of all estimates (simple and composite) with their natural semantics determined by fuzzy interpretations for LZ. For the logic LE, some sound and complete inference system, based on the use of analytic tableaux, is presented.
Investment Decision Making by Using Natural Language Processing

The use of natural language data can improve decision-making. Classical considerations suggest that language may have developed because it improves decision-making which justifies the study of natural language’s contribution to decision-making. The study of data-based decision-making within the context of evolution provides a view of data use that permits us to both describe the phenomenon of information use as well as to make clear the way of its application. The paper deals with investment decision making through Natural Language Processing. The private citizen tries to invest to his best interest. To arrive at his decisions, he met group of experts to decide which investment option he should partake in. Linguistic terms extracted from the text conveyed by experts’ group are interpreted into fuzzy numbers and Fuzzy Inference Analysis is applied based on the data. According to the results conveyed by FIS the best alternative for investment is determined.
The AI Quid Pro Quo Problem: Suggesting a Framework for Patents Involving Artificial Intelligence Assisted or Created Inventions

Innovation involving artificial intelligence (AI) is rapidly expanding and diffusing into other areas of technology. Additionally, inventors have been using AI to assist in new technology for quite a while and have likely received patents from the United States Patent and Trademark Office (USPTO or “Office”) for their inventions without disclosing the AI involved in the patentable subject matter. As AI has become increasingly present in the implementation of new technology, the question of whether an AI can be an inventor has arisen. In Thaler v. Iancu and on appeal, the courts have affirmatively said no. However, this decision implicates the reliability, clarity, and incentivization of patents involving this subject matter. Given how fast AI technology has been developing, Congress should act to modernize the Patent Act to account for AI-assisted or -created inventions. In the meantime, this Note suggests the USPTO, through its regulatory power, can help alleviate these concerns by creating an identification system and requiring inventors to disclose the kind of AI involved in the assistance or in the creation of subject matter in patent applications.
Smart Traffic Monitoring and Control System

The article deals with the issues related to digitalization and the development of digital railways in connection with the transition to a high level of technological development. Improving the data-ware and intelligent structure of railways is one of the pressing issues to promote the innovation and development of railways and increase their basic competitiveness. Intelligent transportation systems (ITS) refer to information and communication technologies improving transportation outcomes such as transportation security, transportation productivity, travel reliability, informed travel choices, environmental protection, information exchange, and safety, which are applied to transport infrastructure and vehicles. Perspectives of the smart railway are presented, which allow the railway to be managed on the basis of information security based on the research results, the application of modern sensor and information communication technologies, and big data technology with the aim of efficient use of future smart transport in internal and external connected areas within the trends of creating smart cities and smart villages. An integrated overall structure of the high-level design of intelligent railways and a structural functional model of intelligent monitoring and traffic control systems are proposed to enable the development of high-speed railway construction. Also, the level of big databases, types of big data models, and various big data techniques are reviewed and summarized. The results of this research identify current research gaps and thus directions for future research in BDA in railway transport systems. The ability to use fuzzy logic in control and decision-making under conditions where risks are minimized and eliminated in the railway system by computing known linguistic terms and graphing uncertain knowledge is of great importance.
Application of Leointief’s Input-Output Model of Azerbaijan Economy Under Fuzzy Information

In this study, the economy of Azerbaijan is analyzed using the fuzzy approach. The main goal of the work is to achieve an optimal solution to the problem of calculating the matrix of social accounts in terms of probable changes in various ranges using mathematical methods of fuzzy set theory. In Leontief’s input-output model, the expected impact of changes in the volume of the final product (Yi) on the total product (Xi) was calculated using fuzzy set theory. In the study, calculations were made to study the effect of an increase or decrease in the final product of the agricultural sector on other sectors using fuzzy values of the matrix Ã.
Prediction of the Power Output of a 4.5 kW Photovoltaic System Using Three Empirical Models: A Case Study in Nahr El-Bared, Lebanon

In this paper, Multilayer Feed-Forward Neural Network (MFFNN) and Cascade Feed-forward Neural Network (CFNN) have been used to predict the electricity production (EP) from a 4.5 kW PV system located in Nahr El-Bared, Lebanon. Moreover, the accuracy of the proposed models is compared with the response surface methodology (RSM). For this aim, the hourly data of beam irradiance (BI), diffuse irradiance (DI), reflected irradiance (RI), sun height (SH), air temperature (AT), and wind speed (W) were collected and used as input parameters for the proposed models. The results showed that all proposed models were suitable for predicting the EP of the PV system. Among the developed models, the CFNN model presented significantly better prediction performance based on the value R2 and RMSE.
The Morphogenesis of Symbolic Forms in Music

The symbolic form of music was not in Cassirer’s original list, but it is fundamental because it shows the impact of sensory and other bodily conditions on symbolic forms. The morphogenesis is first analyzed concerning evolution, starting with Paleolithic musical instruments (35,000 BP). The perception of music is the first dimension of embodiment. We can distinguish the organ of balance and the cochlea. Complex musical forms demonstrate the elaboration and diversification of basic morphogenetic patterns. Motion control and motion perception is the second relevant dimension in the morphogenesis of music. Finally, since baroque theories of music, emotion is considered fundamental. We apply an actual, evolutionary, and cognitive model of human (and animal) emotion. A particular focus is on melodies (or themes in larger pieces of music). Eventually, five applications range from songwriters (Bob Dylan and Leonard Cohen), a string quartet by W. A. Mozart, fugues by J. S. Bach, and quasi-narrative patterns in film music to innovations in the twentieth and twenty-first centuries.
Comparative Analysis of Secure QKD Protocols for Small Satellites Constellation

Nowadays, everyone uses Internet, do financial transactions and many other day-to-day activities. As the users are becoming more and more aware of the privacy, data security had become a prime concern. Quantum Internet inspires to be a possible solution. Quantum Internet uses quantum key distribution to provide security by generating symmetric keys for the data transfer. Newly developed technologies and manufacturing processes paved way for designing and development of small satellites. Small satellites can be launched in forms of clusters at a marginal cost of launching large communication satellites. Here, we review the basic concepts of quantum key distribution. Its application in satellite communication specifically using CubeSat satellites. We also touch upon the recent development in the field of satellite-based QKD systems. A brief on the major milestones and important small satellite mission is also presented. Further, we present a comparison study between the prominent QKD algorithms pertaining to their application in satellite-based QKD systems. We conclude the article with discussion on the applications, advantages and disadvantages of using QKD on satellite-based communication networks, further extending it from local area networks to worldwide Internet.
Application of Genetic Algorithm in Volcanology and Seismology

In this chapter after presenting a briefed list of the GA applications, various advanced applications of Genetic Algorithms in the fields of seismology and volcanology are presented. The examples are arranged so that cover both the seismological and volcanological aspects.