Reviewerly/exHarmony huggingface dataset:
Modified Discrete Differential Evolution with Neighborhood Approach for Grayscale Image Enhancement

The Different Evolution (DE) algorithm is a simplistic tool under the repository of Evolutionary Algorithm (EAs) for solving complex real-valued parameter optimization problems. Extending the superiority of DE to solve discrete-valued parameter optimization problem is an active research area. This chapter proposes a modified DE, best neighborhood DE (BNDE), which is faster in solving discreate-valued benchmarking and real-world optimization problems. The study presented in this chapter has two phases. In the first phase, the proposed BNDE, which is embedded with a new mapping approach for mutant vector generation, is designed, implemented, and tested. The proposed mapping technique is to increase the exploitation and exploration capability of the DE and to reach the optimal solution faster. The performance highlights of the proposed BNDE approach were empirically and statistically analyzed with a set of benchmarking travelling salesman problems (TSPs). In the second phase, the proposed BNDE algorithm is used to enhance the contrast and brightness of the microscopic images. The BNDE performed contrast enhancement of image by optimizing the parameters of the transformation function. Performance of BNDE is compared with the Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) approaches. The empirical performance of the proposed approach is quantitatively measured using the metrics Structural Similarity Index (SSIM), Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), and entropy. The proposed BNDE is found to be outperforming the counterpart algorithms on the performance metrics considered.
Content-Based Image Retrieval Using Deep Features and Hamming Distance

Content-based image retrieval is a method of retrieving images based on its semantic features. Classical CBIR models in the literature uses low-level features like color, shape, and texture to retrieve similar images from the database. Low-level features work well when the dataset is small; however, it fails to reduce the semantic gap when the image database becomes large and diverse. In the recent past, many researchers have exploited the usage of pretrained neural network as a feature extractor capable of giving a high-level feature representation for images. In this chapter, we use the output vectors of intermediate layers of an Inception Resnet deep learning model as the feature representation for image retrieval. The high-level feature vectors obtained from the deep layers of Inception Resnet are clustered using K-means clustering algorithm to make the retrieval process faster. The second part of the chapter explores various distance metrics to find most similar images corresponding to the query image. Based on the analysis on various distance measures, it is found that Hamming distance performs better than Euclidean distance in retrieving similar images when using deep features owing to the sparse nature of the feature vectors produced by the intermediate deep layers of Inception Resnet.
Shape Feature Extraction Techniques for Computer Vision Applications

Computer vision (CV) is a branch of artificial intelligence that educates and assists computers in recognizing and comprehending the content of digital images. It is primarily concerned with replicating attributes of a human vision system and empowering computer systems to process and categorize artifacts in digital images similar to humans. CV can be applied in various domains, including robotics, autonomous vehicles, remote sensing, medical diagnosis, pattern recognition, etc. Extracting image features has become a key element in CV applications. For this purpose, we are using shape feature detectors and descriptors. Motivated by the need to understand shape feature detector fundamentals and applications in CV, the present work aims to explore various feature extraction techniques and shape detection approaches required for image retrieval. In addition, real-time applications of shape feature extraction and object recognition techniques are also discussed with examples.
GLCM Feature-Based Texture Image Classification Using Machine Learning Algorithms

Texture is one of the important characteristics of an image, and its features can be used to identify specific region of interest from the image. Texture features can be extracted for any kind of images such as RGB, monochrome, aerial, and satellite images. This chapter describes an approach for texture image classification based on Grey-Level Co-occurrence Matrix (GLCM) features and machine learning algorithm such as support vector machine. Ten different GLCM features were extracted and fed as input to support vector machine for classification. The proposed method is trained and tested with dataset collected from Center for Image Analysis, Swedish University. In recent days, machine learning (ML) methods were highly used to mimic the complex mathematical expressions of texture features. The main objective of this chapter is to demonstrate the state of the art of ML models in texture image prediction and to give insight into the most suitable models.
Multi‐agent Path Planning with Heterogenous Interactions in Tight Spaces

Abstract By starting with the assumption that motion is fundamentally a decision making problem, we use the world‐line concept from Special Relativity as the inspiration for a novel multi‐agent path planning method. We have identified a particular set of problems that have so far been overlooked by previous works. We present our solution for the global path planning problem for each agent and ensure smooth local collision avoidance for each pair of agents in the scene. We accomplish this by modelling the collision‐free trajectories of the agents through 2D space and time as rods in 3D. We obtain smooth trajectories by solving a non‐linear optimization problem with a quasi‐Newton interior point solver, initializing the solver with a non‐intersecting configuration from a modified Dijkstra's algorithm. This space–time formulation allows us to simulate previously ignored phenomena such as highly heterogeneous interactions in very constrained environments. It also provides a solution for scenes with unnaturally symmetric agent alignments without the need for jittering agent positions or velocities.
Design of Trading Energy System Management Using Blockchain Hyperledger Fabric

There are numbers of research that have been done to improve the security, transparency, and honesty of electricity transactions in smartgrid or peer-to-peer trading communities without exposing the privacy of their consumer and prosumer, one of them is blockchain. Nowadays, blockchain is well known and most of them are used in financial cases. Blockchain is used to make trading management systems using Hyperledger Fabric where the permissioned blockchain system is aimed to secure the privacy of the consumer. In this paper, the Hyperledger Caliper by IBM was used to make the scenario of testing. The result shows that for creating data or transactions, the average latency of 50 tps is 0.4s, 100 tps is 6.57s and 200 tps is 9.18s. This result shows the validation takes more time. The bigger the transaction from the results, the more validators can be made. Computing power is a matter in this project. So, with the configuration of 3 organizational nodes, it can still handle 1000 transactions per second properly without failure and this configuration can be integrated with cloud server or the public blockchain through the API so, that a more dynamic model can be obtained from this proposed model.
Empirical Review on the Use of Blockchain Technology in Supply Chain in the Sultanate of Oman

Blockchain technology greatly transforms several industries, particularly since it has revolutionized the world of supply chain management systems. A traditional supply chain management system is critical due to fast-changing market trends and advancements in technology. Oman’s 2040 vision highlights and gives great importance to the digitalization of the economy. This study aims to explore the use of blockchain technology in supply chain management systems, its challenges, and its benefits. The study highlights the use of blockchain technology in Oman and offers a conceptual model which depicts four attributes of blockchain in the supply chain and perspective benefits that a country can enjoy after the successful implementation of blockchain in the supply chain. The future of the supply chain is based on technological developments and advancements such as blockchain technology. Thus, blockchain greatly improves the supply chain by lowering administrative costs and competing in the international market.
A Chinese Speech Recognition System Based on Binary Neural Network and Pre-processing

Neural networks have made excellent progress in the field of speech recognition. However, more research needs to be done in some scenarios where computational resources are limited or real-time, and low power consumption is required. In this paper, we propose a lightweight speech recognition model based on pre-processing + binary neural network, which can significantly reduce the number of weight parameters while ensuring an acceptable error rate. The speech pre-processing part converts the 1D speech signal to the 2D Mel spectrum and uses Voice Activate Detection (VAD) to make the speech Mel spectrum input variable. The speech data set is also expanded using data augmentation methods. For convolutional layers, the weights are binarized to reduce the number of model parameters and improve computational and storage efficiency. The number of model parameters after quantization is 6.94% of the number of full precision model parameters, and the error rate on the ST CMD speech dataset increases by only 2.07%. Finally, a circuit structure based on binary weights for convolutional computation is designed, and a single multiplication can be implemented using only the hardware resources of the 7 Look Up Table (LUT).
Deterministic Convergence of Backpropagation Algorithm with Cyclic Dropconnect for Linear Output Neural Networks

In this paper, we consider the backpropagation algorithm with cyclic DropConnect (BPA-CDC) for neural networks with linear output. Under mild conditions, we establish the deterministic convergence theory for BPA-CDC, showing that the cost function tends to a constant, the gradient tends to zero, and the weight vector tends to a point. Simulation results are provided to validate our theoretical findings.
Exploring Artificial Intelligence (AI) Impact on Businesses: Perspectives from Big Data and Security

The concept of Artificial Intelligence (AI) has changed with time, and with the advent of Big Data, Machine Learning, Deep Learning, and Artificial Neural Networks, the fundamentals of AI and the way it is perceived in today’s life have undergone a sea-change. However, along with this, the perception of Data and Security has also come a long way. Security earlier meant being in a secure, safe, risk-free zone; it has now metamorphosed itself into a concept of perimeter-driven layers of security – which changes from person to person. Against this backdrop, some of the pertinent questions that face us today are: Does AI aim to replace Human Intelligence? Would AI overtake Human Intelligence? Can AI ‘learn’ on its own – thereby teaching itself new concepts and mechanisms – while on the fly? This article takes us through the various concepts of AI, its types and modes of growth and expected future development, leading to its impact on society, businesses, culture and people. Further, this study leads to impact points of AI development and progress on points like data and security and possible costs of security and whether they would be restricted to financial costs alone. Towards the end, the authors put forth thoughts to ponder on whether Big Data and AI are the answers to all questions that lie ahead; and how AI can play a role in the betterment of Data and Security available to global citizens of future societies; with a responsible impact on businesses, economies, people and culture.
The Prosecution of Cybercrimes in Nigeria: Challenges and Prospects

The globalisation of the world through ICT has gained universal acceptance. Almost every facet of all sectors which includes but is not limited to education, business, media, entertainment, and government are now internet compliance. This welcoming benefit of the internet advancement in technology also included its growth rate of criminalities called cybercrimes. This study investigates various challenges that inhabit the successful prosecution of cybercrime in Nigeria and its prospects. The study found that the major challenges of prosecuting attackers in Nigeria’s cyberspace are the lack of robust law and wea enforcement, the slow judicial process, forensic analyses and so on. The necessity of a robust legislative regime for Regulatory bodies that will bring about the effectiveness of prosecuting cyber criminalities will be emphasized in the paper. The results of this study may be put to use to formulate necessary steps toward curbing the menace of Nigerian cybercrime.
Cybercrime Regulatory Agencies need urgent Reform to Protect Nigeria

Today’s life revolves around the internet. Its importance in communications and the economic growth of a nation cannot be overstated. Today, the globe can attain many things that were inconceivable decades ago. Due to rising dependency on cyberspace, millions of corporate bodies and individuals are taking advantage of the World Wide Web to perpetuate crime for a living. Simply by using a mouse to select an option on a computer, one can get anything they desire at any moment from the internet, anyplace, regardless of location. Despite the internet’s advantages, cybercrime has brought serious threats to individuals and states. The Nigerian government has created various Regulatory Agencies in an attempt to combat cybercrime. This work investigates Nigeria’s institutional Agencies’ structure for dealing with cybercrime while also highlighting the difficulties regulatory bodies have in doing so. The study, therefore, concludes that if Nigeria must be free from the top third-ranked nation where cybercrime grows but would attract investment from Africa and other countries to improve our economy, it is important for the Federal Government to create a specialised institutional body that has the only mandate of regulating and controlling cybercrime in Nigeria.
Police Patrol Routes Optimization: A Literature Review

Security is achieved when people feel reassured and safe, hence that, governments of countries seek to become a role model by achieving peace. Here comes the role of the security systems and police. There are three main types of patrols such as land patrols, marine patrols, and air force patrols. Police are assigned to combat various kinds of crimes and carry many tasks such as search and rescue operations, securing and organizing events. Therefore, the time and location where crimes occur should be predicted, so that all the needed procedures are taken. Further it becomes very necessary to optimize police patrol routes, as this will lead to crimes prevention or to arrest the perpetrators. Accordingly, the main purpose of this study is to classify the most common objectives and problems facing the police, and the methods and models used in order to solve them. A systematic literature review is the methodology used to conduct this study, by focusing on previous studies aimed to optimize patrol routes and prevent crimes. The study relied and focused on 31 selected research papers to compare and clarify models used in crimes prediction and patrols optimization. The study classified crime prediction and patrol routes optimization methods into five main methods: machine learning, mathematical, simulation, stochastic, and recommendation system methods.
The Legal Approach for Fighting Cybercrimes in Nigeria: Some Lessons from the United States and the United Kingdom

In our contemporary world, internet business is now a crucial component of our day-to-day activities, not only does the internet make life easier for people, but they also bring many devastating criminal impacts upon the people, institution, nation, government and the economy. To checkmate cyberspace criminality, various countries of the world including Nigeria, have enacted laws to regulate telecommunication infrastructures, and protect and safeguard the people, organisations, institutions and the state against cyber criminals. The thrust of this study is to examine the current laws put in place by the Nigerian government for fighting cybercrimes. This study found that Nigeria’s Cybercrimes Law has played an important role in online business, particularly in the prevention and punishment of cybercrime, card and ATM fraud, internet fraud, identity theft, malicious hacking, and data theft. The research also highlighted Nigeria’s legal system’s ability to support and encourage online businesses. The work also discusses the USA and the UK legal regimes with the view of drawing lessons that Nigeria as a country can adopt in solving the problem of cybercrimes. In conclusion, the study would provide a guide as to the need for the National Assembly to enact a robust legal regime for effective cybercrime fighting in Nigeria. In the alternative, the study recommends that our legislature should reform the Cybercrime Act 2015 and other existing extant laws on cybercrimes in line with international best practices to boost investors’ and consumers’ confidence, promote economic growth and generate job opportunities for Nigeria’s youth.
Pseudo-embeddings and hyperovals of the generalized quadrangle of order (3,5)

We determine explicit constructions for the five homogeneous pseudo-embeddings of the generalized quadrangle GQ(3,5). We also discuss a number of applications to hyperovals and tight sets. In particular, we find explicit equations for each of the eight hyperovals of GQ(3,5).
Rating scale preferences for accurate recommendations

Recommender systems allow users to express preferences for items using a rating scale. However, employing this scale differs from one user to another, and sometimes it seems that each user has a personal scale. Hence it is vital to capture the user preferences for the rating scale for better recommendations. Previous work tried to weight either the similarity value or the individual ratings of items. However, weighting the rating scale itself to reflect personal user interests is more effective. This reduces the problem space by directly targeting the tool used to express user preferences, not individual ratings for items. This paper proposes different approaches for generating a personalized rating scale that captures the user preferences reflected by the statistical properties of his profile. Some approaches consider the features of the active user, while others also consider the attributes of the training user. The experimental results disclose different user behavior patterns for the rating scale. They show that the system performance is improved so much by introducing weights to the rating scale with 70% improvement for some schemes.
Digital Literature Literacy (DLL) and Literature Digital Reading (LDR) in European Schools

DILECTINGS (Digital Literature Educational Competences for Teachers: Intercultural Inclusive Good Practices) is a European Key Action 2 Project that explores digital literature teaching. Digital literature—literature intended as authentic materials with cultural value—is an engaging and interactive narrative form delivered via digital media, that encompasses the language of books, films, web pages, radio, among others. This paper aims to present a preliminary questionnaire to explore the needs of primary and secondary teachers in terms of digital literature literacy and literature digital reading. This article will show the questionnaire design process to carry out a proper needs analysis.
CHARACTERIZATIONS OF THE INVERSION FORMULA OF THE CONTINUOUS BESSEL WAVELET TRANSFORM OF DISTRIBUTIONS IN Hμ′(ℝ+)

The inversion formula of the continuous Bessel wavelet transform of distributions is investigated by exploiting the theory of the Hankel transform. Some auxiliary results related to the inversion formula are also obtained in this paper. Using the theory of inversion formula of continuous Bessel wavelet transform of distributions, the Calderón reproducing formula is developed. The continuous Bessel wavelet transform of distributions through heat equation is discussed and its inversion formula is considered.
Modification of Learning Ratio and Drop-Out for Stochastic Gradient Descendant Algorithm

The stochastic gradient descendant algorithm is one of the most popular neural network training algorithms. Many authors have contributed to modifying or adapting its shape and parametrizations in order to improve its performance. In this paper, the authors propose two modifications on this algorithm that can result in a better performance without increasing significantly the computational and time resources needed. The first one is a dynamic learning ratio depending on the network layer where it is applied, and the second one is a dynamic drop-out that decreases through the epochs of training. These techniques have been tested against different benchmark function to see their effect on the learning process. The obtained results show that the application of these techniques improves the performance of the learning of the neural network, especially when they are used together.
Restaurant Web Application with Integrated Dine-in Self Service

This paper explores the use of automated technologies in the food service industry, with a focus on automating the food ordering process in restaurants using QR code scanning technology. The project aims to improve the dining experience of customers by providing a more personalized and efficient service through the design and implementation of a Digital Food Ordering Web application. The system includes a dynamic database utility, enabling the retrieval of data or information from a centralized database. Once the customer selects the food items using the QR code scanner, the ordered list is directly sent to the room and the bill counter, thereby increasing the quality and speed of service. The system offers a cost-effective chance to provide a personalized service experience to customers from feeding to ordering to payment and feedback. The paper highlights the efficiency and accuracy of the system by reducing human effort, providing customer feedback and accepting different types of payment. The system is designed to connect multiple restaurants to provide seamless services to customers, and all communication between the customer, kitchen display, and the cashier counter are done through a Web Application.