Reviewerly/exHarmony huggingface dataset:
Design of a low-cost technological alternative tool to assess the amplitude of the visual field, based on an empirical study

In Aeronautics, vision is the most important sense as it ensures all the passenger’s safety. The pilot's ability to efficiently process the visual scene guarantees this safety. This paper presents a preliminary literature review that bases the design of a low-cost alternative technological tool that supports the assessment of the visual field amplitude. The design of the technological tool has been carried out applying the Kaur methodology. A virtual task was designed that will include three virtual environments: habituation, training and evaluation. Each virtual environment includes components, 3D objects and devices agreed with the specialists. The prototype will be developed using Unreal Engine and Blender free software tools. The software will integrate virtual reality devices for visualization and interaction. The prototype will be assessment following a defined validation protocol. The proposal could be a useful tool to contribute in one of the visual tests that are part of the pilot candidate selection process.
Vehicle Detection and Classification using Vibration Sensor and Machine Learning

Road traffic censuses have been carried out manually for many years since measurements by machines were not widely spread due to the difficulty of installation. To solve installation difficulty, the size issues of the necessary equipment, and privacy issues of the existing traffic counter, we are conducting research and development of portable traffic counters using a vibration sensor and machine learning. However, vehicle type classification was not realized in the previous work, hence it was not possible to survey traffic volume by vehicle types. In addition, to the best of our knowledge, there is no existing study that can detect and classify vehicles based on road vibrations with a single sensor. In this paper, we propose a method of vehicle type classification that is capable of binary classification of small and large vehicles by machine learning combined with Support Vector Machine and Random Forest for vibrations of passing vehicles. We evaluated the proposed method by conducting measurements for up to 12 hours at two actual road locations. We tested over 5 hours of data and confirmed that small vehicles classified with the F-measure of 0.96 and large vehicles with the F-measure of 0.83.
Rényi Fair Information Bottleneck for Image Classification

We develop a novel method for ensuring fairness in machine learning which we term as the Rényi Fair Information Bottleneck (RFIB). We consider two different fairness constraints - demographic parity and equalized odds - for learning fair representations and derive a loss function via a variational approach that uses Renyi's divergence with its tunable parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> and that takes into account the triple constraints of utility, fairness, and compactness of representation. We then evaluate the performance of our method for image classification using the EyePACS medical imaging dataset, showing it outperforms competing state of the art techniques with performance measured using a variety of compound utility/fairness metrics, including accuracy gap and Rawls' minimal accuracy.
Relevance Feature Discovery in Text Mining Using NLP

The volume of text that is created and formed each day is dramatically increased and increasing. This colossal sum or volume of profoundly unstructured text cannot be basically and effortlessly handled and seen by computer systems. Therefore, we can get an useful patterns from the text using an efficient and effective techniques and algorithms. Text mining is a strategy for dissecting text records to extricate valuable data and knowledge from the text document, analysis is done using Text mining. Mining contains a few exercises that assists you with deriving data from unstructured text information. Before you'll apply diverse text mining procedures, you might want to begin with text preprocessing, which is that the act of cleaning and rebuilding text information into a usable organization. This training could likewise be a center part of Natural Language handling (NLP) and it generally includes the utilization of strategies like language identification, tokenization, filtering, lemmatization and stemming to design information suitably for examination. At the point when text preprocessing is finished, after that you will apply text mining calculations to get experiences from the information. However, ensuring the excellent of chose highlights from message is a test because of the monstrous measure of insignificant (boisterous) data in message report is a major test, to guarantee the norm of found significance highlights in the message archives for portraying client inclinations because of huge scope terms and information designs.
Application of Crow Search Algorithm to Solve Discrete Optimal Control Problem with Constraints for a Flexible manipulator

In this paper, we have used a popular metaheuristic algorithm called the the Crow Search Algorithm (CSA) to solve the discrete optimal control problem with constraints. The main intention of this paper is to discretize the problem using the forward Euler method from which the performance index is considered. The problem formulation and the comparison of the proposed technique with different metaheuristic algorithms such as; Differential Evolution Algorithm (DE), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), is the main highlight of the paper. The performance index of these metaheuristics problems is compared with the propose approach and the Crow search algorithm has shown impressive results.
Software Effort Estimation Using Synthetic Minority Over-Sampling Technique for Regression (SMOTER)

Estimating the effort of a software project is one of the initial steps in software project development. Effort estimation is a practice of estimating how much effort will be necessary to develop or maintain a software project. It is important to calculate the effort at early stage in order to deliver the project on time without compromising the quality of project. The main goal of our research is to study the impact of Synthetic Minority Over-Sampling Technique for Regression (SMOTER) to predict effort estimation by using machine learning algorithms. The different machine learning algorithms that we have used are decision tree regressor, random forest, linear regression, lasso regression and ridge regression. In our study we used three datasets i.e., China, Maxwell and COCOMO81. The two-performance metrics Mean Magnitude Relative Error (MMRE) and PRED(25) were used to compare the results of each model using SMOTER and without using SMOTER. We have seen a significant decrease in error of each model after applying SMOTER.
Secure Online Book Resale Store System using Machine Learning

Books are the indivisible part of the student's life. The demand for books will never decrease. When it comes to selling or buying a second-hand book, then it becomes a time-consuming task. Students who have to buy or sell the books have to go to the local shop and then they have to sell or buy the books at a lower price or higher price respectively. This scenario involves the involvement of a middle man (broker) which increases the price of books. The internet plays an important role in daily life. And the e-commerce industry has come into the spotlight in recent years. The local bookstore’s space, time, and operation time are restricted. Because of that idea we provide services and convenience to people. The project aims to create one simple e-commerce website which contains the feature of selling and buying books online along with secure login using a hashing algorithm and recommendation system and Fake review detection using machine learning.
Docker Containerized Infrastructure Orchestration with Portainer Container-native Approach

Virtualization is an old technology that facilitates launching a computer infrastructure on another computer infrastructure. Very recently a containerized approach arrived in the computing world. Among most container vendors, Docker is the most trending and highly used container classification. To interact, orchestrate, govern and manage any Docker platform, a command-line-oriented approach was used. Portainer is a container-oriented approach, which provides the capability to orchestrate, govern and manage the whole Docker farm. Still, there is no such kind of evaluations for Portainer oriented orchestrations for Docker farms. This research study aims to evaluate Portainer oriented approaches to orchestrate Docker farms than traditional Docker command line approaches. The command-line-based approach brings plenty of complexity for the users to make interactions with Docker. Therefore, according to the observed facilities and capacities of the Portainer, Portainer is with very less overhead to the host infrastructure. As well interactive graphical user interfaces, easy accessibility, high platform security integration, and versatile monitoring features are beneficial to the end-users.
Garbage Classification using Machine Learning to Aid Recycling

Management of waste is one of the most basic essential services which are provided to the citizens by municipal authorities of the country to keep urban centers clean. However, almost all authorities dump solid waste at a dump yard either within or outside the city in a haphazard manner. Research conducted by experts suggests that India has been following a defective system for the disposal of waste and management of disposed waste. The solution to deal with inefficient waste management is to ensure proper segregation of garbage at its source. For this purpose, two models will be trained and tested. One is Resnet-50 (a convolutional neural network that is 50 layers deep) and the other is YOLO (A state of the art, real-time object detection system). Both of these techniques have been extensively used in the field of object detection and classification. In this paper, the performance of these two models will be compared.
Blockchain based Supply Chain Management

In recent times, blockchain technology has seen an unprecedented growth to new areas beyond finance into varied industries. Theoretical models in areas such as pharmacy, fashion and logistics as a whole have proved the technology’s mettle in effective supply chain management. Medical missions, disaster relief operations, and other situations can all benefit from Supply Chain Management (SCM) knowledge and capabilities. Utilizing blockchain within the supply chain can offer assistance to members recording cost, date, area, quality, certification, and other pertinent data. In this work, we offer a systematic literature mapping to provide a clear overview of blockchain technology’s potential advantage and influence on long-term supply chain performance. We also discuss the differences that could be laid out between the non-blockchain supply chain and a supply chain that utilises blockchain-based solutions. Modern applications are then analysed regarding their industry, implemented blockchain framework and addressed challenges. We also list key features to be contemplated for any effective blockchain-based solution and also propose architecture for Fabric supply chain transactions.
Metaheuristic Optimization of Neural Networks for Phishing Detection

The evolution of the world digitally has caused a considerable escalation in the number of cyberattacks, particularly phishing attempts, and solutions to this problem appear to be rare. Email phishing, spear phishing, angler phishing, and other phishing attacks exist and the goal is to correctly and optimally identify URLs and websites as legitimate or illlegitimate. The proposed approach uses techniques like principal component analysis and synthetic minority oversampling for preprocessing of the data. The preprocessed data is trained using various machine learning models. Two metaheuristic algorithms, the emperor penguin algorithm and the salp swarm algorithm, are implemented to optimize the trained model. Optimization with the salp swarm algorithm gave an accuracy of 93.98 % and computes the result in 80 seconds, while the emperor penguin algorithm gave an accuracy of 94.09 % and a computation time of 70 seconds which is at par with the state-of-the-art models.
Semantic Web: Knowledge Graph of Digital Minimalism Ontology

Nowadays students spend most of the time on the Internet for attending lectures, online certification courses and online internships. Living digital life is always joyful and fun act. Internet uses growing rapidly day by day due to online education. The digital algorithms are designed in such a way that students automatically attracts towards it instead of concentrating on study. They spend 4 - 5 hours to attend online lectures and 8 - 10 hours on social media platforms. It is very difficult for students to avoid wasting time on social media. In this paper, we have implemented Digital Minimalism (DM) ontology using Protégé 5.5.0 for students to spend time wisely on the Internet. Basically, Digital technology is developed to improve quality of life. Present time is valuable than money for students. This time can be used for career planning, personal development and learning new things. DM ontology is constructed by analyzing current problems of students. Next, we executed digital minimalism ontology DL query. Further, knowledge graph of DM ontology is graphically displayed using OntoGraph, OntoViz and VOWL.
Speak Pseudocode2c : A framework to convert customized pseudocode to c code

The source code of certain languages can be tough to understand for beginners and might make them lose interest in the subject altogether. Instead, the pseudocode is simpler compared to source code, as it abstracts away the peculiarities of programming language. The solution proposed in this paper is a framework, Speak Pseudocode2c, that addresses the simplicity of using natural language pseudocode, spoken line-by-line by the user which is simultaneously converted into source code in c language using a static stub-based approach. This approach is designed on programming constructs for different types of statements and loops, triggered by the user speaking an action word i.e. the first word present in the speech, which otherwise is counted as narration and is commented. Using Natural Language Processing (NLP) gives this approach the flexibility to interpret the pseudocode robustly.
A Modified Differential Evolution Algorithm Solving for Engineering Optimization Problems

For solving optimization issues, this work provides a modified differential evolution (MDE) technique. The DE parameters, such as scaling factor (F) and crossover ratio (CR), are dynamically modified in this approach to get the global minimum value, which was previously thought to be set for the classic DE algorithm. On seven multi-modal benchmark test functions, the suggested technique's performance is validated. Particle swarm optimization (PSO), cuckoo search algorithm (CSA), lightning search algorithm (LSA), arithmetic optimization algorithm (AOA), black widow optimization algorithm (BWOA), and seagull optimization algorithm (SOA) are also compared in terms of mean, maximum, minimum, and standard deviation of the objective function value. In comparison to previous approaches, the simulation results show that the proposed approach finds the best solutions.
A Study of Various Word Embeddings in Deep Learning

With the number of users and reviews rising on the internet, and the liberty was given to post anything on it, it is still a major issue to identify the sentiment of the reviews. The traditional machine learning models are taking a lot of computational time to get desired results. The advancement in the field of deep learning has left people wondering whether it would provide better results for such applications. Word Embeddings play a huge role in the classification of text, as they represent similar meaning documents with equivalent representation. The result of the deep learning model trained on a particular set of data depends upon the sort of word embedding used. In this work, we intend to present a study of various embeddings like GloVe, BERT, Word2vec, and Fast-text using a proposed model. The research was conducted on the IMDB movie review dataset and the result displayed that GloVe embeddings produced the best result with an accuracy of 87.936 % on our proposed model.
Fusion Implementation of Security Mechanisms for Secured Transmission of Images

In this digital epoch, immense use of internet has led to perturbations of information during transmission over open networks. Secure communication is possible with security mechanisms such as: "confidentiality, integrity and authenticity". The proposed algorithm implements encryption using hybrid chaotic maps. Security mechanisms such as Watermarking and Steganography are implemented using "Wavelet Transform and Singular Value Decomposition". Performance of the implemented scheme is assessed using Objective evaluation metrics such as "Histogram, Information Entropy, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), Bit Error Rate (BER), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Normalized Correlation Efficient (NCC), Structural Similarity (SSIM)". Watermarking objective assessment measures were computed in all four frequency bands. The conclusions made in this research are based on the use of a DICOM image database to test an encryption based nested watermarking system. It was observed that the proposed algorithm, was able to maximize watermark payload with the dimension of cover image and watermark images being same. Both the watermarks were embedded in LL band against the belief that it will result in the degradation of perceptual quality of the image. However, it was seen with the computation of PSNR, that good imperceptibility of the final cover (watermarked) image is achieved. PSNR for the implemented algorithm exhibited a value of 79.05 and SSIM of 0.999 very close to the expected values. The hybrid chaotic map, a composition of Logistic map, Skew tent map and Henon map was quantitatively analysed by computing Lyapunov exponent. Positive Lyapunov exponent indicates stronger encryption capability.
Review on Difference Expansion based Reversible Watermarking

Lossless watermarking techniques are used in sensitive areas like medicine forensic and military consignment. In it, an exact reconstruction of the cover from a watermarked image is required after the watermark extraction. The lossless or reversible watermarking schemes are varied, such as difference expansion (DE), histogram equalization, interpolation-based and predictive coding. Though different methods are present in the literature on reversible watermarking, the proposed paper reviews the evolution of DE based reversible watermarking in a structured way to analyze the developments from Tian's work to date. Algorithms in the DE are provided in addition to embedding and decoding procedures.
Face Recognition at varying angles from distant CCTV Footage using Siamese Architecture

Face Recognition is a deeply studied and researched domain. There are quite large solutions and model architectures to tackle majority of the face recognition related concerns. In this work we come up with a more specific version of face recognition which can mainly be used to achieve long distance and natural limitations of CCTV identification in real world scenarios using existing methods to better modifications. The solution which paper proposes using deep learning can be used to recognise a person even if they wear face masks due to the Covid-19 pandemic. One-shot learning is incorporated which can be used to train the specific model with just one image per person of the individual to be recognized. The designed model is modified from Siamese network architecture trained in triplet loss function to achieve these requirements.
Data Protection: The Cloud Security Perspective

Cloud computing is rapidly expanding, and businesses are seeking for ways to store their data in the cloud. Cloud security has evolved into a diversified sector, with cloud companies competing furiously to deliver the highest level of protection to their customers. The goal of this article is to discuss the various strategies used by cloud providers to provide data encryption on their clouds, as well as to compare the encryption algorithms employed by these well-known cloud providers. We also attempt to explain the widely used encryption algorithm Advanced Encryption Standard (AES) in the cloud provider sector. This article includes Homomorphic Encryption, which is a type of encryption employed by many cloud providers to allow the user to analyze and manipulate the data without the need to decrypt the data. Homomorphic Encryption, which is utilized by a number of cloud companies to protect data.
A Novel Facial Manipulation Detection Method Based on Contrastive Learning

Nowadays, numerous synthesized face-swapping videos generated by face forgery algorithms have become an emerging problem, which promotes facial manipulation detection to be a significant topic. With the development of face forgery algorithms, some fake face images or videos generated by those strong forgery algorithms are very realistic, which have brought much difficulty to facial manipulation detection. In this paper, we present a novel facial manipulation detection method based on contrastive learning. We analyze the texture features of manipulated facial images and propose to compare and learn the features of the whole face and the center face in order to get more general features. We calculate the similarity and distribution distance between the whole face and the center face. The experiments implemented on FaceForensics++ dataset demonstrate that the proposed method achieves outstanding results and can learn the general features.
Multi-objective Gray Wolf Optimization Algorithm for Multi-agent Pathfinding Problem

As a core problem of multi-agent systems, multiagent pathfinding has an important impact on the efficiency of multi-agent systems. Because of this, many novel multi-agent pathfinding methods have been proposed over the years. However, these methods have focused on different agents with different goals for research, and less research has been done on scenarios where different agents have the same goal. We propose a multiagent pathfinding method incorporating a multi-objective gray wolf optimization algorithm to solve the multi-agent pathfinding problem with the same objective. First, constrained optimization modeling is performed to obtain objective functions about agent wholeness and security. Then, the multi-objective gray wolf optimization algorithm is improved for solving the constrained optimization problem and further optimized for scenarios with insufficient computational resources. To verify the effectiveness of the multi-objective gray wolf optimization algorithm, we conduct experiments in a series of simulation environments and compare the improved multi-objective grey wolf optimization algorithm with some classical swarm intelligence optimization algorithms. The results show that the multi-agent pathfinding method incorporating the multi-objective gray wolf optimization algorithm is more efficient in handling multi-agent pathfinding problems with the same objective.