Reviewerly/exHarmony huggingface dataset:
Information Mesh Concepts in Support of Multi-organizational Interoperability

Information is central to the decision making processes that allow organizations to meet their strategic objectives. Organizations types span public and private industry, military, public security and safety, as well as academic. In most organizations, information sources can be internal but are generally external and include a stakeholder community that impose information handling requirements that can affect the exploitation of the information. Over the years, many protection mechanisms have been implemented. These mechanisms generally rely on cryptographic solutions applied by end-users. An information mesh provides a framework within which access management, protection, trust and chain of custody can be achieved. In addition, such a framework addresses authorship, ownership, custodianship and sovereignty challenges. This chapter provides an introduction to the concept of information mesh that abstracts away information technology and introduces information management in the context of a multi-organizational information sharing ecosystem.
Evolutionary Algorithms with Focus on Genetic Algorithm

In this chapter, an important group of intelligent optimization methods called evolutionary algorithms is presented, and among these methods, the genetic algorithm (GA), which is one of the most common evolutionary algorithms, is specifically focused on so that the reader, in addition to basic concepts, in details get acquainted with the GA method.
Performance Analysis of Machine Learning Algorithms for Medical Datasets

Medical applications using machine learning have gained importance; therefore, research on the applicability and adequacy of algorithms has also gained momentum. Investigating the performance of the algorithms is crucial for the real-life implementation of classification systems as diagnostic or decision support systems. This paper analyzes the performance of six machine learning algorithms, Decision Tree, Random Forest, Gradient Boosting, Extreme Gradient Boosting, Support Vector Machine, and Logistic Regression, on three medical datasets to demonstrate the weaknesses and strengths of the algorithms. Three experiments in two stages, hyperparameter tuning and performance evaluation are performed. Binary classification and multi-class evaluation are considered in order to analyze the performance of the models with varied challenges. Gradient Boosting and Logistic Regression achieved superior results in binary classification experiments, while Random Forest produced superior results in the multi-class experiment. The results suggested that different machine learning algorithms might obtain unstable performances or stable and consistent results.
Intelligent Methods and Motivations to Use in Volcanology and Seismology

In this chapter we present a brief glance at the various kinds of intelligent methods and also their most important advantages are described. Furthermore the motivations to use intelligent methods in volcanology and seismology is explained with a briefed list of the recent applications.
Deep Learning: Applications in Seismology and Volcanology

In this chapter, after presenting a list of the DL applications with brief descriptions, various advanced applications of deep learning in the fields of seismology and volcanology are presented. The examples are arranged in two separate parts, one for seismological and the other for volcanological aspects.
Deep Learning: The Concepts

This chapter presents the concepts of Deep Learning (DL) models. Also, comprehensive descriptions of general DL architectures are provided, and different types of such models are categorized and discussed in an applicable and reproducible form. The main feature of this chapter is presenting various examples with python codes so that the reader can easily follow the algorithms of designing and testing DL models step-by-step.
Performance Evaluation of Symmetric Encryption Algorithms Using MCDM Methods

Data and information are found to be stored, processed or transferred with the support of modern technological tools and hardware specifications. Due to the increase in the usage of digital communication and higher dependency on the internet technology platform, the risk factor also increased. Thus, it is essential to use safety and precautionary measures while storing and transferring data in order to prevent unauthorized access. One of the way of achieving this is through confidentiality, which uses various encryption algorithms to convert a readable message (plain text) into unreadable form (cipher text). Cryptographic algorithms are of two types- symmetric encryption algorithms and asymmetric encryption algorithms. This paper focused on few symmetric encryption algorithms namely 3DES, AES, Blowfish and IDEA. The objective of this work is to evaluate the performance of these encryption algorithms on the basis of three criteria- time, resource and privacy. The evaluation is done using MCDM methods such as PROMETHEE II, COPRAS and ARAS, which determine the ranking of these encryption algorithms based on the score obtained. The simulation has been performed in C/C++ compiler and the result analysis shows that Blowfish algorithm is considered as the best symmetric encryption algorithm in comparison to the others for COPRAS and ARAS methods whereas, IDEA is the best ranked in PROMETHEE II method.
Efficient Data Hiding Method in Image Based on Modified LSB

The rapid surge in digitalization has also increased the necessity for data security. Either in terms of storage, such as databases protected by cryptographic techniques, or in terms of individuals attempting to practice security for assorted reasons. Steganography is the process of concealing useful information within an uninteresting entity, such as an image or video that does not elicit much attention. This study focuses on the deployment of a flutter-based mobile application that enables the secure transfer of steganography-hidden images between users. The employed algorithm is RGB-based and further safeguarded by a key. The paper examines the key generation process as well as the Flutter application in detail and uses cloud-based resources to address common issues such as scalability and accessibility.
Smart Photo Editor using Generative Adversarial Network: A Machine Learning Approach

With the rapid development of digital photos that can be taken at a very low cost using a low powered device, there is a demand for programmes that can edit images for users who have no technical expertise of image editing. We've employed multiple Machine Learning models of GAN -Generative Adversarial Network - and algorithms to extract information from photographs and produced two modules of it summed up as “AI Photo Editor” which can be used as an all-in-one image editor based on the same principle. Using the input satellite image as a starting point, one of the modules can remove various small things from the image, while another one can create the “Map View.” It can also be quite beneficial in terms of providing datasets/databases for other Machine Learning and Neural Network models.
Dynamic Hand Gesture Recognition

Gestures were most likely utilised by our ancestors to communicate. Armstrong once stated that he believes movements using the hands were the earliest form of complex human communication. The beginning stage of human computer Interactions is a gesture recognition system. Here, we have designed a Dynamic Hand Gesture Recognition (HGR) System using a Neural Network, which can recognize the gesture using the Computer or Laptop’s Web Camera and do the corresponding tasks. The model has been divided into mainly three modules. Firstly, Module 1 is about building a CNN model which we use to predict the gestures. Our second module is about predicting the gesture through live video feed. The final module is about assigning a specified task for a particular predicted gesture. For the predicted gesture, we use PyAutoGUI module to control devices like mouse and keyboard. Finally, the system is made to do some desired tasks in response to the gestures and obtains 99.84% of accuracy.
Recognition of Handwritten Digit Using Neural Networks

Because of its practical uses in our daily lives, manuscript number identification is gradually relevant in the present times. Numerous identification techniques such as calligraphy number identification are deployed in many areas where great classification efficiency is required in recent years. In addition pattern recognition is a key component of any computer vision or artificial intelligence system. Machine Learning as well as Computer Vision researchers have used it extensively to build practical applications such as reading computerized bank draft numbers. In this project, we utilized an ANN to develop several surfaces attached development of a thinking computer system with two invisible surfaces to recognize manuscript numbers. The backpropagation method searches for the weight value that delivers the lowest overall error in the network while data processing between layers. Invisible layer neurons employ the ReLU activation function, whereas output neurons use SoftMax. The testing was done using the MNIST handwritten database, which is freely available. Finally retrieved a complete 60,000 digit pictures from the MNIST database for training, validation and testing.
Continual Learning in an Industrial Scenario: Equipment Classification on Edge Devices

The ability to incrementally learn to categorize objects is a key feature for a personalized system in real-world applications. The major constraint for such scenario relies on the catastrophic forgetting problem, which negatively impacts the performance of the models on previously learned representations. In this work, we developed an equipment classification model to be deployed on edge devices by applying regularization and memory-based class-incremental strategies, such that it can detect new classes while preserving its ability to detect previously known classes, mitigating the forgetting phenomenon. The strategies were tested on three datasets: CIFAR100 to validate the implementation, Stanford Dogs to ensure the reliability of the results as it is a more representative dataset, and SINATRA, which is the work's industrial dataset for equipment recognition. Experimental results on these datasets show that the Experience Replay strategy performed better. For the SINATRA dataset, average accuracy values of 95.57% and of 100% were achieved for Águas e Energias do Porto and Plastaze subsets, respectively. The outcomes of this work proved that by retaining only a limited number of exemplars from old classes, it is possible to update a pre-existing system to classify new devices in a shorter period and avoid catastrophic forgetting.
Evaluating Attention in Convolutional Neural Networks for Blended Images

In neuroscientific experiments, blended images are used to examine how attention mechanisms in the human brain work. They are particularly suited for this research area, as a subject needs to focus on particular features in an image to be able to classify superimposed objects. As Convolutional Neural Networks (CNNs) take some inspiration from the mammalian visual system – such as the hierarchical structure where different levels of abstraction are processed on different network layers – we examine how CNNs perform on this task. More specifically, we evaluate the performance of four popular CNN architectures (ResNet18, ResNet50, CORnet-Z, and Inception V3) on the classification of objects in blended images. Since humans can rather easily solve this task by applying object-based attention, we also augment all architectures with a multi-headed self-attention mechanism to examine its effect on performance. Lastly, we analyse if there is a correlation between the similarity of a network architecture's structure to the human visual system and its ability to correctly classify objects in blended images. Our findings showed that adding a self-attention mechanism reliably increases the similarity to the V4 area of the human ventral stream, an area where attention has a large influence on the processing of visual stimuli.
Image Interpolation with Edges Preserving and Implementation on the Real ADAS Platform

To save transmission, processing and memory resources in Advanced Driver Assistance Systems (ADAS), it is often necessary to reduce the image resolution. Sometimes it is necessary to increase it after the transmission. Both resolution changes involve an image interpolation process. This paper describes implementation for three well-known interpolation methods, nearest neighbour interpolation (NN), bilinear interpolation (BL) and bicubic interpolation (BC), on a real automotive AMV ALPHA platform, using multiple processors on the same System on Chip (SoC). Implementation was done using C programming language and Vision Software Development Kit (VSDK). Specific attention is given to the optimal distribution of tasks to the certain processor. The results have shown that, on the real automotive AMV ALPHA platform, BL interpolation achieves the best trade-off between the quality of interpolated image for the usage in automotive image-processing based algorithms and execution time, especially for the algorithms where the lower frame rate is acceptable (like surround-view, park assist, etc.).
Text summarization using Secretary problem

Automatic text summarization is a long-running program, a crucial part of NLP(Natural language processing), which is again a subpart of Artificial Intelligence(AI). Many successful research techniques have been invented for summarization purposes. We propose a unique concept to generate the summary using the secretary problem, which comes under the extractive text summarization method. We divide the document text into two parts. We will match our text with the main title in the first part. If the main title’s words match the document text sentence, maintain those sentences in one of the lists. We will apply the secretary problem in other sentences that do not have title words. Combined with other sentence generation methods, the Secretary problem will guarantee the best candidate one-third of the time or 37%. This article presents our concept of leveraging a mathematical model to generate a summary that does not include some important sentences.
Machine Learning: The Concepts

The purpose of this chapter is to provide the description of some of the main algorithms used in the field of Machine Learning. The mathematical details are reduced to the minimum necessary in order to give the readers a clear idea of the key points and allow them to proceed in their applications with greater awareness.
Comparison of Static and Single Axis Solar Tracker

Solar energy is now the third-largest source of energy globally. It is drawing interest from all corners of the world. In order to evaluate the solar power potential of the Bokaro region in the Indian State of Jharkhand, this study has been done. It describes the operation of a single-axis solar tracker at coordinates of 23.6693⁰ Latitude and 86.1511⁰ Longitude. Developing a single-axis solar tracker has as its main goal to detect the sun’s position throughout the day. The zenith angle, Berger-Duffie model and the most basic clear sky global horizontal irradiance (GHI) i.e., the highest solar radiation on Earth’s surface in a particular moment and location in the absence of clouds, were used to determine the solar irradiance over the course of a day. The total energy produced during the day was also measured. This technology’s basic tenet is to maximize solar radiation from dawn until dusk to account for the sun’s shifting locations during the winter. The maximum power received from the panels is determined using the Maximum Power Point Tracking (MPPT) technique. The energy gain and efficiency of the suggested tracking unit, as shown in the findings, are provided in the form of graphs and tables which indicates that the Single Axis Solar Tracker performs noticeably admirable than the Static Solar System. Maximum power extraction is required to increase the system’s efficiency. This is accomplished by designing and simulating a Maximum power point (MPP) tracker using MATLAB/SIMULINK.
Union Embedding and Backbone-Attention boost Zero-Shot Learning Model (UBZSL)

Zero-Shot Learning (ZSL) aims to identify categories that are never seen during training. There are many ZSL methods available, and the number is steadily increasing. Even then, there are still some issues to be resolved, such as class embedding and image functions. Human-annotated attributes have been involved in recent work on class embedding. However, this type of attribute does not adequately represent the semantic and visual aspects of each class, and these annotating attributes are time-consuming. Furthermore, ZSL methods for extracting image features rely on the development of pre-trained image representations or fine-tuned models, focusing on learning appropriate functions between image representations and attributes. To reduce the dependency on manual annotation and improve the classification effectiveness, we believe that ZSL would benefit from using Contrastive Language-Image Pre-Training (CLIP) or combined with manual annotation. For this purpose, we propose an improved ZSL model named UBZSL. It uses CLIP combined with manual annotation as a class embedding method and uses an attention map for feature extraction. Experiments show that the performance of our ZSL model on the CUB dataset is greatly improved compared to the current model.
Hybrid Watermarking Algorithm to Protect and Authenticate KhalifaSat Imagery using DWT-SVD and SHA3 Hash Key

Using DWT-SVD and SHA3 Hash function, this research aims to develop an ownership protection and image authentication technique that embeds the watermark information and hash authentication key in a hybrid domain. The experiment was conducted with multispectral images from the KhalifaSat. The Performance of the proposed method is evaluated using wavelet domain signal to noise ratio (WSNR), structural similarity index measurement (SSIM) and peak signal to noise ratio (PSNR). To analyse the efficacy of the recovered watermark, two metrics are used: Normalized Correlation (NC) and Image Quality Index (IQI). The method presented is robust against many intended and unintended attacks. Without sacrificing transparency, our proposed watermarking approach meets the objectives of imperceptibility and robustness. It accurately detects the manipulated locations on the satellite image and is sensitive to even small changes.
Animal Video Retrieval System using Image Recognition and Relationships Between Concepts of Animal Families and Species

In recent years, video streaming services have become increasingly popular. In general, the search function in a video sharing service site evaluates the relevance of a search query to the title, tags, description, and so on given by the creator of the video. Then, the search results with the highest relevance are displayed. Therefore, if a title is given to a video that does not match its content, there is a possibility that a video with low relevance will be found. In this research, (1) we built a new system that retrieves animal videos that are relevant to its content using image recognition. (2) By describing the relationships between the concepts of animal families and species and incorporating them into the retrieval system, it is possible to retrieve animal videos by their family names. Adding retrieval by animal family name enabled us to find species that have not been learned. In this research, (3) we confirmed the usefulness of our video retrieval system using trained neural networks, GoogLeNet and ResNet50, as animal species classifiers.
Real or Fake? A Practical Method for Detecting Tempered Images

Tempering images has become technology that almost everyone can complete, including fake news, fake evidence presented in court, or forged documents. The main reason is because these editing tools, such as Photoshop, is simple to use, which is an urgent issue we need to solve. Hence, automatic tools helping to find manipulated images apart is critical for fighting misinformation campaigns. Here we propose and evaluate a neural network-based method. It can detect whether images have been artificially modified (classification), and further indicate the forged parts (segmentation). Our proposed method has better performance than most baseline methods. Last but not least, our method is not only effective on JPEG format, but can also be used on other formats.
C-ESRGAN: Synthesis of super-resolution images by image classification

With the development of deep learning, super-resolution image synthesis techniques for enhancing low-resolution images have advanced remarkably. However, mainstream algorithms focus on improving the quality of the entire image on average and this may result in blurring. In this paper, we propose three key components for synthesizing super-resolution images that can reflect the fine details of an image. We synthesize super-resolution images by image classification. First, the neural network weights learned using the images in the same image category were utilized in synthesizing super-resolution images. For this purpose, image classification was performed using a transfer-trained ResNet. Second, SENet was applied to the generators in our proposed method to obtain detailed information about the images. Finally, the feature extraction network was changed from VGG to ResNet in order to get more important features. As a result, we achieved better image evaluation values (PSNR, NIQE) for the super-resolution images of dogs and cats compared to the previous studies. Furthermore, the images were generated more naturally on the benchmark dataset.
Adversarial Machine Learning Protection Using the Example of Evasion Attacks on Medical Images

This work considers evasion attacks on machine learning (ML) systems that use medical images in their analysis. Their systematization and a practical assessment of feasibility are carried out. Existing protection techniques against ML evasion attacks are presented and analyzed. The features of medical images are given and the formulation of the problem of evasion attack protection for these images based on several protective methods is provided. The authors have identified, implemented, and tested the most relevant protection methods on practical examples: an analysis of images of patients with COVID-19.
Characterizing and Understanding Development of Social Computing Through DBLP: A Data-Driven Analysis

During the past decades, the term “social computing” has become a promising interdisciplinary area in the intersection of computer science and social science. In this work, we conduct a data-driven study to understand the development of social computing using the data collected from Digital Bibliography and Library Project (DBLP), a representative computer science bibliography website. We have observed a series of trends in the development of social computing, including the evolution of the number of publications, popular keywords, top venues, international collaborations, and research topics. Our findings will be helpful for researchers and practitioners working in relevant fields.