Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis on Social Media (Twitter) about Vaccine-19 Using Support Vector Machine Algorithm

Currently the world is experiencing a Corona Virus Disease (Covid-19) pandemic which attacks the respiratory tract and spreads very quickly to various countries including Indonesia, so the World Health Organization (WHO) has declared Covid-19 as a pandemic. To overcome this pandemic, experts in the medical field also intervened by making vaccinations to strengthen human immunity against the Covid virus. This sentiment analysis was carried out to see opinions on the object, namely the existence of a Covid-19 vaccine. Data collection by crawling data with the keyword &#x2018;Covid Vaccine&#x2019;. The method that will be used is the Support Vector Machine (SVM). The analysis was carried out by comparing the classification accuracy values of the two SVM kernel functions, namely linear and Radial Basic Function (RBF). The results of the study obtained positive sentiment of 43.5&#x0025;, negative of 19.1&#x0025;, and neutral of 37.4&#x0025;. Then the evaluation of the system using the confusion matrix obtained an accuracy value for the linear kernel of 79.15&#x0025;, a precision value of 77.31&#x0025;, and a recall value of 78.09&#x0025;. While the RBF kernel has an accuracy of 84.25&#x0025;, a precision value of 83.67&#x0025;, and a recall value of 81.99&#x0025;. While the cross validation obtained the optimum value at <tex>$\mathrm{k}=1$</tex> with an accuracy value of 80.18&#x0025; for the linear kernel and 85.88&#x0025; for the RBF kernel. So the RBF kernel has a higher accuracy than the linear kernel.
Comparison Between Different Machine Learning Algorithms

Machine learning is a major application of AI it is a phenomena by which system automatically learn and improve from real world experience in form of data on which it is trained or by observing the surroundings. Machine learning is changing the planet by transforming all segments including healthcare services, education, transport, food, entertainment, and different production line and lots of more. Machine learning plays a great role in changing our lives as well as industries in sector like housing and applications, cars, shopping, food ordering, etc. One of the application of ML is character recognition. Character recognition is used to convert handwritten or printed documents or image characters of document to computer codes. The basic process involves examining the text of a document and translating the characters into code that can be used for data processing. Hardware, like an optical scanner or specialized circuit card is employed to repeat or read text while software typically handles the advanced processing. Software can also take advantage of machine learning algorithms to implement more advanced methods of intelligent character recognition, like identifying languages or styles of handwriting. In this era there are various machine Learning algorithms like a linear regression, Convolutional Neural Network(CNN), kNN, K-Means, Random Forest etc. In this paper we are going to compare the results between between linear regression and Convolution Neural Network(CNN) over a same data-set of 0-9 digits.
Salient Object Detection Based on Multi-layer Cascade and Fine Boundary

Due to the continuous improvement of deep learning, saliency object detection based on deep learning has been a hot topic in computational vision. The Fully Convolutional Neural Network (FCNS) has become the mainstream method in salient target measurement. In this article, we propose a new end-to-end multi-level feature fusion module(MCFB), success-fully achieving the goal of extracting rich multi-scale global information by integrating semantic and detailed information. In our module, we obtain different levels of feature maps through convolution, and then cascade the different levels of feature maps, fully considering our global information, and get a rough saliency image. We also propose an optimization module upon our base module to further optimize the feature map. To obtain a clearer boundary, we use a self-defined loss function to optimize the learning process, which includes the Intersection-over-Union (IoU) losses, Binary Cross-Entropy (BCE), and Structural Similarity (SSIM). The module can extract global information to a greater extent while obtaining clearer boundaries. Compared with some existing representative methods, this method has achieved good results.
Recent Trends in Text Region Identification and Localization in Native Surrounding Images

Vision-based content analysis and understanding is a crucial aspect in the field of machine vision and combining it with machine learning-based techniques improves the system&#x2019;s efficiency and accuracy significantly. Identification and Localization of the Textual information in scene images is one of the demanding issues in this field. And it is utilized in a variety of recent growing vision-based applications that includes robot navigation, language translation, and industrial automation. Because textual information is dispersed across an image and has no prior knowledge of its location, it a is tiresome process to identify and localize text instances in images acquired in an uncontrolled environment. In this paper, we have analyzed and summarized the recent breakthroughs in this domain that aim to resolve the complications inherent with it using classic and modern methodologies. Also, discuss the available benchmarked datasets and evaluation procedures for measuring the effectiveness of the reported approaches. Finally, we addressed the upcoming research direction towards the mentioned domain.
Back to the Future—Revisiting OrdinalGP and Trustable Models After a Decade

OrdinalGP (2006) [4] embraced a fail-fast philosophy to efficiently model very large data sets. Recently, we realized that it was also effective against small data sets to reward model generalization. ESSENCE (2009) [6] extended the OrdinalGP concept to handle imbalanced data by using the SMITS algorithm to rank data records according to their information content to avoid locking into the behavior of heavily sampled data regions but had the disadvantage of computationally-intensive data conditioning with a corresponding fixed data ranking. With BalancedGP (2019) we shifted to a stochastic sampling to achieve a similar benefit. Trustable models (2007) [3] exploited the diversity of model forms developed during symbolic regression to define ensembles that feature both accurate prediction as well as detection of extrapolation into new regions of parameter space as well as changes in the underlying system. Although the deployed implementation has been effective, the diversity metric used was data-centric so alternatives have been explored to improve the robustness of ensemble definition. This chapter documents our latest thinking, realizations, and benefits of revisiting OrdinalGP and trustable models.
A novel chaos sparrow search algorithm for TSP problem

A novel chaos sparrow search algorithm is designed to solve the TSP problems. Firstly, the greedy method and cubic chaos are combined to initialize the population. Secondly, a new formula for location update of reconnaissance and early warning is designed. Then, the local search is carried out by combining Gaussian mutation, insertion mutation, and reverse order operation to make the algorithm jump out of the local optimum. And in the iteration process, the fitness of the population is monitored, the historical global optimal solution is introduced timely to coordinate the local and global search ability of the algorithm. Finally, several TSP examples are tested and compared with the bat algorithm and ant colony algorithm. The results show that the novel chaos sparrow search algorithm has obvious advantages in solving the TSP problems, both in accuracy and efficiency.
Improving WordNet using Word Embeddings

The main objective of this paper is to create a proof of concept regarding the improvement of the human-generated database WordNet using computer-generated information from Word2Vec. Thus, we change the WordNet content, using the information from existing corpora. The main method used to achieve this goal is by comparing the results of path algorithms for computing the semantic similarities between WordNet concepts (such as Path, Wu and Palmer, or Leacock and Chodorow similarities), with cosine similarity between the Word2Vec vectors of the same concepts. One way to improve WordNet is by adding new concepts from the Word2Vec corpus which have strong connections with existing words from WordNet. Another way to improve it is by updating its existing connections to underline semantic change. Our experimental results prove that the method we propose may be used to improve the number of concepts and the quality of links between synsets in WordNet, creating a more meaningful semantic resource.
Leveraging Decentralized Internet of Things (IoT) and Blockchain Technology in International Trade

Supply chains in the modern era have developed into very complex value networks and have become a critical source of competitive advantage. Verifying the origin of raw materials and maintaining visibility of goods and merchandise as they move through the value chain network is difficult. The Internet of Things (IoT) application may assist businesses in observing, tracking, and monitoring items, activities, and processes inside their value chain networks. Both blockchain and Internet of Things technologies have the potential to accelerate the implementation of Industry 4.0 and transform foreign trade procedures. Integrated technology utilization may be a component of the answer to this future. This paper&#x2019;s contribution is to demonstrate how the marriage of Blockchain technology with IoT infrastructure may simplify and enhance contemporary supply chains in a variety of ways. Such as increasing scalability, security, immutability, and auditing, as well as information flows, traceability, and interoperability, and quality, using Ping Asset devices and Chainlink, and so setting the groundwork for future research projects.
Anti-spoofing Performance Enhancement by Facial Micro-expression Detection using Kinect Sensor

Face detection is a point of interest in many systems. Due to the reason of being less-intrusive characteristic. But various systems are not that much capable of preventing high-level facial spoofing attacks. The attacks are generally done by 3D printed masks or eye-cut photos etc. Detecting micro-expressions in this case can make those systems invulnerable to the attacks because micro-expressions are the only expressions that cannot be controlled. In this article, an approach to detect the micro-expressions of the face has been shown. With the successful detection of micro-expression, the liveness of the face can be detected using a histogram of gradient (HOG) descriptor. This descriptor is used to detect the change in pixel intensities. The descriptor has been applied on specific ROI of the face image i.e., two eyes, nose, and lips of the detected face. The dataset utilized in this project is self-created. The device used to capture the snaps of the dataset is Kinect Xbox One. This approach is effective in preventing such face spoofing attacks.
Cyberterrorism

The cyber threat landscape has continued to evolve with time and enhanced technology. With the advent of new breeds of terrorists and cybercriminals, the cyberterrorism debate has again wielded global attention. In this chapter, the author will attempt to delve deeper into the concept of cyberterrorism. Firstly, it will discuss the related issues which include the definition consensus, perception, and media abuse problems. The next section draws on parallels from research on cyber threats and terrorism based on six themes (i.e., modus operandi, domain, targets, impact, antagonists and motivations) to formulate a cyberterrorism conceptual framework. The third section will provide a hypothetical four-step cyberterrorism attack sequence and suggestions for countering cyberterrorism. This chapter will then conclude by highlighting several implications of interest.
A Multi-Face Recognition Framework for Real Time Monitoring

From the past few years, face recognition has become critical for security and surveillance applications, and is now necessary in many different settings, including offices, educational institutions, airports, corporations, and social spaces. In this paper, we present a framework of multi-face recognition for real time monitoring, resulting in simultaneous face tracking and recognition. First, the faces are detected in the video frames by using viola-jones algorithm. To remove the outliers from the detected face region, we design a face skeleton based on YCBCR color space for further feature points detection and extraction. Then harris corner feature points and SURF feature points are detected from each face, where harris points are used to track the faces in the video and the SURF feature points are used to extract facial features from the cropped faces. As the face tracking is going on, faces are simultaneously recognized by the trained classifier (support vector machine). The experiments conducted on publicly available dataset suggest that our method is reliable, accurate, and robust that can be deployed for real-world multi-face recognition systems.
The Tragedy of the Miners

In a network of mining pools that secure Bitcoin-like blockchains, it is known that a self-interested mining pool can dishonestly siphon off another pool’s mining rewards by executing a block withholding (BWH) attack. In this paper, we show that a BWH attack is always unprofitable for an initial startup period which is at least one difficulty retarget interval (approximately 14 days for Bitcoin). Furthermore, we prove that the payback period to recoup this initial startup cost is always at least as long as the initial unprofitable startup interval, and we show numerically that it can be substantially longer. Thus, the decision of whether or not to execute a BWH attack is not a dominant strategy, and the so called Miner’s Dilemma is not in fact a dilemma.
An Optimization Scheme for College Teacher Recruitment Management System Based on Blockchain and Text Recognition

At present, some colleges and universities use the recruitment system to complete the registration and review of candidates in the recruitment work, but the review work still requires reviewers to spend a lot of time to complete. In addition, candidates submit malicious incorrect registration information to disrupt recruitment, which not only increases the workload of reviewers, but also affects the efficiency of recruitment. Candidates need to upload scanned copies of certification materials (such as ID card, degree certificate, graduation certificate, etc.) when registering. Some candidates are worried about whether the information will be leaked and the reviewers are unfair in reviewing for personal gains. In response to these problems, this article proposes to add text recognition to the recruitment management system of a university teacher to complete the system's automatic registration review. Based on the traceability and non-tampering characteristics of blockchain technology, the security and traceability of registration information are guaranteed. Kind of optimization plan. By analyzing the system process, selecting a third-party API interface to complete text recognition, and combining the simple filtering function of the system to build an automated audit function of the system. Select the candidate's registration information as the traceability object, optimize the system process, write smart contracts, web3.js connect the system and the blockchain. Experiments have proved that the automated audit function is automatically executed after candidates submit the registration form, and the average audit speed is 550ms. Reduce the workload of reviewers and improve recruitment efficiency. If the candidate has questions about the review process, the candidate can retrospectively verify whether the registration information has been tampered with through the blockchain, which ensures the fairness of the recruitment work.
Rem9ja: Bitcoin Lightning Network Remittance Solution

This paper is concerned with the efficiency and cost of the transfer of money across international boundaries, in small sums for the settlement of transactions of a personal nature. The paper aims to illustrate the inefficiencies in the existing methods of executing these payments known as remittances. It demonstrated the viability of utilizing a second-layer network built atop the Bitcoin network, as an alternative to the existing financial infrastructure.
Selection SMEs of Batik Bangkalan Using Fuzzy Interval Type-2 Method based on Group Support System

The Covid-19 pandemic has caused the domestic and global economic sectors to experience turmoil. The impact of the pandemic has been most pronounced in the Small Medium Enterprise (SMEs) sector. Most SMEs experienced a decline in sales turnover, difficulties in obtaining financing/credit, problems in the distribution of goods, and difficulties in obtaining raw materials. The purpose of this research is to determine the selection of Decision Support System (DSS) SMEs Batik Madura Indonesian, in the face of the Covid-19 pandemic based on influential indicators. The method used is interval value fuzzy type-2 Technique for Others Reference by Similarity to Ideal Solution (TOPSIS) based on Group Decision Support System (GDSS). The method for processing expert data is interval value fuzzy type-2 Geometric Mean Aggregation (GMA). The research findings are the most influential indicator weights in the selection are a variety of batik motifs and online market places. The contribution of the research is to develop the fuzzy interval type-2 method to support SMEs selection decisions by modifying the midpoint on triangular Fuzzy Number interval based on GRUP (GDSS). Based on expert data, the comparison of accuracy between fuzzy type-2 with one point, and two points, the highest accuracy result is using fuzzy type-2 with two points.
Introduction

Almost everything that we hear about Artificial Intelligence (AI) today is thanks to Machine Learning (ML) and especially the ML algorithms that use neural networks as baseline inference models. This scientific field is called Deep Learning (DL). The core of deep learning is to design, train and deploy end-to-end trainable models that are able to use raw sensor information, build an internal representation of the environment, and perform inference based on this representation. Although this end-to-end training approach has been successfully followed for many different tasks ranging from speech recognition to computer vision and machine translation in the last decade, the big challenge for the next years is to successfully apply the same end-to-end training and deployment approach for robotics, which means to build models that are able to sense and act using a unified deep learning architecture. This chapter provides an introduction to real world problems representation under a deep learning perspective, basic machine learning tasks, shallow and deep learning methodologies, and challenges in adopting deep learning in robotics. Moreover, it provides an introduction to the topics of deep learning for robot perception and cognition covered in the book.
A Novel Hybrid Big Data Handling Optimized Architecture for Document Clustering

Document clustering is an important aspect of big data. Data growth has been directed to time for many days, and storing the data in a structured manner is an issue. Clustering is a strategy and procedure for automatically grouping together relevant documents. The clustering of documents is a solution for organizing important database items. Our paper's major purpose is to bring together pertinent records. This work proposes a hybrid big data handling architecture as a clustering technique that effectively clusters documents. This project uses the K-Means and Gaussian Firefly Algorithms to cluster documents and search for beginning values, as well as gauze to optimize and increase the accuracy of square and time errors. Finally, to update the cluster's initial values or centroid, we add the squared errors. The cluster's efficiency improves as a result of this. The experimental data is sufficient, and the simulation results are extremely trustworthy. With a low squared error sum, the high-quality solutions look quite promising.
MOGROM: Multiobjective Golden Ratio Optimization Algorithm

This study introduces the multiobjective version of a very recently proposed metaheuristic optimization algorithm known as the Golden Ratio Optimization Method (GROM). The proposed method, named Multiobjective GROM (MOGROM), uses an external Repository matrix for storing the best obtained Pareto Front that is achieved by nondominated sorting of the whole population. The members of the Repository are also sorted based on a crowding distance concept to calculate the best and the worst individuals corresponding to the highest and lowest crowding distance, respectively. To evaluate the performance of the proposed algorithm, 22 multiobjective standard test functions with different Pareto Front shapes and search spaces are employed. The obtained results using the MOGROM are compared to those of five well-known multiobjective optimization algorithms including NSGA-II, MOPSO, MOALO, MOGWO, and MOLAPO. Four different criteria, namely, Generational Distance (GD), Inverted GD, Metric of Spread, and Metric of Spacing, are also employed for the sake of results’ comparison. The comparisons demonstrate the excellent performance of the proposed method in solving different types of multiobjective problems.
Deep Learning Architecture Reduction for fMRI Data

In recent years, deep learning models have demonstrated an inherently better ability to tackle non-linear classification tasks, due to advances in deep learning architectures. However, much remains to be achieved, especially in designing deep convolutional neural network (CNN) configurations. The number of hyper-parameters that need to be optimized to achieve accuracy in classification problems increases with every layer used, and the selection of kernels in each CNN layer has an impact on the overall CNN performance in the training stage, as well as in the classification process. When a popular classifier fails to perform acceptably in practical applications, it may be due to deficiencies in the algorithm and data processing. Thus, understanding the feature extraction process provides insights to help optimize pre-trained architectures, better generalize the models, and obtain the context of each layer's features. In this work, we aim to improve feature extraction through the use of a texture amortization map (TAM). An algorithm was developed to obtain characteristics from the filters amortizing the filter's effect depending on the texture of the neighboring pixels. From the initial algorithm, a novel geometric classification score (GCS) was developed, in order to obtain a measure that indicates the effect of one class on another in a classification problem, in terms of the complexity of the learnability in every layer of the deep learning architecture. For this, we assume that all the data transformations in the inner layers still belong to a Euclidean space. In this scenario, we can evaluate which layers provide the best transformations in a CNN, allowing us to reduce the weights of the deep learning architecture using the geometric hypothesis.