Reviewerly/exHarmony huggingface dataset:
Evolution of Sentiment Analysis: Methodologies and Paradigms

With the advent of the digital age, almost everything has come down to better understanding of the data. Natural language processing is equally in pursuit and is rather among the most researched areas of computer science. Post 1980, a major revolution in NLP embarked with the emergence of machine learning algorithms resulting from steady escalation in computational power. Unlike other data, text semantics becomes more complex both because of its contextual nature and daily evolving language usage. While the continuous efforts of improving language representation for logical units interpretation is still prevalent, much to our realization, traditional, and long established recurrent neural networks which were supposed to grasp a bi-directional context of language have been surpassed by attention models in constructing improved embeddings allowing systems to better understand language. Among numerous applications circumventing, understanding sentiment of text has been widespread in fields including but not limited to customer reviews, stock market, elections, healthcare analytics, online, and social media analytics. From binary classification of it to more challenging cases such as negation handling, sarcasm, toxicity, multiple attitudes, or polarity, this research chapter explores the evolution of sentiment analysis in the light of emerging text processing and the transition of text understanding from rule-based to a statistical one with a comparison of benchmark performance from state-of-the-art models over various applications and datasets.
Search Method of Number of Trees for Genetic Programming with Multiple Trees

Genetic programming (GP), which is an evolutionary computational method, is known to be effective for agent problems because individuals are represented by a tree structure. As an extension method, GP with control nodes (GP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CN</sub> ) has been proposed. Because one individual has multiple tree structures, GP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CN</sub> can efficiently evolve and obtain highly readable behavioral rules. However, the number of trees suitable for each problem has to be manually adjusted in advance and cannot be easily applied various problems. In the previous study, a method for automatically determining the number of trees have proposed. However, because the method of the previous study changes the fitness function and uses a special population, it cannot be combined with the extension methods to improve the evolution performance. In this study, a method for searching for the appropriate number of trees using three islands is proposed. The proposed method divides the population into three islands, but because the genetic operations and the fitness function of each island are not changed, it can be combined with the existing extension methods. In the experiments, they are compared these using two benchmark problems.
Overview of Quantum Technologies, Standards, and their Applications in Mobile Devices

In this paper, we summarize the quantum mechanics that define quantum technologies (quantum states, superposition, entanglement, and decoherence), introduce modern quantum technologies and their broader applications (quantum computing, quantum communication, quantum cryptography, quantum internet, and quantum error correction), examine the state quantum technology standards, and discuss how quantum technologies relate to the mobile world. For decades, methods for quantum technologies have been theorized or proven mathematically. Real implementations of these methods exist in modern devices such as lasers and magnetic resonance imaging (MRI) scanners. However, the precise manipulation of quantum particles - the fundamentally probabilistic building blocks of our world - is still in its relative infancy, with technologies that depend on manipulation being usable but limited in either function or adoption.
The Proteus effect on security and safety exergamers

The Proteus effect of exergames has been widely regarded as a factor that influences human performance and learning. Within the context of security and defense exergames, identification with a digital alter-ego affects students’ task performance as well as their emotional states. In the present study, we examined the influence of the perceived masculinity of male avatars on players who were engaged in gamified military exercises (based on competition and command) in a three-dimensional environment. Analysis of data that were obtained from 51 male learners using semi categorized interviews revealed that, during the game, participants with more masculine avatars were more self-confident, engaged with the task, and obtained higher scores than their counterparts with less masculine avatars. Moreover, when they adopted an avatar that they perceived to be more masculine, they reported improved mood, self-confidence, and decisiveness after the completion of the game.
Inner Join Query Performance: MariaDB vs PostgreSQL

Abstract Research to test the performance of inner joint queries between MariaDB and the PostgreSQL database management system. Inner joint queries have the same output in MariaDB and PostgreSQL DBMS. This study aims to provide a comparative analysis of the response time performance of MariaDB and PostgreSQL databases in the use of inner join queries. The research test was conducted with 1,050,000 records using 21 response time data collection scenarios ranging from 50,000 data to 1,050,000 data collection with multiples of 50,000 data. The PostgreSQL DBMS was found to have better response times than MariaDB in all test scenarios. The results of the regression analysis to test the effect of using the F-test show that there is a significant effect on the amount of data and the relationship together on the response time in MariaDB and PostgreSQL. Then the T-test is carried out to strengthen the results of the study that PostgreSQL has a faster response time than MariaDB, testing 1 combined relationship shows a significant difference, testing 2 combined relationships shows a significant difference and testing 3 combined relationships shows a significant difference.
Toward Interpretable Machine Learning for Understanding Epidemic Data

The COVID-19 pandemic is a worldwide crisis with impacts that are both devastating and inequitable as effects often fall hardest on communities that are already suffering from economic, social, and political disparities. Interpretable machine learning (IML) offers the possibility for detailed understanding of this and similar disease outbreaks, allowing subject matter experts to explore the data more thoroughly and find patterns and connections that might otherwise remain hidden. As an active area of research in artificial intelligence, IML has great significance yet numerous technical challenges to overcome. In this paper, we focus on approximating epidemic curves using an interpretable artificial neural network. This is a first step toward a flexible and interpretable modeling framework that we plan to use to study impacts of various demographic, socioeconomic, and other factors on disease outbreaks. We tap into a substantial but little-known collection of IML studies in nonlinear function approximation from engineering mechanics, where domain knowledge including visually observable features of the data is systematically sorted and directly utilized in the initialization of sigmoidal neural networks leading to training success and good generalization. After an introductory review of existing work, we present a feasibility study on approximating a particular epidemic curve leading to a promising result.
Combining Cinematic Virtual Reality and Sonic Interaction Design in Exposure Therapy for Children with Autism

This chapter presents a preliminary study whose goal is to investigate the benefits of cinematic virtual reality combined with sonic interaction design in exposure therapy for autistic children. A setup was built with two players, one child and one guardian, which together could virtually interact during a children’s concert. Results of an evaluation test in a school for children with special needs shows the potential of VR for exposure therapy.
Isolated Handwritten Digit Recognition Using LPQ and LBP Features

Several approaches for handwritten digits recognition are proposed an appearance approach based on feature extraction. In this paper we process handwritten digit image without any normalization method using Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) extracted from the complete image as well as from different regions of the image by applying a uniform grid sampling to the image. LBP and LPQ is a very efficient feature descriptor for handwritten which is arise from variations in size, shape and slant. Moreover, the SVM has been employed as classifier which has better responses. The experimental study is conducted on CVL dataset and achieved high recognition rates which is comparable with the state of the art.
Using Class Activation Maps on Deep Neural Networks to Localise Waste Classifications

A serious global waste crisis is currently in effect which originates from our lack of sense of duty. This can be resolved by automating the separation process using AI empowered by weakly supervised learning. A prototype system was created by using pre-trained CNN models in CV such as VGG, ResNet, MobileNet and DenseNet. The prototype showed promising results by having the best algorithms obtain an F1-score of over 80% on 2 datasets known as TrashNet and MINC. Some algorithms were also quite efficient, reaching over 10FPS while maintaining less than 10Mb. The localisation accuracy generated from the CAMs of the best models has shown to be around 83% on TrashNet and around 69% on MINC. These results show that not only is it possible through AI to accurately and efficiently classify waste through datasets, but it can also be used to integrate accurate localisation via weak supervision for easier data annotation.
Automatic Event Extraction method for Analyzing Text Narrative Structure

This paper presents an analysis of contemporary methods for event extraction from text narratives and of various event expression formats. It also briefly discusses future directions in narrative understanding and generation using artificial intelligence. The three-step study method for extracting events from text stories, comprising token analysis and part-of-speech tagging, dependent parsing, and standardization work, is analyzed. Expressions created using a tuple format are compared and contrasted with expressions created using the 5W format. Finally, we propose a novel method to organize events in a tuple format, reconstructing compound and complex sentences as simple sentences. Our method identifies and extracts verbs, subject, object, and preposition phrases. It then automatically extracts the multiple events that comprise each sentence.
Challenges Regarding Scientific Transcription in Virtual Office Hours

Mathematics teachers face great difficulties during virtual communication with their students, mainly because scientific and technical subjects use a great many mathematical symbols, abstract objects and a wide variety of technical nomenclatures that are not directly represented on the keyboards of computers, tablets or other communication devices. This paper aims to facilitate oral and written communication between teachers and students in mathematics subjects. In this regard, we describe realistic procedures for use in virtual office hours. We analyse different methods that combine the use of computers, tablets, webcams and an institutional video lecture system. The improvement in oral and written communication will have a positive effect on academic performance and decrease the dropout rate in distance learning courses, because the students feel more closely accompanied in their learning.
A New Bio-heuristic Hybrid Optimization for Constrained Continuous Problems

A novel bio-inspired evolutionary algorithm known as MoFAL is presented in this article. The proposed algorithm (MoFAL) is based on the hybrid amalgamation of two nature inspired methods based on Moth Flame Optimization and Ant Lion Optimizer algorithms. It is well known that elitism forms an important characteristic of evolutionary algorithms that allows them to maintain the best fitness(es) obtained at any stage of the optimization process. MoFal is bench-marked using a set of 23 classical benchmark functions employed to test different characteristics during its evolutionary computation process. Numerical experiments demonstrate that the solutions of the constrained optimization problems like Pressure Vessel and the Rolling Element Bearing designs found using our algorithm are highly accurate and their convergence is comparatively fast coupled with improved exploration, local optima avoidance and exploitation. The results clearly exhibit that MoFAL algorithm is capable of finding superior optimal designs for our case study problems that include diverse search spaces. Our algorithm is able to determine global solutions of constrained optimization problems more efficiently than traditional evolutionary algorithms, and also avoid the occurrence of premature phenomena during its convergence process.
Object Detection and Classification for Autonomous Vehicle

Abstract The proposed work presents efficient objection detection and tracking algorithm for autonomous vehicles, which is developed in MATLAB with Image Processing Computer Version Toolbox and Automated Driving Toolbox. The developed algorithm track and detects moving and stationary objects such as other vehicles, pedestrians, and traffic lanes. Accurate and efficient tracking are important to analyze object behavior. For this work various build in detectors from MATLAB tool box were tested and compared. The evaluation of algorithm was carried on for 19 short videos from 8 seconds to 23 seconds, and then it applied to K-dataset and full road experiments by the Automated Driving Lab. The full road tests are between one to five minutes, including CAR to CAR WEST, CAR WEST to CAR, and campus marked roads.
Approximate Distance-based Anomaly Detection at Massive Scale

Anomaly detection is valuable in many domains. Distance-based algorithms are one of the most well-known techniques for anomaly detection. These algorithms have been shown to produce good accuracy in a wide range of applications. However, distance-based algorithms are limited in terms of scalability. This makes it difficult for these algorithms to handle massive data sets. To overcome this limitation, in this paper we propose a parallel formulation of the popular KNN distance-based algorithm for unsupervised anomaly detection. Our formulation focused on improving scalability by analyzing in parallel different parts of the data. This approach is valuable whenever the analyzed data does not fit comfortably into memory, or when it is distributed across several locations. To reduce synchronization latency, the proposed algorithm computes approximate nearest neighborhoods. We show, both theoretically and empirically, how this approach greatly improves scalability without significantly penalizing detection accuracy. To validate our approach, we perform extensive experiments on both synthetic and real-world data sets.
Making Tangible the Intangible Gestures of Craft

This chapter explores human activity analysis in craft-based human computer interaction. It introduces a novel method of human activity analysis leveraging beauty technology and robotic colour sensing cameras to trace the hand gestures of craftspeople. The method is contextualised by a review of current gesture tracking techniques. A case study is presented tracing the intangible cultural heritage practice of horsetail embroidery of the Shui Ethnic Minority in China. The significance of the novel method is that it offers a technique to digitally record, analyse and archive the intricate dynamics of craft practices without disturbing natural interaction between materials and processes. The chapter goes on to describe potential applications of the system, using the traced data as a medium for generative creative processes. Future critical and speculative design outcomes are described. The research is located at the intersection of physical and digital interaction. It describes hybrid materialities and methods of practice. The chapter promulgates a renewed focus on material agency, in futures of resource scarcity.
Educational Environment in the Context of Digitalization

The article notes that education is the most advanced area in terms of digitalization of business processes. This process is facilitated by the active use of information and communication technologies (ICT) by the younger generation; the development of the digital economy as a new stage in the development of the information society, requiring new skills, competencies and skills from employees; active introduction of digital technologies in all spheres of human life. In Russia, a digital educational environment is being formed that simplifies access to modern educational resources, not only national but also international. This expands the students’ ability to create their own educational path and allows you to communicate with many participants in the educational process. The authors of the article draw attention to the fact that the digitalization of education, as well as other areas of the economy, brings to life problems and difficulties associated not only with the ability to use modern digital technologies, but also with other aspects of human life and activity - psychological, physiological, moral, moral, ethical, pedagogical, etc.
A keypoint‐based object detection method with wide dual‐path backbone network and attention modules

Keypoint-based object detection is one of the most efficient and speedy methods at present, yet its performance is often worse than the anchor-based method. Without prior settings in the keypoint-based method, the huge search space of the keypoints results in the high recall but low precision. In this paper, the wide dual-path backbone network is introduced as a feature extractor to extract richer original information, which has fewer parameters and better classification performance. Then, the attention fusion module is designed to effectively fuse the dual-path with the consideration of the respective advantages of the residual-path and the densely connected path. In order to provide more accurate pixel-level information for keypoint prediction, the upsample dual-attention module is proposed to recover the spatial size of the feature map, which integrates multi-scale of channel-wise and spatial attention. Compared with other state-of-the-art detectors, this method has achieved accuracy-efficiency results with fewer parameters, lower FLOPs, and smaller model size. Experimental results show that the proposed wide dual-path backbone network has achieved 4.98% top1-error on the CIFAR-10 classification dataset. On the PASCAL VOC object detection dataset, this model has achieved an accuracy-efficiency tradeoff result of 78.3% mAP at the speed of 41 FPS.
Face Detection using Local Patterns in FPGA

Face detection is currently one of the most common technologies used in a variety of applications. This paper presents the design and implementation of face detection with some local patterns for achieving real-time performance. It is designed to process the image received in real time from an industrial camera using local patterns to detect the face on the FPGA. Each development result is separately analyzed specifically in terms of hardware resources.
Research on the Whole Teaching Process and Influencing Variables in Higher Vocational Colleges

This paper studies the whole process of teaching in higher vocational colleges. The whole process of teaching and its management are the core parts of school teaching and management. We have studied the teaching process theories at home and abroad and defined the teaching process in higher vocational colleges. The research group analyzed the input and output influencing variables in the whole process of higher vocational teaching, and noticed that the operation and management of higher vocational teaching is to manage the variation of the whole process of teaching. According to the 80/20 rule, 20% of the important influencing variables in the whole process of higher vocational teaching are well controlled, so as to add value to teaching activities and teaching processes, and make students' learning and teaching results be integrated with knowledge and practice.
Clinical Sublanguage Trend and Usage Analysis from a Large Clinical Corpus

The field of clinical natural language processing (NLP) has been built on the analysis of clinical sublanguage characteristics. It is well recognized that not only does clinical sublanguage differ from general English (or other languages) but also clinical sublanguage differs among clinical subspecialties and among corpora originating from different healthcare systems. A less recognized aspect is that clinical sublanguage, like all languages, evolves over time. This paper analyses the evolution of clinical sublanguage using a large, national clinical text corpus spanning 15 years. Through the analyses of document types, length, ngrams, and concepts, we found strong evidence that clinical sublanguage does evolve and such changes have implications for NLP development and maintenance. Although the analysis is performed on one corpus, our observations of sublanguage changes are generalizable.
Pandemic Opens Opportunities to Solve Pathology Manpower Challenges Worldwide

Conventional processes of pathology education have been disrupted by the pandemic of 2020, forcing a re-evaluation of the means of teacher-learner interactions. Digital and remote teaching methods have become the standard, spearheaded by several national and international organizations and enforced by a need to social distance during a pandemic. At the same time, the public health predictions regarding the impact of non-communicable diseases on the developing world, and the resultant increased demand for pathology services to help cope with that tidal wave of disease, presents a challenge to a declining population of pathologists in North America. However, the shift in teaching methods has produced a wealth of on-line training resources. This offers for the first time a leveraging effect on the teaching value of pathology expertise, largely concentrated in the developed world, and an opportunity to both enhance and equitize training internationally, potentially sufficient to meet the rising wave of non-communicable diseases ahead in the next 20 years. We propose four next steps to take advantage of the current opportunity to meet this challenge: Curate and organize digital training materials; Invest in the digital pathology infrastructure for education and clinical care; Expand student exposure to pathology through virtual or in person electives; Develop further competency-based rather than time-based certification and board eligibility.