Reviewerly/exHarmony huggingface dataset:
Assessment and Evaluation of cancer CT images using deep learning Techniques

Image detection and extraction play's a significant role for automation and image analysis process. In the field of medical domain, the extraction of features from real-time images of cancer CT has becoming challenging. The incorporation of machine learning in medical data could explore the salient features that could be used for exploring patterns which then aids in decision making process.Motivation The realm of determining the accuracy of the observed medical image serves as an important factor in accessing image quality and recognition. The quality of image acquisition and analysis lies at quality factors such as range, sharpness, color, fare of lens, artifacts, and tone reproduction. The mechanism of automatic prediction is being useful for a number of practical applications, but if there is a systematic means of accessing the sections of poor quality of subsequent images in medical informatics it can be helpful for assessment and evaluation to determine flags and noise thereby rendering quality-based fusion on all observed images.Objective The objective is to validate the accuracy of cancer CT images using machine learning techniques. The assessment is made in accordance with the deployment of Convolution Neural Network with the enhancement in the image quality features.Methodology The process of assessment and evaluation involves training the dataset with proposed quality metrics. Once trained then it is modeled using CNN with ELM and its training parameters. Once modeled then tested accordingly with automatic prediction with quality factors and detecting sections of poor fusion focusing on interpretation and evaluation.Results and Conclusion The analyses of lung Computerized Tomography (CT) has various interventional variations which can significant degrade the level of accuracy. The NIST FRTV rate has FNMRs of lesser that 3% at the level of 0.01% FMR for higher learning algorithms. When assuming at the level of 0.1% the level of magnitude moves to a higher level of variations. Video frames if received will also have some of the variations in higher level of magnitude. In order to improve the quality metrics, we proposed a new model for predicting the level of cancer using Deep Convolution Extreme learning machine.
Developing a Mobile Learning Application for Preschooler

Mobile learning is a trend method that people used to study online nowadays. There is a lot of different study methods in mobile learning. Although mobile learning has been widely used for primary school, secondary school or university and college but still yet to fully deploy in preschools age. Mobile learning can also be provided to preschoolers similar to university platforms, but for children or kids, game based mobile learning will be more suitable for them. This research will study the requirements of having a mobile learning application and to develop a mobile learning application for children age from 3 to 5 years old.
Implicit Measures as a Useful Tool for Evaluating User Experience

With the increasing presence in our lives of interactive and technological items, the concept of “experience” becomes crucial in both the design and the evaluation phases. When aiming to measure such experience, researchers typically adopt self-report methodology. However, it has been established that also unconscious motives (i.e., emotions, implicit attitudes) - which people are not always aware of - need to be taken into account in understanding users’ perception, by means of indirect measures. The present study investigates the possibility of measuring UX by both implicit and explicit attitudes measures. In an experimental study (N = 36), we asked participants to evaluate a conversational chatbot prototype, which had the fictional goal of pre-selecting candidates for a job, and to fix a date for the job interview. We manipulated between-participants the chatbot’s gender (male vs. female) and the tone of voice (formal vs. informal). Explicit user experience’s evaluation (i.e., UEQ) and implicit attitudes towards the chatbot (i.e., IAT) were the dependent variables, as well as self-reported measures of chatbot’s efficacy and emotional experience. Our findings showed an implicit preference towards the informal version of the chatbot, as revealed by the IAT scores, whereas no differences emerged on explicit measures of the UEQ. Results are discussed in light of the effect of implicit attitudes on overall experience, and on the importance of comparing implicit and explicit measures, as well as objective usability measures, when evaluating the UX.
Text Classification Using FP-Growth Association Rule and Updating the Term Weight

Text classification plays a vital role in many real-life applications. There are different methods for text classification primarily Naive Bayes classifier, support vector machine, etc. A good text classifier must efficiently classify large set of unstructured documents with optimal accuracy. Many techniques have been proposed for text classification. In this paper, we propose an integrated approach for text classification which works in two phases. In initial preprocess phase, we select the frequent terms and adjust the term weight by use of information gain and support vector machines. Second phase consists of applying Naïve Bayes classifier to the document vector. The experiment has been performed on the open research dataset of Forum of Information Retrieval (FIRE). In association rule, the correlation between data items is obtained with no requirement of external knowledge, whereas in classification, attention is given to small set of rules with the help of external knowledge. The proposed work uses FP-growth algorithm with absolute pruning for obtaining frequent text sets, and then, Naïve Bayes classifier model is used for training and constructing a model for classification. Our experimental result shows increase in efficiency while comparing with other traditional text classification methods.
Designing the Empathetic Research IoT Network (ERIN) Chatbot for Mental Health Resources

Grounded in the user experience driven innovation (UXDI) framework, we designed and developed a chatbot, ERIN, to help college students with finding resources about sensitive issues such as mental health and Title IX. ERIN was designed to be accessed via different devices. Throughout the design process, the analysis of user interviews suggested that the service experience of the chatbot and its adoption may strongly be influenced by the medium through which it is accessed. To test this possibility, we conducted an experiment comparing user reactions to the chatbot using two different devices: laptop and smart phone. The preliminary results showed that user experience of the chatbot was almost significantly better in the mobile group and people in that group were almost significantly more likely to adopt the chatbot. These results and their implications are discussed.
Proposal of Wisdom Science

This paper proposes a new field of knowledge that aims to integrate explicit knowledge and tacit knowledge, with quantitative treatment. The aim of wisdom science is to formulate a theory of knowledge based on brain activities, and on more detailed level, on neuron functions. Tacit and explicit knowledge are treated as two facets that represent knowledge of a person, differing from conventional interpretation of two independent entities. The hypernetwork model is used to describe both tacit and explicit knowledge, which is capable of representing structures that cannot be described by conventional system models. The concept of system science serves as the basic framework for the modeling and analysis.
Machine Learning, Compositional and Fractal Models to Diagnose Soil Quality and Plant Nutrition

Soils, nutrients and other factors support human food production. The loss of high-quality soils and readily minable nutrient sources pose a great challenge to present-day agriculture. A comprehensive scheme is required to make wise decisions on system’s sustainability and minimize the risk of crop failure. Soil quality provides useful indicators of its chemical, physical and biological status. Tools of precision agriculture and high-throughput technologies allow acquiring numerous soil and plant data at affordable costs in the perspective of customizing recommendations. Large and diversified datasets must be acquired uniformly among stakeholders to diagnose soil quality and plant nutrition at local scale, compare side-by-side defective and successful cases, implement trustful practices and reach high resource-use efficiency. Machine learning methods can combine numerous edaphic, managerial and climatic yield-impacting factors to conduct nutrient diagnosis and manage nutrients at local scale where factors interact. Compositional data analysis are tools to run numerical analyses on interacting components. Fractal models can describe aggregate stability tied to soil conservation practices and return site-specific indicators for decomposition rates of organic matter in relation to soil tillage and management. This chapter reports on machine learning, compositional and fractal models to support wise decisions on crop fertilization and soil conservation practices.
Cybersecurity Threats in Line with Awareness in Saudi Arabia

This study aims at exploring the cybersecurity threats in line with the awareness of Saudi Arabia nationals. This paper analyzes the recent threats reports conducted by IBM, McAfee and Kaspersky. Then this paper matches the analyzed threats reports with the awareness of the Saudi nationals by examining five surveys of related studies. The results of the analysis indicate the following: Saudi Arabia is considered one of the main targets in cybersecurity with a very costly data breach. Healthcare, Financial, technology, Multiple, Individual/private, and public sectors should be given a high priority and must implement extensive trainings and awareness programs. Also, malware, account hijacking, system errors are the most widely and costly threats and awareness should be targeting these threats. Also, the study concludes that most Saudis are aware of the security tools but there are a large number that do not use nor know about these tools. The study also has found that a large number of Saudis did not receive any awareness nor training programs and businesses are not paying much attention in this regard. The study also has found that a huge number of Saudis do not know how nor want to report cybercrimes.
A sequence models-based real-time multi-person action recognition method with monocular vision

In intelligent video surveillance under complex scenes, it is vital to identify the current actions of multi-target human bodies accurately and in real time. In this paper, a real-time multi-person action recognition method with monocular vision is proposed based on sequence models. Firstly, the key points of multi-target human body skeleton in the video are extracted by using the OpenPose algorithm. Then, the human action features are constructed, including limb direction vector and the skeleton height-width ratio. The multi-target human bodies tracking is then achieved by using the tracking algorithm. Next, the tracking results are matched with the action features, and the action recognition model is constructed, which includes the spatial branch based on Deep neural networks and the temporal branch based on Bi-directional RNN and Bi-directional long short-term memory networks. After pre-training, the model can be used to recognize the human body action from action features, and a recognition stabilizer is designed to minimize false alarms. Finally, extensive evaluations on the JHMDB dataset validate the effectiveness and the superiority of the proposed approach.
Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem

This paper considers the problem of multi-agent distributed linear regression in the presence of system noises. In this problem, the system comprises multiple agents wherein each agent locally observes a set of data points, and the agents' goal is to compute a linear model that best fits the collective data points observed by all the agents. We consider a server-based distributed architecture where the agents interact with a common server to solve the problem; however, the server cannot access the agents' data points. We consider a practical scenario wherein the system either has observation noise, i.e., the data points observed by the agents are corrupted, or has process noise, i.e., the computations performed by the server and the agents are corrupted. In noise-free systems, the recently proposed distributed linear regression algorithm, named the Iteratively Pre-conditioned Gradient-descent (IPG) method, has been claimed to converge faster than related methods. In this paper, we study the robustness of the IPG method, against both the observation noise and the process noise. We empirically show that the robustness of the IPG method compares favorably to the state-of-the-art algorithms.
Topological Quantum Codes from Lattices Partition on the n-Dimensional Flat Tori

In this work, we show that an n-dimensional sublattice Λ′=mΛ of an n-dimensional lattice Λ induces a G=Zmn tessellation in the flat torus Tβ′=Rn/Λ′, where the group G is isomorphic to the lattice partition Λ/Λ′. As a consequence, we obtain, via this technique, toric codes of parameters [[2m2,2,m]], [[3m3,3,m]] and [[6m4,6,m2]] from the lattices Z2, Z3 and Z4, respectively. In particular, for n=2, if Λ1 is either the lattice Z2 or a hexagonal lattice, through lattice partition, we obtain two equivalent ways to cover the fundamental cell P0′ of each hexagonal sublattice Λ′ of hexagonal lattices Λ, using either the fundamental cell P0 or the Voronoi cell V0. These partitions allow us to present new classes of toric codes with parameters [[3m2,2,m]] and color codes with parameters [[18m2,4,4m]] in the flat torus from families of hexagonal lattices in R2.
Recognition of emotions in verbal messages based on neural networks

Emotion detection and recognition by text is an under-researched area of natural language processing (NLP), which can provide valuable input in various fields. Speech and Emotion Recognition (Speech Emotion Recognition SER) has potentially wide applications, such as interaction with robots, banking, call centers, car onboard systems, computer games, etc.
Spherical Search Algorithm: A Metaheuristic for Bound-Constrained Optimization

This chapter is based on a recently published paper [9] of authors of this chapter in which a method for solving bound-constrained non-linear global optimization problems has been proposed. The algorithm obtains a sphere and then generates new trial solutions on its surface. Hence, this algorithm has been named as Spherical Search (SS) algorithm. This chapter starts with an introduction to the SS algorithm and then discusses different components and steps of the algorithm, viz., initialization of population, the concept of a spherical surface, the procedure of generation of trial solutions, selection of new population using greedy selection, stopping criteria, steps of the algorithm, and space and time complexity of the algorithm. Then, the algorithm has been applied to solve 30 bound-constrained global optimization benchmark problems of IEEE CEC 2014 suite and the results of the spherical search algorithm on these benchmark problems have been compared with the results of variants of well-known algorithms such as particle swarm optimization, genetic algorithm, covariance matrix adapted evolution strategy, and Differential Evolution on these problems to demonstrate its performance. Further, the SS algorithm has been applied to solve a model order reduction problem, an example of a real-life complex optimization problem.
Digital Competence Assessment Survey for Language Teachers

In this paper, we present the results of the digital competence assessment survey for language teachers. The survey assessed how language teachers use digital technologies, their attitude towards these technologies, their related skills and competencies, their satisfaction and training needs, and the institutional support they receive. In total, 283 language teachers from 43 countries participated in the survey. The data were collected in 2019. The results indicate that language teachers use various computer-assisted language learning instructional methods. They generally consider that digital technologies are beneficial for the classroom. The lack of training prevents them from using specific technology-based methodologies, in contrast to the lack of technical infrastructure. The majority of the participants are not satisfied with their level of digital language teaching expertise. Two-thirds of those who receive digital competency training at their organizations, report that the training sessions happen irregularly or rarely. At the same time, the majority of the teachers confirm these training to be effective and the skills they received are applied in practice. We discuss the results of the survey in relation to the previous research, policies and practical challenges of the digitalization of language education. The major contribution of the paper is a representation of the teacher’s perspective on the role of digital technologies in their practice. We assume that the new realia of teaching in the context of COVID-19 have changed the overall need in digital language teaching skills among the teachers, as well as the share of the teaching staff who teach online on a regular basis. The future work includes a new survey in order to evaluate the scope of this change.
Extending defoe for the Efficient Analysis of Historical Texts at Scale

This paper presents the new facilities provided in defoe, a parallel toolbox for querying a wealth of digitised newspapers and books at scale. defoe has been extended to work with further Natural Language Processing () tools such as the Edinburgh Geoparser, to store the preprocessed text in several storage facilities and to support different types of queries and analyses. We have also extended the collection of XML schemas supported by defoe, increasing the versatility of the tool for the analysis of digital historical textual data at scale. Finally, we have conducted several studies in which we worked with humanities and social science researchers who posed complex and interested questions to large-scale digital collections. Results shows that defoe allows researchers to conduct their studies and obtain results faster, while all the large-scale text mining complexity is automatically handled by defoe.
A physics-guided reinforcement learning framework for an autonomous manufacturing system with expensive data

Making intelligent decisions is the biggest challenge in building an autonomous manufacturing system that can build artifacts with desired properties without human intervention. Although reinforcement learning (RL) has favorable characteristics for such a task, the sample efficiency of RL is poor, which makes it difficult to implement on a manufacturing system due to the expense of producing parts to collect reward and action data. This paper focuses on building a framework for implementation of RL on manufacturing systems with expensive data and presents the framework for autonomous manufacturing of Phononic Crystals (PnCs), a type of acoustic metamaterial. Leveraging knowledge from physics-guided models and temporal abstraction ideas, we detail a framework that reduces the task of finding optimal manufacturing parameters from thousands of manufacturing samples to the order of 50 samples. The method is applied in simulation to a stochastic model of PnC production. Critically, we show that by using a long temporal abstraction horizon and order of 50 sample budget, the RL algorithm finds the optimal region greater than 95% of the time.
STEM Excellence and Equity in K-12 Settings: Use of Augmented Reality-Based Educational Experiences to Promote Academic Achievement and Learner Success

This exploratory paper examines the degree to which Extended Reality (XR) and more specifically, Augmented Reality (AR) technology along with experiential learning pedagogy, can be used to facilitate academic readiness, engagement, and motivation of underserved populations in the STEM fields. We examine the literature and approaches currently being used to help students and other learners gain the technical and behavioral skills needed to succeed in STEM-related courses. We specifically pay attention to studies involving teachers’ use and integration of XR/AR applications and other advanced technology tools and applications, even at the exploratory level, to determine readiness and ease of use of those applications as part of regular school activities. We outline some pedagogically-grounded ideas and suggestions to promote excellence and equity in K-12 STEM education, teacher training, and professional development.
Performer Experience on a Continuous Keyboard Instrument

Abstract On several keyboard instruments the produced sound is not always dependent exclusively on a discrete key-velocity parameter, and minute gestural details can affect the final sonic result. By contrast, variations in articulation beyond velocity have normally no effect on the produced sound when the keyboard controller uses the MIDI standard, used in the vast majority of digital keyboards. In this article, we introduce a novel keyboard-based digital musical instrument that uses continuous readings of key position to control a nonlinear waveguide flute synthesizer with a richer set of interaction gestures than would be possible with a velocity-based keyboard. We then report on the experience of six players interacting with our instrument and reflect on their experience, highlighting the opportunities and challenges that come with continuous key sensing.
Non-dominated Sorting Manta Ray Foraging Algorithm with an Application to Optimize PD Control

This paper presents a multi-objective (MO) version of Manta Ray Foraging Algorithm (MRFO) by using components in Non-Dominated Sorting Genetic Algorithm (NSGAII). MFRO is a recent developed algorithm which was inspired from behavior of a cartilaginous fish called Manta Ray. MRFO search solution by using three strategies of manta ray which are chain foraging, cyclone foraging and somersault foraging. However, this algorithm solves only single-objective problem and can be improved to solve multi-objective problem. Thus, non-dominated sorting (NS) strategies including crowding distance (CD) were adopted into MRFO. NS is a sorting technique based on Pareto’s game. It is a fast strategy to develop a good characteristic of Pareto’s front (PF). Meanwhile, CD is a strategy to preserve good distribution of solutions along the PF. This proposed algorithm is called NSMRFO. It is tested using several benchmark functions and its performance is compared to its parent by using statically analysis of hypervolume indicator. Then, it is applied to a Proportional-Derivative (PD)-controller for an Inverted Pendulum System (IPS) in order to know its performance on real-world application. Result of the NSMRFO on benchmark functions shows that it outperforms NSGAII and satisfactorily optimizes PD-control for the IPS.