Reviewerly/exHarmony huggingface dataset:
Argumentation Profiles

An argumentation profile is defined as a methodological instrument for analyzing argumentative discourse considering distinct and interrelated dimensions: the types of argument used, their quality, and the emotions triggered. Walton’s theoretical contributions are developed as a coherent analytical and multifaceted toolbox for capturing these aspects. Argumentation schemes are used to detect and quantify the types of argument. Fallacy analysis and the assessment of the implicit premises retrieved through the schemes allow evaluating arguments. Finally, the frequency of emotive words signals the most common emotions aroused. This method is illustrated through a corpus of argumentative tweets of three politicians.
Blockchained Industry Information Handoff Based on Internet of Things Devices with Intelligent Customized Object Recognition

To determine the quality and safety of each product used in manufacturing, the exchange of measured data between machines, operators, production lines, and manufacturing companies is crucial. In this study, we developed a system with customized object recognition capability for the secure blockchain-based transfer of industry information through Internet of Things (IoT) devices. In the proposed system, product history data are transferred through blockchains through artificial intelligence (AI)-based object recognition. Individual objects are recognized and represented using a unique number sequence for use as a private key on a blockchain. The data history can be automatically secured, and all the data are traceable and trackable. The reliability and validity of the proposed system were verified using the Jetson Nano Developer Kit. The proposed AI-based system is a low-cost embedded system. Based on the open-source cloud computing platform, the required computing resources for blockchain computing and storage are available. In an experiment, the proposed system achieved >99% accuracy within 1 s. Furthermore, the computational cost of the proposed system was 10% that of traditional AI systems. The proposed device can be rapidly connected to IoT devices that require limited manual operation and can be adopted in manufacturing and production lines.
Eland: An Efficient Lightweight Anonymous Authentication Protocol Applied to Digital Rights Management System

&lt;p&gt;A digital rights management (DRM) system provides the function of packaging raw content into an appropriate distribution form, protection for content temper-proof transmission and circumvention of unauthorized use. The digital rights contents in DRM system improve people&amp;rsquo;s spiritual quality and create huge market value. However, in the sharing of digital contents, malicious adversaries could bring security and privacy issues even cause infringement of copyright. To solve these problems, valid user identity authentication and secret key distribution to legal users is an efficient method. Nevertheless, the existing authentication schemes for DRM system have problems of security weakness or efficiency to be enhanced. Thereby, we cryptanalyze these protocols&amp;rsquo; vulnerabilities and propose a new lightweight anonymous authentication protocol called Eland that can be efficiently applied to DRM system. We compare our protocol with six DRM-related authentication protocols from the aspects of security properties, computation expense and communication cost. Comparisons results indicate that our protocol achieves a well trade-off between security and performance. Specifically, our protocol reduces 37.5% communication cost and satisfies more security requirements than Yu et al.&amp;rsquo;s scheme.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
The Metaverse as the Digital Leviathan: A Case Study of Bit.Country

As Bitcoin continued to make headlines in 2021, additional digital assets such as non-fungible tokens brought more users into the blockchain ecosystem. As more individuals and entities took a closer look at the use cases for blockchain technology, the term metaverse began to emerge across news outlets and social media platforms. With Mark Zuckerberg, the Chief Executive Officer of Facebook, announcing that the organization would become a metaverse company and change the organization's name to Meta, this announcement came with some criticism in that the Meta metaverse would be centralized. In this case study, the current state of nation-states was viewed through the lens of Hobbes' The Leviathan to assess whether decentralized metaverses will transition to a Digital Leviathan using Bit.Country - a metaverse within the Polkadot blockchain ecosystem. The case study was conducted through interviews and uncovered that the quadruple bottom line implemented in conventional business could be transferrable to a digital world built on various blockchains, non-fungible tokens, and governance in a Digital Leviathan governed by the people.
Optical tracking in team sports

Abstract Sports analysis has gained paramount importance for coaches, scouts, and fans. Recently, computer vision researchers have taken on the challenge of collecting the necessary data by proposing several methods of automatic player and ball tracking. Building on the gathered tracking data, data miners are able to perform quantitative analysis on the performance of players and teams. With this survey, our goal is to provide a basic understanding for quantitative data analysts about the process of creating the input data and the characteristics thereof. Thus, we summarize the recent methods of optical tracking by providing a comprehensive taxonomy of conventional and deep learning methods, separately. Moreover, we discuss the preprocessing steps of tracking, the most common challenges in this domain, and the application of tracking data to sports teams. Finally, we compare the methods by their cost and limitations, and conclude the work by highlighting potential future research directions.
Acceptance of E-Learning Among University Students During Covid-19 Crisis: Bangladesh Perspective

COVID-19 pandemic has dramatically transformed the global education system to a great extent. In a short period, e-learning has been adopted globally as an al-ternative teaching-learning medium. However, this sudden transition raises many concerns about e-learning acceptability. To make a clear inference, this study investigated the acceptance of e-learning (perceived usefulness, and perceived ease of use) among university-level students using the Technology Acceptance Model (TAM). The empirical analysis was performed on a sample of 694 university students in Bangladesh during the COVID-19 crisis. Findings revealed that students' overall e-learning acceptability was not adequate (32.8%), a significant number of students (46.8%) were not satisfied with e-learning experience and that majority (70.2%) of students preferred face-to-face education systems for their future study. The study also highlighted the underlying factors that negatively affect students' e-learning acceptance such as lack of technological skills, less familiarity with e-learning, lack of simplicity, low productivity, inefficiency, and so on. The overall findings of this study are intended to assist stakeholders to understand the gaps that need to be addressed immediately to increase students ’e-learning acceptability in the future.
IMPLICATIONS OF BLOCKCHAIN DEPLOYMENT IN ENERGY SUPPLY CHAIN MANAGEMENT: REPORT INTEGRITY

This study aims to investigate literature findings on the current blockchain technology requirement. The obtained findings on blockchain technology requirements and limitations, helps to draw conclusions on the adoption of blockchain technology in energy consumption reporting. Blockchain technology offers transparency of energy consumption reports. Blockchain keeps all the transactional records and guarantees security, and decentralization between the blockchain network. Blockchain technology integration for energy consumption can promote transparency and integrity of energy management reporting. Among the reasons for companies’ reluctance to report energy consumption is non-transparency, the unnecessary cost for the report, and lack of regulation to release an energy consumption report or it is voluntary in best cases. This paper reviewed 121 papers related to the topic and conclusions drawn based on findings of previous literature. Blockchain can help to alleviate the issues of transparency and cost related to energy reporting. Decision and policymakers should offer incentives and include regulations on energy reporting requirements. The reporting should be requested through a blockchain network to guarantee transparency and integrity.
An Inertial Sensing-Based Approach to Swimming Pose Recognition and Data Analysis

In this paper, inertial sensing is used to identify a swimming stance and analyze its swimming stance data. A wireless monitoring device based on a nine-axis microinertial sensor is designed for the characteristics of swimming motion, and measurement experiments are conducted for different intensities and stances of swimming motion. By comparing and analyzing the motion characteristics of various swimming stances, the basis for performing stroke identification is proposed, and the monitoring data characteristics of the experimental results match with it. The stance reconstruction technology is studied, PC-based OpenGL multithreaded data synchronization and stance following reconstruction are designed to reconstruct the joint association data of multiple nodes in a constrained set, and the reconstruction results are displayed through graphic image rendering. For the whole system, each key technology is organically integrated to design a wearable wireless sensing network-based pose resolution analysis and reconstruction recognition system. Inertial sensors inevitably suffer from drift after a long period of position trajectory tracking. The proposed fusion algorithm corrects the drift of position estimation using the measurement of the visual sensor, and the measurement of the inertial sensor complements the missing measurement of the visual sensor for the case of occlusion of the visual sensor and fast movement of the upper limb. An experimental platform for upper-limb position estimation based on the fusion of inertial and visual sensors is built to verify the effectiveness of the proposed method. Finally, the full paper is summarized, and an outlook for further research is provided.
Fuzzy-PI Conroller Tuned With GWO, WOA And TLBO For 2 DOF Robot Trajectory Control

In this study, a manipulator robot with two degrees of freedom was controlled by Fuzzy-PI adjust by three meta-heuristic algorithms (Grey wolf optimizer (GWO), Whale Optimization Algorithm (WOA) and Teaching–learning-based optimization (TLBO)). The scale factors of the fuzzy system of the takagi-soguno type (the width of the membership functions) and the parameters of PI were optimized by those three algorithms under the cost function of the absolute magnitude of the mean error (MAE). In order to investigate the robustness of the proposed controller we considered the friction forces. The results of the simulation prove the controller's effectiveness to follow a given trajectory.
HeteroFL Blockchain Approach-Based Security for Cognitive Internet of Things

Cognitive learning is progressively prospering in the field of Internet of Things (IoT). With the advancement in IoT, data generation rate has also increased, whereas issues like performance, attacks on the data, security of the data, and inadequate data resources are yet to be resolved. Recent studies are mostly focusing on the security of the data which can be handled by blockchain. Blockchain technology records the learned data into the block which is generated after completing proper consensus mechanism. In this paper, Hetero Federated Learning approach is used to apply cognitive learning on data produced by Internet of Thing devices. Security on cognitiveIoT data is provided by blockchain using Proof of Work consensus mechanism. By applying blockchain over heteroFL approach, we have conducted various simulations to check the performance of our proposed framework. Parameters taken into consideration during performance evaluation are effect of number of blocks on memory utilization and impact of data sample size on accuracy according to different learning rates.
Geostandards and Geoanalytical Research Bibliographic Review 2020

Key Points Literature review of 7200 geoanalytical publications for the year 2020. 628 selected articles with summaries of target analytes, relevant reference materials and producers. Selected publications include data obtained by new analytical developments and improved analytical protocols for established RMs, and identifies recently developed RMs for specific scientific topics.
Mitigation of Routing Congestion on Data Networks: A Quantum Game Theory Approach

Because of the sustained growth of information and mobile users transmitting a great amount of data packets, modern network performances are being seriously affected by congestion problems. In fact, congestion management is a challenging task that can be roughly summarized as a trade off between transmission latency and cost. In order to contribute to solve the congestion problem on communication networks, a novel framework based on a quantum game model is proposed, where network packets compete selfishly for their fastest route. Simulations show that final network routing and traveling times achieved with the quantum version outperform those obtained with a classical game model with the same options for packet transmission for both. Pareto optimality and Nash equilibrium are studied as well as the influence of simulated and real noise in the quantum protocol. This leads to the opportunity of developing full-stack protocols that may be capable of taking advantage of the quantum properties for optimizing communication systems. Due to its generality, this game approach can be applied both in classical complex networks and in future quantum networks in order to maximize the performance of the quantum internet.
Radial basis network estimator of oxygen content in the flue gas of debutanizer reboiler

&lt;span&gt;The energy efficiency in the debutanizer reboiler combustion can be monitored from the oxygen content of the flue gas of the reboiler. The measurement of the oxygen content can be conducted in situ using an oxygen sensor. However, soot that may appear around the sensor due to the combustion process in the debutanizer reboiler can obstruct the sensor’s function. In-situ redundancy sensors’ unavailability is a significant problem when the sensor is damaged, so measures must be made directly by workers using portable devices. On the other hand, worker safety is a primary concern when working in high-risk work areas. In this paper, we propose a software-based measurement or soft sensor to overcome the problems. The radial basis function network model makes soft sensors adapt to data updates because of their advantage as a universal approximator. The estimation of oxygen content with a soft sensor has been successfully carried out. The soft sensor generates an estimated mean square error of 0.216% with a standard deviation of 0.0242%. Stochastics gradient descent algorithm with momentum acceleration and dimension reduction using principal component analysis successfully improves the soft sensors’ performance.&lt;/span&gt;
Seismicity in the Afar Depression and Great Rift Valley, Ethiopia

Integrated mapping is essential in geological studies to assess risks of earthquake hazards. Cartographic techniques have become a commonplace approach to visualizing data in the continuous geologic and geophysical fields. However, traditional GIS mapping is a manual process with a time-consuming workflow that can lead to mistakes and misinterpretation of data. This study applied two mapping approaches to address this problem: Generic Mapping Tools (GMT) used for automated cartographic workflow employing scripts and QGIS used for traditional geologic mapping. The study area includes Ethiopia, notable for its complex geologic setting. The study aimed to analyse the relationships between the geophysical, geological, topographic and seismic setting of the country by presenting six new thematic maps:1 topography based on the GEBCO/SRTM15+ high-resolution grid;2 geological units with consistent lithology and age from the USGS database;3 geological provinces with major Amhara Plateau and Somali Province using USGS data;4 geoid based on the Earth Gravitational Model 2008 (EGM-2008) grid;5 free-air gravity anomaly model using satellite-based remote sensing data;6 seismicity showing earthquakes and volcanos from 05/03/1990 to 27/11/2020.The comparison of the topography, seismicity, geophysics and surface geology of the Afar Depression and the Great Rift Valley was based partly on extant literature on the geologic setting of Ethiopia which primarily focuses upon discussing tectonic processes that took place in the East African Rift System in the past. The current study contributes to the previous research and increases cartographic data on the geology and geophysics of Ethiopia. The outcomes can be implemented in similar regional projects in Ethiopia for geophysical and geological monitoring.
ACO-Kinematic: a hybrid first off the starting block

The use of robots in carrying out various tasks is popular in many industries. In order to carry out a task, a robot has to move from one location to another using shorter, safer and smoother route. For movement, a robot has to know its destination, its previous location, a plan on the path it should take, a method for moving to the new location and a good understanding of its environment. Ultimately, the movement of the robot depends on motion planning and control algorithm. This paper considers a novel solution to the robot navigation problem by proposing a new hybrid algorithm. The hybrid algorithm is designed by combining the ant colony optimization algorithm and kinematic equations of the robot. The planning phase in the algorithm will find a route to the next step which is collision free and the control phase will move the robot to this new step. Ant colony optimization is used to plan a step for a robot and kinematic equations to control and move the robot to a location. By planning and controlling different steps, the hybrid algorithm will enable a robot to reach its destination. The proposed algorithm will be applied to multiple point-mass robot navigation in a multiple obstacle and line segment cluttered environment. In this paper, we are considering a priori known environments with static obstacles. The proposed motion planning and control algorithm is applied to the tractor-trailer robotic system. The results show a collision and obstacle free navigation to the target. This paper also measures the performance of the proposed algorithm using path length and convergence time, comparing it to a classical motion planning and control algorithm, Lyapunov based control scheme (LbCS). The results show that the proposed algorithm performs significantly better than LbCS including the avoidance of local minima.
Flipped Classroom Effects on Grade 9 Students’ Critical Thinking Skills, Psychological Stress, and Academic Achievement

Flipped classroom is a recent trend in educational technology that tends to improve students learning.The purpose of this study is to examine the effectiveness of flipped classroom on grade 9 students' critical thinking skills, psychological stress and academic achievement in four subjects: Science, Math, IT and English.Quasi-experimental method was used in this study with pretestposttest design for quantitative data, and the thematic analysis approach for qualitative data.The respondents are 16 teachers and 385 students from 4 public male and female schools.The study was conducted during the second semester 2019.The video lessons that developed or selected from internet were presented on closed Facebook group per subject for students to view and prepare before coming to classroom session.Seven research tools were prepared: open questionnaire tool for teachers and six research tools for students were prepared and conducted twice for experimental and control groups: before and after flipped classroom implementation.The tools were critical thinking skills test, psychological stress questionnaire and academic achievement test per subject.Descriptive and inferential analyses were used.The flipped classroom implementation results in statistically significant difference at the level of significance (α <0.05) between controlled and experimental groups in favor of the latter one in critical thinking skills, Math achievement and psychological stress.
Deep Learning Segmentation in 2D X-ray Images and Non-Rigid Registration in Multi-Modality Images of Coronary Arteries

X-ray angiography is commonly used in the diagnosis and treatment of coronary artery disease with the advantage of visualization of the inside of blood vessels in real-time. However, it has several disadvantages that occur in the acquisition process, which causes inconvenience and difficulty. Here, we propose a novel segmentation and nonrigid registration method to provide useful real-time assistive images and information. A convolutional neural network is used for the segmentation of coronary arteries in 2D X-ray angiography acquired from various angles in real-time. To compensate for errors that occur during the 2D X-ray angiography acquisition process, 3D CT angiography is used to analyze the topological structure. A novel energy function-based 3D deformation and optimization is utilized to implement real-time registration. We evaluated the proposed method for 50 series from 38 patients by comparing the ground truth. The proposed segmentation method showed that Precision, Recall, and F1 score were 0.7563, 0.6922, and 0.7176 for all vessels, 0.8542, 0.6003, and 0.7035 for markers, and 0.8897, 0.6389, and 0.7386 for bifurcation points, respectively. In the nonrigid registration method, the average distance of 0.8705, 1.06, and 1. 5706 mm for all vessels, markers, and bifurcation points was achieved. The overall process execution time was 0.179 s.
Forecasting Network Interface Flow Using a Broad Learning System Based on the Sparrow Search Algorithm

In this paper, we propose a broad learning system based on the sparrow search algorithm. Firstly, in order to avoid the complicated manual parameter tuning process and obtain the best combination of hyperparameters, the sparrow search algorithm is used to optimize the shrinkage coefficient (r) and regularization coefficient (λ) in the broad learning system to improve the prediction accuracy of the model. Second, using the broad learning system to build a network interface flow forecasting model. The flow values in the time period [T-11,T] are used as the characteristic values of the traffic at the moment T+1. The hyperparameters outputted in the previous step are fed into the network to train the broad learning system network traffic prediction model. Finally, to verify the model performance, this paper trains the prediction model on two public network flow datasets and real traffic data of an enterprise cloud platform switch interface and compares the proposed model with the broad learning system, long short-term memory, and other methods. The experiments show that the prediction accuracy of this method is higher than other methods, and the moving average reaches 97%, 98%, and 99% on each dataset, respectively.