Reviewerly/exHarmony huggingface dataset:
E-Book, Audio-Book, or E-Audio-Book: The Effects of Multiple Modalities on EFL Comprehension

This study explores effects of e-book and audio-book on EFL comprehension. It was conducted at a university in Korea with 75 college students. The whole experiment lasted for five weeks in 2020. To determine effectiveness of multiple modalities, participants were assigned to three different experimental groups: e-book, audio-book, and e-audio-book (a combination of e-book and audio-book) groups. While the e-book group was provided with passages in a text format, the audio group received the same passages in an audio format. The e-audio-book group was given both text and audio formats. At the beginning and end of the treatment, pretest and posttest were administered. As expected, the e-audio-book group benefited more than the other two groups in listening comprehension. However, they showed better performance in reading comprehension than the audio-book group only. Additionally, the e-book group performed better in reading comprehension compared to the audio-book group. Based on these findings, pedagogical implications are made.
Optimal Face Recognition System using Haar Classifier

Human beings when born are adorned with a presence that helps them to differentiate among other individuals around ie. “Face”. Alongside speech and fingerprints, A face is significantly used in biometrics as no two individuals have the same face. Myths though say that in this world you may resemble with some facial characteristics to 7 different individuals then these facial detection and recognition algorithms help us to identify between them. Facial expressions detection, face detection, and recognition have been impactful in the research domain especially in fields of image processing. Although it might sound like a cakewalk though undoubtedly it has proven to be a complex task for a computer to detect and pose all the features of the image passed since various other variables impair the accuracy/confidence of the algorithms mainly illumination variation, the background noise of the image, low resolution, occlusion, blurriness of the image, pixel scale of the image and many more components.Considering these myths, this research is based upon face recognition algorithms and a model to identify if there is a face in an image passed, identify the number of faces, and justify with confidence value obtained which algorithm should be suggested, is most optimum for face detection and recognition among (Haar classifiers, default face recognizer, Local Binary Pattern Histogram (LBPH) face recognizers, {Adaboost algorithm}, Eigenface methods, etc.). This research particularly focuses on the use of Haar cascades and the LBPH Face recognizer algorithm as a hybrid combination for the smart voting system as an authentication approach.
An Obstacle Avoidance Method Based on Advanced Rapidly-exploring Random Tree for Autonomous Navigation

Obstacle avoidance is an important issue in mobile robot localization and navigation. This paper presents a method for mobile robots to autonomously avoid dynamic obstacles and reach the target point, which can be used in Automated Guided Vehicles (AGV), driverless trucks, or other forms of mobile robots for transportation, delivery, and unmanned tasks. This method detects and tracks obstacles by LIDAR, models the obstacle information using a probability grid map. Then, use the advanced Rapidly-exploring Random Tree (RRT) algorithm for local path planning to update the current path to achieve obstacle avoidance based on the obstacle map. The proposed method was implemented in Robot Operating System (ROS) and tested in several real-world environments. This method was able to solve the problem of avoiding dynamic obstacles in real-time during the process of robot autonomous navigation. The average processing time per iteration is 30 ms and 95 ms in the 15m×15m and 50m×50m areas. And the final traveling path is 5% shorter compared with the A* planner and Dynamic Window Approach (DWA) planner used in the Navigation package of ROS.
Automatic lexical collocate extraction for corpus-based ontology building and refinement

Abstract Traditional corpus-based methods rely on manual inspection and extraction of lexical collocates in the study of selection preferences, which is a very costly, labor-intensive, and time-consuming task. Devising automatic methods for lexical collocate extraction becomes necessary to handle this task and the immensity of corpora available. With a view to leveraging the Sketch Engine platform and in-built corpora, we propose a working prototype of a Lexical Collocate Extractor (LeCoExt) command-line tool that mines lexical collocates from all types of verbs according to their syntactic constituents and Collocate Frequency Score (CFS). This might be the first tool that performs comprehensive corpus-based studies of the selection preferences of individual or groups of verbs exploiting the capabilities offered by Sketch Engine . This tool might facilitate the task of extracting rich lexico-semantic knowledge from diverse corpora in a few seconds and at a click away. We test its performance for ontology building and refinement departing from a previous detailed analysis of stealing verbs carried out by Fernández-Martínez &amp; Faber (2020) . We show how the proposed tool is used to extract conceptual-cognitive knowledge from the THEFT scenario and implement it into FunGramKB Core Ontology through the creation and modification of theft-related conceptual units.
Faces of the polytope of doubly substochastic matrices

We study the faces of the convex polytope of all n×n doubly substochastic matrices, denoted by ωn. We give the necessary and sufficient conditions of a face being nonempty. We also describe all 1-dimensional faces, 2-dimensional faces, and facets of ωn. Moreover, we explore the relation between the faces of ωn and the faces of Ωn, the convex polytope of all n×n doubly stochastic matrices.
Metrology-aware Path Planning for Agricultural Mobile Robots in Dynamic Environments

Path planning for long-term tasks is currently an important research area for robotic applications in uncertain environments, such as farms. Here, a robot automatically follows an optimal path generated by the robot planner, and the robot planner must consider the uncertainties in the environment to adapt and improve its motion plan during run-time. In some cases, the robot must change its motion direction multiple times in a long-term task. Therefore, the robot has to decide which place to visit next to adapt to unexpected events, such as avoiding obstacles not recognized by the robot or some robot features and environmental conditions. The robot will need to either plan a short path and then back to the global path or, if better for its overall performance, create a new global path to the target and automatically follow the new path. This study proposes a path planning method to generate the unmanned ground vehicle path to visit all predefined locations on a farm. The proposed method is an extended version of our previously developed method, called the Boundary Node Method and Path Enhancement Method. Then, this method is combined with the online path optimization scheme for re-planning and scheduling the new path at run-time to improve the robot’s path and adapt to unexpected events. Different simulation environments are examined to demonstrate the performance of the proposed method. The results show that the robot can re-plan an optimal path toward the goal points to avoid unexpected threats and uncertain obstacles.
Online Information-Aware Motion Planning with Inertial Parameter Learning for Robotic Free-Flyers

Space free-flyers like the Astrobee robots currently operating aboard the International Space Station must operate with inherent system uncertainties. Parametric uncertainties like mass and moment of inertia are especially important to quantify in these safety-critical space systems and can change in scenarios such as on-orbit cargo movement, where unknown grappled payloads significantly change the system dynamics. Cautiously learning these uncertainties en route can potentially avoid time- and fuel-consuming pure system identification maneuvers. Recognizing this, this work proposes RATTLE, an online information-aware motion planning algorithm that explicitly weights parametric model-learning coupled with real-time replanning capability that can take advantage of improved system models. The method consists of a two-tiered (global and local) planner, a low-level model predictive controller, and an online parameter estimator that produces estimates of the robot’s inertial properties for more informed control and replanning on-the-fly; all levels of the planning and control feature online update-able models. Simulation results of RAT-TLE for the Astrobee free-flyer grappling an uncertain payload are presented alongside results of a hardware demonstration showcasing the ability to explicitly encourage model parametric learning while achieving otherwise useful motion.
Ground robot navigation with Deep Reinforcement Learning in immersive environment

The article presents studies of deep reinforcement learning method for the autonomous positioning problem of a small robot in a simulation environment. In our experiments, the open source game engine Unreal Engine is used to simulate a physically adequate 3D scene with obstacles. Images obtained by a virtual robot camera in the simulation environment are entered into a neural network to determine the required direction of the target and obstacles localization in 3D environment and then analyze the training of a real robot with reinforcement. In this study, we investigate the agent’s ability to learn free movement without interacting and colliding with other static or moving objects on the scene.
Privacy-Preserving Periodic Frequent Pattern Model in AIoT Applications

We begin with a sanitization strategy for concealing sensitive periodic frequent patterns in this study. The developed method employs the Term Frequency and Inverse Document Frequency (TF-IDF) to determine which transactions and objects should be sanitized based on user-defined sensitive periodic frequent patterns. Using the designed approach, it is possible to correctly and properly choose victim items in the transactional database for data sanitization.
Automatic Categorization of News Articles and Headlines Using Multi-layer Perceptron

News categorization is the task of automatically assigning the news articles or headlines to a particular class. The proliferation of social media and various web 2.0 platforms usage has resulted in substantial textual online content. The majority of this textual data is unstructured, which is extremely hard and time-consuming to organize, manipulate, and manage. Due to the fast and cost-effective nature, automatic news classification has attained increased attention from news agencies in recent years. This paper introduces a deep learning-based framework using multilayer perceptron (MLP) to classify Bengali news articles and headlines into multiple categories:accident, crime, entertainment, and sports. Due to the unavailability of the Bengali news corpus, this work also developed a dataset containing 76343 news articles and 76343 headlines. Additionally, this work investigates the performance of the proposed classifier using five-word embedding techniques. The comparative analysis reveals that MLP with Keras embedding layer outperformed the other embedding models achieving the highest accuracy of 98.18% (news articles) and 94.53% (news headlines).
An Improved Quantum Solution for the Stereo Matching Problem

As the most computationally intensive part of a stereo vision system, stereo matching has been the focus of intense research activities for the last four decades. We present the first attempts to compare a quantum stereo matching solution with state-of-the-art approaches on the Middlebury stereo datasets. We first looked at quantum annealing computation as a way to interact with the D-Wave quantum computer and then improved the quantum solution to the stereo matching problem found in the literature. Using a line-by-line approach, we traded accuracy off for the qubits availability in the QPU (Quantum Processor Unit). Our findings show that it is possible to obtain results from real-sized images despite the scarcity of physical qubits in the quantum hardware. While the current quantum solution solves a class P stereo matching problem, its real advantage over classical stereo matching algorithms will arise with NP-hard problems which will be the focus of our future research.
Study on the Algorithms of Principal Component Analysis and Related Techniques

With the rapid development of the computer technology, new algorithms are put forward to deal with the large datasets. As a result, the algorithm of principal component analysis becomes one of the most popular algorithms used for this purpose. The algorithm is well-known applied for the dimensionality reduction at the least cost of information loss. This paper starts with the introduction of basic theoretical algorithm of principal component analysis, and then the use and interpretation the algorithms for principal component analysis is elaborated in the applied research of financial performance evaluation. Results confirm the algorithm of principal component analysis is important instrument to obtain the accurate evaluation and prediction.
Reactive Multi-Fitness Learning for Robust Multiagent Teaming

Multi-robot systems deployed in remote missions for longitudinal tasks (tasks that occur over extended periods of time) are not only required to perform their initial tasks, but also need to react to changes in environmental conditions. In addition, performance in longitudinal missions is often difficult to capture in a single reward function. This work introduces Reactive Multi-Fitness Learning (R-MFL) to address both problems by separating “behaviors” and mission goals. R-MFL builds on Multi-Fitness Learning (MFL) that addresses which fitness matters when in complex and sequential tasks. R-MFL enables agents to change their set of behaviors instead of incorporating new insights into a single policy, while also determining when new behaviors are necessary. We show that the agents using R-MFL are able to react to unforeseen changes without catastrophic forgetting, and identify when and where they need to react during a deployment. R-MFL is shown to provide up to 90% improvement over MFL and 100% over a one-step evolutionary approach.
Application of the Complex Moments for Selection of an Optimal Sensor

In the first time we apply the statistics of the complex moments for selection of an optimal pressure sensor (from the available set of sensors) based on their statistical/correlation characteristics. The complex moments contain additional source of information and, therefore, they can realize the comparison of random sequences registered for almost identical devices or gadgets. The proposed general algorithm allows to calculate 12 key correlation parameters in the significance space. These correlation parameters allow to realize the desired comparison. New algorithm is rather general and can be applied for a set of other data if they are presented in the form of rectangle matrices. Each matrix contains N data points and M columns that are connected with repetitious cycle of measurements. In addition, we want to underline that the value of correlations evaluated with the help of Pearson correlation coefficient (PCC) has a relative character. One can introduce also external correlations based on the statistics of the fractional/complex moments that form a complete picture of correlations. To the PCC value of internal correlations one can add at least 7 additional external correlators evaluated in the space of fractional and complex moments in order to realize the justified choice. We do suppose that the proposed algorithm (containing an additional source of information in the complex space) can find a wide application in treatment of different data, where it is necessary to select the "best sensors/chips" based on their measured data, presented usually in the form of random rectangle matrices.
Recognition of Eye-Written Characters with Limited Number of Training Data Based on a Siamese Network

Developing a hum–computer interface (HCI) is essential, especially for those that have spinal cord injuries or paralysis, because of the difficulties associated with the application of conventional devices and systems. Eye-writing is an HCI that uses eye movements for writing characters such that the gaze movements form letters. In addition, it is a promising HCI because it can be utilized even when voices and hands are inaccessible. However, eye-writing HCI has low accuracy and encounters difficulties in obtaining data. This study proposes a method for recognizing eye-written characters accurately and with limited data. The proposed method is constructed using a Siamese network, an attention mechanism, and an ensemble algorithm. In the experiment, the proposed method successfully classified the eye-written characters (Arabic numbers) with high accuracy (92.78%) when the ratio of training to test data was 2:1. In addition, the method was tested as the ratio changed, and 80.80% accuracy was achieved when the number of training data was solely one-tenth of the test data.
Visualizing Feature-based Similarity for Research Paper Recommendation

Research paper recommender systems are widely used by academics to discover and explore the most relevant publications on a topic. While existing recommendation interfaces present researchers with a ranked list of publications based on a global relevance score, they fail to visualize the full range of non-textual features uniquely present in academic publications: citations, figures, charts, or images, and mathematical formulae or expressions. Especially for STEM literature, examining such non-textual features efficiently can provide utility to researchers interested in answering specialized research questions or information needs. If research paper search and recommender systems are to consider the similarity of such features as one facet of a content-based similarity assessment for academic literature, new methods for visualizing these non-textual features are needed. In this paper, we review the state-of-the-art in visualizing feature-based similarity in documents. We subsequently propose a set of user-customizable visualization approaches tailored to STEM literature and the research paper recommendation context. Results from a study with 10 expert users show that the interactive visualization interface we propose for the exploration of non-textual features in publications can effectively address specialized information retrieval tasks, which cannot be addressed by existing research paper search or recommendation interfaces.
On a two-stage progressive clustering algorithm with graph-augmented density peak clustering

Due to the rapidly growing volume and velocity of big data, real-time streaming data analysis has become increasingly important in many applications. To discover knowledge from such data, a wide range of machine learning techniques have been proposed and used in practice. Among them, clustering, which aims at grouping objects into different classes on the basis of their similarity, is the most common form of unsupervised learning. However, most existing clustering algorithms are designed for static data, and hence are not best suited for streaming data. In this paper, we propose PC-DPC, a two-stage progressive clustering algorithm with graph-augmented density peak clustering. PC-DPC first identifies clusters of streaming data using an improved density peak clustering algorithm, and then merges newly arriving data into the existing data pool by measuring inter-cluster structural similarity, which considers the distance between a center and representative points. We illustrate the superiority of PC-DPC over several state-of-the-art clustering algorithms in terms of clustering accuracy and running time on publicly available benchmark datasets.
The Lehmann-Rosenblatt Test Applied for Determination of Statistical Parameters of Charles Dickens's Authorial Style

The multifactor method has been applied for author identification. The method involves determination of the topic factor effect and authorial style factor effect. The former shows the difference in topic of the two compared samples, the latter &#x005F; the difference in authorial style features. The texts are differentiated by word length distribution. The novelty is the use of the Lehmann-Rosenblatt test to determine the topic factor effect in each sample from one and the same piece of writing by Charles Dickens. Unessential differences of the topic factor effect have been revealed in each sample within the same piece of writing for the literary works &#x00AB;A Christmas Carol&#x00BB; and &#x00AB;A Tale of Two Cities&#x00BB;. The samples are homogeneous. The obtained results confirm the thematic homogeneity of Dickens authorial style within the literary works. The determined characteristics of Dickens literary style established by the Lehmann-Rosenblatt test make it possible to state that the test is efficient for identifying the authorial style features, particularly for determining the topic factor effect and authorial style factor effect. In further research the test will be used for differentiation of texts by different authors.
Representation of Quantum Signal Simulating

The Newton’s laws had been developed on basis experiments with nature signals, and by hand computation for processing of these signals. The signal carries the code of data. The carrier is a wave - the process of transfer in space and time (physical space) of the code. Then the parameters of this transfer determines the state of the signals in time and space. The design of quantum communication systems, information technology, etc. modeling of quantum signals is a complex problem of representation of the carrier, the signal code, and their quantum states. Quantum computing, and explaining on resolving of application tasks (RSA, FFT algorithm, quantum circuits, etc.) in physical the scales theories and practices (in mechanics and quantum mechanics) are different in technics, algorithmic, and the simulation. All of these significantly does complexity of the design of quantum communication systems. This paper discusses the representation of a quantum signal as its general relationship with the difference of digitized sinusoidal functions with different digitization parameters obtained by a classical computer. This difference is considered similar to squeezed states with different sets of digitization parameters. The peculiarities of the obtained difference signatures with respect to similar quantum phenomena are classified. The representation of modeling of a quantum signal by possibilities of the classical computer as it was developed for quantum states, schemes, protocols is offered.