Reviewerly/exHarmony huggingface dataset:
The discrepancy evaluation model in the implementation of online learning (on the basis of parents’ perceptions)

Introduction . The evaluation of online learning is an attempt to see the extent of the education process in Indonesia. This evaluation study seen from parents’ perception is a new study that has not been carried out. Most dominant researchers evaluate the learning process by emphasising the school, but it has not been touched from the parent’s side. Aim . This study aims to evaluate the implementation of online learning based on the perceptions of parents of students. Research methodology and methods . A discrepancy evaluation model method is used in the present research. The discrepancy evaluation model embodies five stages of evaluation: reviewing designs and standards, comparing implementation strategies, reviewing whether the process produces goals, comparing discrepancies with objectives, and cost and benefit analysis. The research subjects were 231 parents, who were involved using a simple random sampling technique. Data were collected using online questionnaires and interview sheets. A table of evaluation criteria and descriptive analysis were employed to analyse the data. Results and scientific novelty . The study results show that the implementation of online learning can run well, but the government needs to prepare appropriate policies to maintain the quality of learning. On average, 72.82% of parents think that the online learning process is sufficient. There are still psychological obstacles in the form of fatigue, anxiety, too many tasks, and technical obstacles in the form of a slow Internet network and the lack of availability of online learning facilities such as cellphones, laptops, personal computers, which are input and follow-up devices for schools, so that online learning is expected to continue to be improved increasing its effectiveness. Practical significance . This research implies that evaluation is one of the indicators that can measure the success of a programme. To implement the online learning programme, namely in junior high schools in Indonesia, it is necessary to have a special education policy by paying attention to the segmentation of the characteristics of parents, especially groups of low-income parents and education level.
Autonomous navigation for Mars probes using only satellite-to-satellite tracking measurements by singularity-avoiding orbit elements

Abstract This paper proposes a novel autonomous navigation method for Mars-orbiting probes. Satellite-to-satellite tracking (SST) between two probes is generally deemed to involve autonomous measurements with no dependence on any external observation sites on the Earth. For the conventional two-body dynamic model, it is well known that the orbit states cannot be estimated by merely using such SST measurements. Considering the effects of third-body gravitation perturbation and the weak Mars tesseral harmonics perturbation, autonomous navigation with SST measurements alone becomes weakly observable and may be achieved by some nonlinear filtering techniques. Two significant improvements are made to mitigate the nonlinearity brought by the dynamic models. First, singularity-avoiding orbit elements are selected to represent the dynamic models in order to reduce the intensity of the nonlinearity which cannot be overcome by the traditional position–velocity state expression. Second, the unscented Kalman filter method is effectively utilised to avoid the linearised errors calculated by its extended Kalman filter counterpart which may exceed the tesseral harmonics perturbation. A constellation, consisting of one low-orbit probe and one high-orbit probe, is designed to realise the autonomous orbit determination of both participating Mars probes. A reliable navigation solution is successfully obtained by Monte Carlo simulation runs. It shows that the errors of the semimajor axes of the two Mars probes are less than 10 m and the position errors are less than 1 km.
Implementation of Self-Controlled Wheelchairs based on Joystick, Gesture Motion and Voice Recognition

A self-controlled automatic wheelchair extremely important for physically challenged people. In this paper, we have presented, the design and implementation details of three schemes for a self-controlled wheelchair and discussed their comparative analysis. These designs include: a) Joystick Controlled Wheelchair b) Gesture Controlled Wheelchair and c) Voice Controlled Wheelchair. The first scheme presents a prototype of movement of the wheelchair using a joystick. The second scheme uses a pseudo glove carrying an accelerometer as an input to the wheelchair. The last scheme uses a HC-05 Bluetooth Module and Bluetooth Controller mobile application for the working of the wheelchair. All these schemes are implemented using an Arduino UNO microcontroller board. Arduino Integrated Development Environment (IDE) is used for developing the necessary software. The UPPAAL software is used for verification of the design. Various test cases are applied to the prototype models under varying environmental conditions. Based on these test results, a combination of voice controlled as well as gesture controlled wheelchair is proposed and designed for obtaining higher efficiency. Besides wheelchairs, these prototype designs can be extended for several other application areas.
A class-driven approach to dimension embedding

The preservation of the inbuilt structures of data sets and the more decomposition of the classes are a significant interest in dimension embedding. In this respect, the dimensionality reduction methods use novel techniques to better ascertain the fundamental structure of the manifold on which the data lies. However, both conventional and state-of-art supervised dimensionality reduction methods cannot benefit from class information good enough. Therefore, their generalization performances on the test data are weak. A new non-linear supervised algorithm, which we call Class-driven Dimension Embedding (CDE), is proposed for utilizing class information. CDE performs three outstanding characteristics: (i) preserving the intrinsic relationship between the data points and classes; (ii) producing wide margins between classes; (iii) enhancing the generalization performance on the test data. The proposed method embeds a d-dimensional data set into the c-dimensional space (c designates the number of classes) through the corresponding values to classes of each point by exploiting a neighborhood graph and a feature weighting function. The experimental results on forty-eight data sets demonstrate that CDE is comparable to or better than twenty-four dimensionality reduction algorithms in terms of classification accuracy and visualization. The source code of CDE can be found in https://doi.org/10.24433/CO.0967299.v1 for computational reproducibility.
Goal-Oriented Obstacle Avoidance by Two-Wheeled Self Balancing Robot

Gurnani, Rajat Rastogi, Shreya Chitkara, Simrat Singh Kumari, Surbhi Gagneja, AbhishekA solution to an age-old problem of keeping the inverted pendulum-based robot upright and its collision-free navigation through an unknown environment is presented in this paper. Two deep reinforcement learning models run simultaneously on a two-wheeled self-balancing robot. The robot acts as the agent in the learning environment created in Unity3D, a real-time development platform. Proximal Policy Optimization algorithm, an on-policy gradient method for reinforcement learning, has been used in both models. The navigation model feeds the intermediate position of the robot to the balancing model, which then outputs a set of actions to the robot. In addition, the navigation model uses curiosity-driven learning thus expanding the set of solvable tasks. The algorithm consequently generates an intrinsic reward function, which makes the robot generalize better with unexplored environments. Furthermore, the resultant intelligent controller is made to navigate both dynamic and static obstacle-filled courses. The simulations were run using Unity and the Nvidia PhysX engine.
The Effect of Augmented Reality Applications in Biology Lesson on Academic Achievement and Motivation

In this study, it was aimed to determine the effect of activities based on augmented reality applications in 9th grade biology lesson on students' achievement and motivation towards biology lesson and to get students' opinions about the applications. In the study, mixed method was used. The study group of the research consisted of students of two different classes at the 9th grade level of a private high school located in the city center of Karaman in the 2018-2019 academic year. When the research findings were examined, it was seen that the academic achievement post-test mean scores of the experimental group students were found to be significantly higher than the mean scores of the control group students. According to another finding of the study, there was no significant difference between the experimental group and the control group students in terms of the total mean scores obtained from the motivation questionnaire for biology lesson. However, a significant difference was found in favor of the experimental group in terms of "anxiety in exams" dimension. As a result of the research, the students stated that they liked the biology lessons in which augmented reality applications were carried out, that augmented reality applications made abstract concepts concrete and made them more permanent, and that the lessons conducted in this way were fun.
A Multi-sensor Gesture Interaction System for Human-robot Cooperation

Gestures are considered as a natural expression of the human body and are used to communicate with other people. The gesture-based human-robot interaction is natural, convenient, and applicable, and can be applied to complex interactive scenarios. In this paper, considering the real-time nature of the human-robot cooperation(HRC) system and the variability of the interaction range, we combine Kinect V2.0 (far-range sensor) and Leap Motion (short-range and high precision sensor), and propose a real-time multi-sensor gesture interaction system. Firstly, a reasonable layout of two sensors is discussed to realize far-range perception of natural gesture interaction. Then, nine gestures are defined that are easy for users to remember and operate. At the same time, a gesture interactive mechanism is proposed that can automatically switch two sensors according to the distance of the operator&#x2019;s position. It can better improve the defects such as occlusion and confusion in the process of gesture controlling, and solve the minimum distance constraint of Kinect. Finally, the interactive experiment proves the stability and accuracy of the system.
Currency Detection for Visually Challenged People using Convolutional Neural Network Model

Often, the visually impaired face a lot of issues in recognizing bank notes (bills). A system is being proposed by us, that facilitates the visually impaired people to recognize the different denominations of Indian Currencies with the basic concept being the usage of Deep Learning Algorithm. A convolutional neural network model has been built by us in which, a high volume of images of different denominations have been considered. The specimen images have been captured in different positions and physical conditions. We then plan on extending this work into a Mobile Application Framework which can be used in Android Mobiles.
Weakly Supervised Deep Soft Clustering for Flood Identification in SAR Images

As flood occurs unpredictably, there is not enough time to label the data in practice. The use of clustering inside flood detection deep networks can reduce their demand for labeled data. However, existing clustering algorithms aim at assigning a unique cluster for each pixel. This leads to the fact that clustering process is non-differentiable to the inputs, hindering their incorporation into deep networks. In this study, we introduce a new assignment strategy for single-polarization SAR images to make the clustering differentiable, named &#x201C;soft association.&#x201D; Here, each pixel is assigned to various clusters with different probabilities. The greater the probability value, the more likely the pixel will be finally assigned to the cluster. Based on this, an end-to-end trainable semi-supervised clustering network for SAR flood detection is established. Compared with the existing state-of-the-art semi-supervised methods, it can achieve similar performance with fewer labeled samples.
Underwater image enhancement by using color correction and contrast techniques

An image in the underwater ocean depth of 10 meters by capturing a low-resolution camera. An image is characterized by low contrast and blurriness. Despite water medium has considering the propagation of light, it degrades the underwater image due to refraction, scatters, and color absorption. This underwater image needs to improve its color contrast and quality image by using Simple Histogram Equalization for color correction and DSNMF (Deep Sparse Non-Negative Matrix Factorization) for color contrast improvement. Finally, the proposed method results describe based on the observations of the qualitative and quantitative parameters of PSNR (Peak Signal to Noise Ratio), RMSE (Root Mean Square Error), and SSIM (System Similarity Index Matrix). The output of This proposed method is shown a qualitative state-of-the-art underwater enhanced image with better brightness and contrast and developed technique simulation produces a quantitative output of enhancing the image of PSNR, RMSE, and SSIM are 25.256, 13.235, and 8.232, with these parameters increasing better quality visual perception.
Evaluating Usability of Mobile Augmented Reality System for Enhancing the Learning Experience

The idea of Usability is difficult to describe since the elements affecting it varies from user to user, and the mutual relationships between the components are complex. However, before any modeling or testing can take place, some conceptual analysis is required. Augmented Reality has gained so much popularity in the past decade and established itself as an emerging research field. The current research and development of AR-based learning systems, especially for education at all levels, is required to comply with the practical usability test. Further, these research studies aimed at statistical generalizations and modeling the learning system, and Usability is the critical parameter in doing so. This paper aims to evaluate the Usability of mobile augmented reality (MAR) systems using a system usability scale (SUS). Teachers from engineering backgrounds were participated and experience the system. The findings of the study indicated that the usability score of MAR is 85.95% which makes it suitable for further deployment.
Irissometry: Effects of Pupil Size on Iris Elasticity Measured With Video-Based Feature Tracking

It is unclear how the iris deforms during changes in pupil size. Here, we report an application of a multi-feature iris tracking method, which we call irissometry, to investigate how the iris deforms and affects the eye position signal as a function of pupil size.To evoke pupillary responses, we repeatedly presented visual and auditory stimuli to healthy participants while we additionally recorded their right eye with a macro lens-equipped camera. We tracked changes in iris surface structure between the pupil and sclera border (limbus) by calculating local densities (distance between feature points) across evenly spaced annular iris regions.The time analysis of densities showed that the inner regions of the iris stretched more strongly as compared with the outer regions of the iris during pupil constrictions. The pattern of iris densities across eccentricities and pupil size showed highly similar patterns across participants, highlighting the robustness of this elastic property. Importantly, iris-based eye position detection led to more stable signals than pupil-based detection.The iris regions near the pupil appear to be more elastic than the outer regions near the sclera. This elastic property explains the instability of the pupil border and the related position errors induced by eye movement and pupil size in pupil-based eye-tracking. Tracking features in the iris produce more robust eye position signals. We expect that irissometry may pave the way to novel eye trackers and diagnostic tools in ophthalmology.
Corporate censorship online: Vagueness and discursive imprecision in YouTube’s advertiser-friendly content guidelines

This article deals with YouTube’s advertiser-friendly content guidelines – the content rules dedicated to defining what YouTube deems advertiser (un)friendly and that YouTube creators seeking to monetise their content through advertising have to follow. Specifically, this study addresses the textual composition of YouTube’s regulations with a focus on occurrences of vagueness. Regarding methods of data analysis, I take a corpus-assisted discourse analytical approach to YouTube’s texts on advertiser-friendliness. That is, I take a wide-angle view on all concordance lines of ‘content’ and identify occurrences of different forms of vagueness. Findings suggest that at least 26% of the lines of ‘content’ detailing YouTube’s ad-friendly content guidelines exhibit at least one of eight forms of vagueness. Consequently, content creators – left unsure about the monetisability of particular content – may choose not to push any boundaries but to produce noncontroversial content, which, in turn, may impede content plurality on YouTube.
Differential Privacy in Privacy-Preserving Big Data and Learning: Challenge and Opportunity

Differential privacy (DP) has become the de facto standard of privacy preservation due to its strong protection and sound mathematical foundation, which is widely adopted in different applications such as big data analysis, graph data process, machine learning, deep learning, and federated learning. Although DP has become an active and influential area, it is not the best remedy for all privacy problems in different scenarios. Moreover, there are also some misunderstanding, misuse, and great challenges of DP in specific applications. In this paper, we point out a series of limits and open challenges of corresponding research areas. Besides, we offer potentially new insights and avenues on combining differential privacy with other effective dimension reduction techniques and secure multiparty computing to clearly define various privacy models.
You should know more: Learning external knowledge for visual dialog

Visual dialog is a task that two agents complete a multi-round conversation based on an image, a caption, and dialog histories. Despite the recent progress, existing methods still undergo degradation on the condition of complex scenarios. Handling these scenarios depends on logical reasoning that requires commonsense priors. In this paper, we propose a novel visual dialog pipeline named Structured Knowledge-Aware Network (SKANet), consisting of an Image Knowledge-Aware Module and a Caption Knowledge-Aware Module. Specifically, the Image and Caption Knowledge-Aware Modules construct commonsense knowledge graphs from ConceptNet. We apply SKANet to two sub-tasks: the conventional visual dialog and a goal-oriented visual dialog named ‘image guessing’. For the conventional visual dialog, the SKANet is combined with an additional Multi-Modality Fusion Module, which is designed to explore the visual content and the textual context about the dialog history. For the goal-oriented visual dialog, we directly apply the Image and Caption Knowledge-Aware Modules to two agents, respectively. Experimental results on VisDial v0.9 and VisDial v1.0 datasets show that our proposed method effectively outperforms comparative methods on both sub-tasks.
Isolated Handwritten Pashto Characters Recognition using KNN classifier

Regional and cultural diversities around the world and especially in Pakistan has given birth to a large number of writing systems and scripts consists of varying character sets. Developing an optimal OCR system for such a varying and large character set is a challenging task. Unlimited variations in handwritten texts due to mood swings, varying writing styles, changes in medium of writing, etc. puzzles the research community. Slight change in character shapes for various scripts acts as a big barrier in developing the character recognition (CR) systems for cursive scripts. Unavailability of benchmark results and corpora for cursive scripts CR impedes the researchers in the development of an optimal CR systems. To efficiently address these issues, the proposed research work aims to develop an optimum OCR system for the recognition of handwritten Pashto characters. Also the unavailability of a standard corpora of the handwritten Pashto characters is addressed by developing a medium sized corpus of the handwritten Pashto characters (14784 handwritten samples). K nearest neighbor is adapted for the recognition of the Pashto characters based on the zoning technique. After testing the proposed OCR system for varying training and test sets an overall accuracy of 85.31&#x0025; is calculated.
Comparison of Case-Based Reasoning and Certainty Factor Methods for Dengue Diagnosis

Dengue Hemorrhagic Fever (DHF) is a disease caused by a dengue virus infection that is transmitted through the bite of Aedes Aegypti and Aedes Albopictus mosquitoes. People with dengue fever who are diagnosed based on symptoms that appear without any medical considerations can lead to mishandling. Therefore, a computer-based system is needed to overcome these problems. This study aims to compare two methods: Case-Based Reasoning (CBR) and Certainty Factor (CF) for diagnosing dengue. The CBR method computed the similarity value of the new case disease with the previous case to justify the disease. Meanwhile, the CF method used the certainty value of the disease. The dataset consists of 110 patient data divided into three categories of dengue fever, namely, dengue fever, dengue hemorrhagic fever, and dengue shock syndrome. There are 19 symptoms of the patient recorded. The expert system presents the result of DHF diagnosis, the appropriate treatment solutions, and the comparison results of the diagnosis from the CBR and CF methods. Based on the evaluation result, it shows that the CBR method achieves the value of accuracy, precision, and recall is 84.455%, 84.455%, and 100%, while the CF method obtain 80.909%, 80.909%, and 100%, respectively. Based on the test results, it shows that there are differences in values, so that the conclusion is that the CBR method is more accurate in diagnosing DHF.
WETA: Automatic taxonomy alignment via word embeddings

Lexical taxonomies are widely used to foster information retrieval and exchange in several domains and applications. When there are multiple taxonomies, heterogeneity among them is a severe problem for efficient collaboration processes. In this paper, we propose WETA, a domain-independent, knowledge-poor method for automatic taxonomy alignment via word embeddings. WETA associates all the leaf terms of the origin taxonomy to one or many concepts in the destination taxonomy, employing a scoring function, which merges the score of a hierarchical method based on cosine similarity and the score of a classification task. WETA is developed in the context of an EU Grant aiming at bridging the national taxonomies of EU countries towards the European Skills, Competences, Qualifications and Occupations taxonomy (ESCO) using AI Algorithms. The results, validated within the EU project activities for bridging the Italian occupation taxonomy CP and ESCO, confirm the usefulness of WETA in supporting the automatic alignment of national labor taxonomies. WETA reaches a 0.8 accuracy on recommending top-5 occupations and a wMRR of 0.72. WETA reduces the human effort needed for building a mapping from scratch: it would allow domain experts to concentrate on the validation task and decrease the incoherence due to multiple judgments. It would also make the approach reproducible and transparent to policymakers.