Reviewerly/exHarmony huggingface dataset:
Augmented Reality for Learning Sign Language Using Kinect Tool

The use of Augmented Reality (AR) has increased markedly as an educational tool due to the implementation of smart devices (PCs, smartphones or tablets), that complement the interaction with the real world, allowing a better perception of the environment for people with hearing impairment by the additional information it generates. This article focuses on developing a low-cost functional application that is interactive and easy to manipulate for people with hearing disabilities or people who work or socialize with them, with the aim of facilitating the process of learning sign language. The implementation was carried out using Augmented Reality along with the Kinect V2 device, due to the fidelity of the depth camera and RGB camera, which enable the design of the learning and evaluation process using Unity software. Finally, the results obtained by carrying out the experimental method and performing the application’s operational tests for prototype validation are presented, achieving a gesture recognition accuracy of 90%. In this way, a reinforcement and complement is allowed in the learning of Ecuadorian sign language in an interactive way, providing an improvement in the quality of life of people with hearing impairment.
On the Complexity of Jelly-no-Puzzle

Jelly-no-Puzzle is a combinatorial puzzle with gravity and colored blocks. It can be considered as a motion planning problem in an environment in which gravity is taken into account. The computational complexity of Jelly-no-Puzzle is studied in this paper. We show that it is NP-complete for one-color, and is NP-hard in the general case.
Synonymy Expansion Using Link Prediction Methods: A Case Study of Assamese WordNet

WordNets built for low-resource languages, such as Assamese, often use the expansion methodology. This may result in missing lexical entries and missing synonymy relations. As the Assamese WordNet is also built using the expansion method, using the Hindi WordNet, it also has missing synonymy relations. As WordNets can be visualized as a network of unique words connected by synonymy relations, link prediction in complex network analysis is an effective way of predicting missing relations in a network. Hence, to predict the missing synonyms in the Assamese WordNet, link prediction methods were used in the current work that proved effective. It is also observed that for discovering missing relations in the Assamese WordNet, simple local proximity-based methods might be more effective as compared to global and complex supervised models using network embedding. Further, it is noticed that though a set of retrieved words are not synonyms per se, they are semantically related to the target word and may be categorized as semantic cohorts.
Transcendent service management

Purpose This vision article alerts service managers to the potential of cognitive computing to reframe their value propositions. Humans are bounded in three ways: perception, rationality and physicality. Cognitive computing, hardware or software that transcends these three limits, offers many opportunities to improve the performance of service systems, in particular those focused on customer engagement. The intangibility spectrum is presented as a mental model for service managers to consider how to use cognitive computing to support augmenting their value proposition by moving across the spectrum. Design/methodology/approach Three frameworks are integrated: a five systems framework, a system's impact classification of types of cognitive computing and a tangibility spectrum. Findings Three examples illustrate the potential value of this integrative approach for service management. Originality/value This is the first integration of these frameworks, and two of them are the result of the first author's research.
Fuzzy Image Enhancement Based on Algebraic Function and Cycloid Arc Length

Fuzzy image enhancement is an important method in the process of image processing. In this paper, we present two intensifier operators in fuzzy image enhancement process based on algebraic function and cycloid arc length respectively. The first method directly uses the algebraic function as a membership intensifier operator. The second method also using a intensifier operator which established established by the cycloid arc length as the independent variable. In the last section, the test image is experimentally analyzed, and the results show that the method we proposed can improve enhance the contrast of the image.
Design and Implementation of Virtual Experiment System Platform for Electrical and Electronic Engineering in Vocational College

Abstract Under the influence of modern vocational education, virtual simulation experiment teaching is widely used as a new teaching method. The traditional electrical and electronic virtual simulation experiment teaching platform has low sensitivity and poor interaction. In this paper, a new kind of virtual simulation experiment teaching platform of electrical and electronic in Vocational College is built by lab view. Through the analysis of the experimental effect, LabVIEW software can realize the virtual simulation of electrical and electronic experiments and the establishment of virtual instruments. The integrated teaching can be easily realized through lab view. It provides a certain reference for the virtual simulation experiment teaching in vocational colleges.
Restricted Boltzmann machine based on a Fermi sea

Abstract In recent years, there has been an intensive research on how to exploit the quantum laws of nature in the machine learning. Models have been put forward which employ spins, photons, and cold atoms. In this work we study the possibility of using the lattice fermions to learn the classical data. We propose an alternative to the quantum Boltzmann machine, the so-called spin-fermion machine (SFM), in which the spins represent the degrees of freedom of the observable data (to be learned), and the fermions represent the correlations between the data. The coupling is linear in spins and quadratic in fermions. The fermions are allowed to tunnel between the lattice sites. The training of SFM can be efficiently implemented since there are closed expressions for the log-likelihood gradient. We find that SFM is more powerful than the classical restricted Boltzmann machine with the same number of physical degrees of freedom. The reason is that SFM has additional freedom due to the rotation of the Fermi sea. We show examples for several data sets.
Fully Automatic Registration Methods for Chest X-Ray Images

Image registration is important in medical applications accomplished by improving healthcare technology in recent years. Various studies have been proposed in medical applications, including clinical track of events and updating the treatment plan for radiotherapy and surgery. This study presents a fully automatic registration system for chest X-ray images to generate fusion results for difference analysis. Using the accurate alignment of the proposed system, the fusion result indicates the differences in the thoracic area during the treatment process.The proposed method consists of a data normalization method, a hybrid L-SVM model to detect lungs, ribs and clavicles for object recognition, a landmark matching algorithm, two-stage transformation approaches and a fusion method for difference analysis to highlight the differences in the thoracic area. In evaluation, a preliminary test was performed to compare three transformation models, with a full evaluation process to compare the proposed method with two existing elastic registration methods.The results show that the proposed method produces significantly better results than two benchmark methods (P-value ≤ 0.001). The proposed system achieves the lowest mean registration error distance (MRED) (8.99 mm, 23.55 pixel) and the lowest mean registration error ratio (MRER) w.r.t. the length of image diagonal (1.61%) compared to the two benchmark approaches with MRED (15.64 mm, 40.97 pixel) and (180.5 mm, 472.69 pixel) and MRER (2.81%) and (32.51%), respectively.The experimental results show that the proposed method is capable of accurately aligning the chest X-ray images acquired at different times, assisting doctors to trace individual health status, evaluate treatment effectiveness and monitor patient recovery progress for thoracic diseases.
A theory‐driven design framework for smartphone applications to support healthy and sustainable grocery shopping

Modeling behavior has been a core topic of Psychology and the Social Sciences since their respective inception as academic disciplines. This has resulted in a fractured landscape of different theories, all addressing different aspects of behavior. At the same time the need to formalize the design of computer and smartphone applications has spawned the field of User Experience (UX). With the convergence of everyday behavior and the use of mobile devices the overlap between these two fields becomes ever more important. In this paper we present a comprehensive model of behavior, integrating five well-established theories, with the aim of creating a design framework for smartphone applications that foster motivation and promote the execution of a target behavior. The operationalization of the approach is demonstrated by showing how to design and implement a prototypical application to support healthy and sustainable grocery shopping behavior. While the framework proposed is not limited to this application, it is used to exemplify the relation with previous design approaches, and the concrete implications of the model-derived framework on its implementation. Our view is that both areas of research can benefit from each other: findings from behavioral theory can inform application design, while at the same time the ubiquitous integration of mobile applications allows to dynamically apply, operationalize, and implement behavioral models into everyday life.
Einstein-Podolsky-Rosen Steering for Mixed Entangled Coherent States

By using the Born Markovian master equation, we study the relationship among the Einstein-Podolsky-Rosen (EPR) steering, Bell nonlocality, and quantum entanglement of entangled coherent states (ECSs) under decoherence. We illustrate the dynamical behavior of the three types of correlations for various optical field strength regimes. In general, we find that correlation measurements begin at their maximum and decline over time. We find that quantum steering and nonlocality behave similarly in terms of photon number during dynamics. Furthermore, we discover that ECSs with steerability can violate the Bell inequality, and that not every ECS with Bell nonlocality is steerable. In the current work, without the memory stored in the environment, some of the initial states with maximal values of quantum steering, Bell nonlocality, and entanglement can provide a delayed loss of that value during temporal evolution, which is of interest to the current study.
Learning Continuous Representation of Audio for Arbitrary Scale Super Resolution

Audio super resolution aims to predict the missing high resolution components of the low resolution audio signals. While audio in nature is a continuous signal, current approaches treat it as discrete data (i.e., input is defined on discrete time domain), and consider the super resolution over a fixed scale factor (i.e., it is required to train a new neural network to change output resolution). To obtain a continuous representation of audio and enable super resolution for arbitrary scale factor, we propose a method of implicit neural representation, coined Local Implicit representation for Super resolution of Arbitrary scale (LISA). Our method locally parameterizes a chunk of audio as a function of continuous time, and represents each chunk with the local latent codes of neighboring chunks so that the function can extrapolate the signal at any time coordinate, i.e., infinite resolution. To learn a continuous representation for audio, we design a self-supervised learning strategy to practice super resolution tasks up to the original resolution by stochastic selection. Our numerical evaluation shows that LISA outperforms the previous fixed-scale methods with a fraction of parameters, but also is capable of arbitrary scale super resolution even beyond the resolution of training data.
Sizes of Superpixels and their Effect on Interactive Segmentation

Semi-automated segmentation, also known as interactive image segmentation, is an algorithm that extracts a region of interest (ROI) from an image based on user input. The said algorithm will be fed the user input information repeatedly until the required region of interest is successfully segmented. Pre-processing steps can be used to speed up the segmentation process while improving the end result. The use of superpixels is one example of such pre-processing step. A superpixel is a group of pixels that share similar characteristics such as texture and colour. Despite the fact that it is used as a pre-processing step in many interactive segmentation algorithms, less studies had been conducted to assess the effects of the size of superpixels required by interactive segmentation algorithms to achieve an optimal result. Therefore, the purpose of this research is to address this issue in order to bridge this research gap. This study will be performed using the Maximum Similarity based region merging (MSRM) with input strokes on selected images from the Berkeleys and Grabcut image data sets, generated by superpixels extractions via energy-driven samples (SEEDS We infer from this research that an image with a minimum of 500 superpixels will aid the interactive segmentation algorithm in producing a decent segmentation result with pixel accuracy of 0.963, F-score of 0.844, and Jaccard index of 0.756. When the superpixels for an image are raised to 10,000, the segmentation results degrade. In conclusion, the size of the superpixels would have an impact on the final segmentation results.
Convolutional Neural Network based Sign Language Recognition to Assist Online Assessment

Sign languages are the visual mode of communication used by the deaf individuals. Learning sign languages at an early age is very essential for the social and intellectual developments of the deaf community. However, the lack of qualified trainers is a challenging problem that restricts the opportunity for getting formal sign language education for the deaf individuals. Hence, the need for developing an online system for learning sign language gestures through self assessment is very essential. The primary requirement for developing such a system is an efficient sign language recognition (SLR) model. This paper presents an efficient convolutional neural network (CNN) based model for automatically recognizing fingerspellings in sign languages. The model has been tested on a novel Indian sign language (ISL) fingerspelling dataset as well as a publicly available hand posture dataset, and has obtained promising results.
An Initial Study of Machine Learning Underspecification Using Feature Attribution Explainable AI Algorithms: A COVID-19 Virus Transmission Case Study

From a dataset, one can construct different machine learning (ML) models with different parameters and/or inductive biases. Although these models give similar prediction performances when tested on data that are currently available, they may not generalise equally well on unseen data. The existence of multiple equally performing models exhibits underspecification of the ML pipeline used for producing such models. In this work, we propose identifying underspecification using feature attribution algorithms developed in Explainable AI. Our hypothesis is: by studying the range of explanations produced by ML models, one can identify underspecification. We validate this by computing explanations using the Shapley additive explainer and then measuring statistical correlations between them. We experiment our approach on multiple datasets drawn from the literature, and in a COVID-19 virus transmission case study.
Political Stability in Africa: A Cross-National Analysis

The main objective of this paper is to test the influence of Africa’s founding fathers and the impact of British colonial legacy on the political stability of Africa. We relied on a sample of 50 African countries and employed cross-sectional research designs, which covered two separate periods (1960–1989 and 1990–2018). Using logistic regression and OLS estimators and controlling for French colonial legacy, economic development, regime type, ethnic heterogeneity, and ethnic polarization, we found that the founding fathers were conducive to Africa’s political stability between 1960 and 2018. We also found that British colonial legacy had some impact on former British colonies’ stability between 1960 and 2018. In addition, GDP per capita had a significant impact on Africa’s political stability over the two periods.
Auto-DL: A Platform to Generate Deep Learning Models

Deep Learning (DL) model building is a tedious and taxing process. The number of prerequisites is high and a lot of time is invested. Hence, there is a scope of Automation. Code to build DL models follows a standard structure, broadly classified into four categories (Imports, Data Input, Model Creation, and Evaluation). The work in this paper proposes to automate this core structure and build a Graphical User Interface (GUI) based tool/platform called “Auto-DL” which, on defining the task and training data, generates code in python for the specified deep learning model. The paper then discusses the platform capabilities and evaluates it and the generated code against various quantitative and qualitative parameters.
Automated Paper Evaluation System for Subjective Handwritten Answers

In order to automate the task of correcting handwritten subjective answers, we envision a system that reads such answers and grades them. In this project, we have created a mobile application to achieve this task. We have used the RAKE algorithm to extract key-phrases, which are then embedded using Word2vec model and compared using Word Mover's Distance. Our created model achieved almost human like evaluation accuracy. We have also provided accessibility features to the users with the aim of making this application a one-stop solution to automatic evaluation of handwritten subjective answers.
Environment representation and path planning for unmanned aircraft in industrial indoor applications

After their commercial success in the consumer sector, autonomous unmanned aircraft (UA) have also opened up industrial fields of application. In addition to the already established use of UA for inspection or stocktaking, the suitability of UA for transportation tasks is topic of research and pilot projects. UA are able to operate in the space above the production systems, thus extend the flow of materials by the third dimension and allow a flexible and fast transportation. Due to their three-dimensional movement capabilities, the common path-planning algorithms used for mobile robots cannot be employed but novel approaches are required to achieve a structured routing and an efficient use of the available space. The paper presents an environment model suitable for the application with UA and a multi-layered path planning approach. Thereby, the environment is divided into individual flight levels, which can be enriched with additional information to control the UA's movements.
Character Recognition from Images Using a Convolutional Neural Network

Optical character recognition (OCR) is the electronic conversion of digital images of handwritten or printed text—it could be from a scanned file, a book, a photo of a document, or a scene photo—into editable and searchable data for deeper and further processing. The aim of this work is to enable a device to automatically recognize text in images for use in other applications, such as in self- driving cars. We build a convolution neural network (CNN) model that can effectively recognize English handwritten characters and digits. The CNN is a common form of deep neural networks capable of extracting features and classifying characters with high accuracy and low dimensions. This paper proposes a CNN model using TensorFlow, which is an open-source library for machine learning. It can be used across a range of tasks, but it focuses particularly on the training and inference of deep neural networks. Our proposed CNN model trained and tested the Extended Modified National Institute (EMNIST) dataset, which contains 131,600 characters across 47 classes of letters and digits. The use of CNN leads to significant improvements across different machine learning classification algorithms. Compared with common proposed methods, our CNN gives an average accuracy of 89.46% within an acceptable computational time on local CPU of personal computer.
A Novel Reversible Data Hiding Scheme in Encrypted Images using Arnold Transform

Reversible data hiding (RDH) in encrypted images is an emerging research domain in the field of data security. Since the embedding rate from the existing RDH schemes in encrypted images is low, those schemes are not well suited for various real-life applications such as medical image transmission and cloud computing. To resolve this issue, in this manuscript we introduce an RDH scheme capable of providing high embedding without compromising the other efficiency parameters such as bit error rate and image recoverability. The proposed scheme uses a block-wise data hiding process in which a block of size B x B will be considered from the encrypted image at a time, and that will be scrambled through a sequence of Arnold transform. The bit sequence that the data hider wants to hide in a block will decide the number of Arnold transforming operations on the block. The same process will be continued for all the blocks in the image to get a final encrypted image with a hidden message. At the receiver side, the extraction of the hidden message and the image recovery is carried out with a trained support vector machine (SVM) model. The SVM model is capable to predict a given image block into any one of the two classes: encrypted block or natural block. The experimental study of the proposed scheme is carried out in the USC-SIPI image dataset and the results show that the new scheme surpasses the recent well-known RDH schemes.
Implementation of a Navigational Path Planning Algorithm for an Autonomous Mobile Robot

An Autonomous mobile robot needs to be equipped with the intelligence of autonomous navigational ability in its operating environment. The ability is mainly decided by the way the robot perceives the environment, the way it plans the navigational path to be traversed, and how it executes its motion actuation. While designing the mobile robots, the accurate perception of the environment is taken care of by intelligent sensing modules. Further, the strategy of navigational path planning is implemented by robust algorithms that ensure the shortest path traversal, hence lesser time consumption and efficient obstacle avoidance capability. The choice of a path planning algorithm depends on the needs of the application employing the mobile robot. The level of autonomy of the mobile robot depends on the way the designer exploits the advantages of the path planning algorithm. The proposed work aims at comparing the intricacies of navigational path planning algorithms, namely, Dijkstra's algorithm, Genetic algorithm and A* algorithm, that can be employed for mobile robots. The demonstrational results of navigational path planning implemented using the A* algorithm is presented