Reviewerly/exHarmony huggingface dataset:
Homogeneous and Non-homogeneous Image Dehazing Using Deep Neural Network

Haze poses challenges in many vision-related applications. Thus, dehazing an image becomes popular among vision researchers. Available methods use various priors, deep learning models, or a combination of both to get plausible dehazing solutions. This paper reviews some recent advancements and their results on both homogeneous and non-homogeneous haze datasets. Intending to achieve haze removal for both types of haze, we propose a new architecture, developed on a convolutional neural network (CNN). The network is developed based on reformulating the atmospheric scattering phenomenon and estimating haze density to extract features for both types of haze. The haze-density estimation is supplemented by channel attention and pixel attention modules. The model is trained on perceptual loss. The quantitative and qualitative results demonstrate the efficacy of our approach on homogeneous as well as non-homogeneous haze as compared to the existing methods, developed for a particular type.
Deep-TDRS: An Integrated System for Handwritten Text Detection-Recognition and Conversion to Speech Using Deep Learning

Development of complete OCR for handwritten document (HOCR) is a challenging task due to a wide variation in writing styles, cursiveness, and contrasts in captured text images. We introduce a new three-staged pipeline process consisting of a) text detection, b) text recognition, c) text to speech conversion for the development of successful HOCR of multi-line document and converting them to speech. We have considered two state of the art object detection deep neural networks, EfficientDet and Faster R-CNN (Region based Convolutional Neural Network) followed by Weighted Boxes Fusion to obtain bounding boxes among all sentence wise text instances in the document. The detected text instances (image) are passed on to a hybrid CNN-RNN(CNN-Recurrent Neural Network) to obtain the recognized texts after appropriate training. The recognized text instances are provided as inputs to a state of the art TTS (Text to Speech) model DeepVoice3 for converting the text to speech which gets compiled as an audio book. The developed handwritten text detection and recognition model is comparable with the state of the art.
Dyadic Interaction Recognition Using Dynamic Representation and Convolutional Neural Network

Human interaction recognition can be used in video surveillance to recognise human behaviour. The goal of this research is to classify human interaction by converting video snippets into dynamic images and deep CNN architecture for classification. The human interaction input video is snipped into a certain number of smaller segments. For each segment, dynamic Image is constructed that efficiently encodes a video segment into an image with an action silhouette, which plays an important role in interaction recognition. The discriminative features are learned and classified from dynamic image using Convolutional Neural Network. The efficacy of the proposed architecture for interaction recognition is demonstrated by the obtained results on the SBU Kinect Interaction dataset, IXMAS, and TV Human Interaction datasets.
Moving Objects Detection in Intricate Scenes via Spatio-Temporal Co-occurrence Based Background Subtraction

Moving object detection using single feature-based techniques often fails in intricate scenes such as complex background, illumination variation, camouflage, moving background objects etc. In this letter, a novel feature descriptor, Multi-frame spatio-temporal co-occurrence (MSC) descriptor is proposed for moving object detection. The MSC descriptor collects the information from the 3D lattice which is formed by neighborhood (typically five) frames in a video. Co-occurrence matrix is computed from the collected 3D grid values. Then, Statistical textural features are calculated from MSC descriptor. Further, the background models are constructed by using Statistical textural features extracted from MSC descriptor, Gray scale intensity value and R, G, B values. Stability of adaptive feature (SoAF) model is modified by applying gaussian distribution with introduction of dynamic weights for proximity calculation. The performance of the proposed algorithm is validated on Change Detection dataset. Qualitative and quantitative analysis are carried out on benchmark datasets. The results after investigation, the proposed method outperforms the state-of-the-art techniques for moving object detection in terms of PR curves and F- measure.
Visual Localization Using Capsule Networks

Visual localization is the task of camera pose estimation, and is crucial for many technologies which involve localization such as mobile robots and augmented reality. Several convolutional neural network models have been proposed for the task against the more accurate geometry based computer vision techniques. However, they have several shortcomings and to our knowledge, this was the first effort that explored the use of an alternative architecture based on capsule-networks for the task. We achieved better results with capsules than with baseline-CNN PoseNet on small NORB dataset, modified for the task of camera pose estimation. Feature visualizations for both the networks produced more insights on their performance and behaviour. We found that there is a scope for improvement and hence propose few directions for future efforts.
The invertible elements of the monoid of dead-ending misère games

We study combinatorial games under restricted misère play, where the equality relation is weakened so that games can be equal modulo a subset of games even if they are not in full misère. In a dead-ending rule set (e.g., Domineering, Hackenbush), if a player has no move now, then they will never again have a move. Since no nonzero positions are invertible in full misère play, and few are invertible even in restricted play, an important open problem in restricted play is to classify the invertible positions. Modulo dead-ending games, it has been shown that all ends (at least one player cannot move) are invertible, some non-ends are invertible, and that many positions, such as ⁎, are not invertible; however, a complete classification has been an open problem since 2013. We solve this by proving that a game is invertible modulo dead-ending if and only if it is ‘P-free’ — i.e., if neither the game nor any of its followers are previous-win.
ISSUES AND CHALLENGES OF SCHOOL EDUCATION: AN INDIAN SCENARIO

The direction and status of education determines the future of a country. Quality education has emerged as an essential requirement of the present world. the main objective of this article was to conduct a comparative study of the basic infrastructure in government and private schools in India. This article is based on the Indian scenario of school education, an attempt has been made to bring out some of the shortcomings of the school education system and some suggestions have been presented at the end which can be helpful in disseminating quality education. The results of the present research shows that still the situation regarding computer and internet facility in both government and Private schools is very poor. Thus, it is understandable that a long way has to be covered to make computer aided learning a reality to deal with the problem of digital divide in India.
Measuring transmitted wavefronts for general optical systems in a broad bandwidth range

Although laser interferometers have emerged as the main tool for the measurement of transmitted wavefronts, their application is greatly limited as they are typically designed for operations at specific wavelengths. In a previous study [Q. Zhang, H. Wang, P. Wu, Y. Fu, X. Li, Q. Wang, and S. Han, “Estimating transmitted wavefronts in a broad bandwidth based on Zernike coefficients,” J. Opt. 21(9), 095601 (2019)], we proposed a method for determining the wavefront transmitted by an optical system at any wavelength in a certain band. Although this method works well for most monochromatic systems where the image plane is at the focal point for the transmission wavelength, for general multi-color systems, it is more practical to measure the wavefront at the defocused image plane. Hence, in this paper, we have developed a complete method for determining transmitted wavefronts in a broad bandwidth at any defocused position, thereby enabling wavefront measurements for multi-color systems. We also conducted experiments to verify these assumptions, validating the method. The experimental setup has been improved so that it can handle multi-color systems, and a detailed experimental process is summarized. With this technique, application of broadband transmission wavefront measurement can be extended to most general optical systems, which is of great significance for the characterization of achromatic and apochromatic optical lenses.
An Innovative Cryptosystem for Needham–Schroeder Authentication Protocols for Tertiary Industries

One of the main communication protocols designed for developing robustness across the entire insecure network has been proposed through an improved version of Needham–Schroeder (N-S) protocol. The proposed protocols and their conventions fulfill fundamental communication properties including confidentiality, common identification of the members, key authentication, and the construction of a common key. This paper outlines the improved version of N-S protocol based on a special case of binary quadratic Diophantine equation $${x}^{2}-D{y}^{2}=N$$ which has various industrial applications (where $$D$$ is a non-zero positive square-free and $$N$$ is a perfect square). In this paper, the critical applications of proposed N-S protocols in credit payment system and blockchain have been explained.
Impact of Sarcasm in Sentiment Analysis Methodology

Sentiment of analysis is a study area applied to numerous environments (financial, political, academic, business, and communication) whose purpose is to search for messages posted on social media, and through these to identify and classify people's opinions about particular item as positive or negative. Rating the sentiment expressed in opinionated messages is such an important task that currently companies invest a lot of money in collecting this type of information and the development of methods and techniques to classify the sentiment that they express, so that they can use the results as useful information in preparing marketing and sales strategies efficiently. However, one of the major problems facing the feelings of analysis is the difficulty of methods to properly analyze messages with sarcastic and/or ironic content, as these linguistic phenomena have the characteristic of transforming the polarity or meaning of a positive or negative statement into its opposite.
Building Sentiment Analysis Model and Compute Reputation Scores in E-Commerce Environment Using Machine Learning Techniques

Online reputation systems are a novel and active part of e-commerce environments such as eBay, Amazon, etc. These corporations use reputation reporting systems for trust evaluation by measuring the overall feedback ratings given by buyers, which enables them to compute the reputation score of their products. Such evaluation and computation processes are closely related to sentiment analysis and opinion mining. These techniques incorporate new features into traditional tasks, like polarity detection for positive or negative reviews. The “all excellent reputation” problem is common in the e-commerce domain. Another problem is that sellers can write unfair reviews to endorse or reject any targeted product since a higher reputation leads to higher profits. Therefore, the purpose of the present work is to use a statistical technique for excluding unfair ratings and to illustrate its effectiveness through simulations. Also, the authors have calculated reputation scores from users' feedback based on a sentiment analysis model (SAM). Experimental results demonstrate the effectiveness of the approach.
Computational Geoscience

A systematic approach to the evaluation of geochemical data involves the use of multivariate methods that identify processes. These processes are represented by element associations that reflect mineralogy. Processes may be linear or nonlinear, depending on the type of process. Different metrics can reflect different processes. Metrics with coordinates derived from principal component analysis, independent component analysis, and t-distributed stochastic embedding, to name a few, reflect different processes. The dominant components of these metrics can be used to enhance the signal/noise ratio in the data. An integral part of process discovery is the geospatial coherence of multivariate signatures. Models can be constructed by tagging the dominant components with attributes such as geology or mineral deposit information. These models can be tested using a range of multivariate classification/validation/prediction procedures from which probability-based measures of likelihood can be determined and displayed geospatially. The application of these techniques requires acknowledgment of the limitations inherent in the data.
Dimensionality Reduction*

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Error Estimation of the 3D Reconstruction of a Displacement Field from DIC Measurements

To enable an accurate simulation of manufacturing processes, it is essential to characterise and model the mechanical behaviour of sheet metals up to large deformations. However, after the onset of necking, deformation becomes highly heterogeneous and the stress is triaxial. By combining full-field measurements and inverse methods, it is possible to calibrate the mechanical behaviour beyond necking. A possibility is to use the virtual fields method, by extending its formulation to a fully three-dimensional approach. However, measuring deformation in the bulk of the material is still a challenge. To address this limitation, a volume reconstruction method able to estimate the deformation inside the specimen was proposed and successfully validated. The aim of this work is to estimate the error of the volume reconstruction method by using a simulated tensile test and the measurement chain associated with a virtual stereo-DIC setup composed of four cameras. A three-dimensional finite element model is used to deform synthetically generated images. The DIC field maps obtained with different setup configurations and DIC settings are used to estimate the error by comparing the reconstructed volume with the reference finite element model. Results show that the impact of conditions and DIC settings on the reconstructed volume is low.
Free Mobile Geographic Information Apps Functionalities: A Systematic Review

Background and Objectives: Geographic Information System (GIS) is a system designed to capture, store, analyze, and manage all types of geographical data. Mobile GIS has the ability to deliver functionalities, data and services without necessarily requiring a fixed location or wired connection. This paper analyzes the functionalities and potential of free mobile GIS applications available in Google Play Store and Apple App Store. Method: A well-known Systematic Literature Review (SLR) protocol was carried out to study free mobile GIS apps functionalities. A quality assessment questionnaire was developed for this purpose to be applied to the selected mobile GIS apps. Results: A total of 42 mobile GIS apps were selected from Apple App Store and Google Play Store. The results showed that the majority of mobile GIS apps support Global Positioning System (GPS) and were designed to be mainly used in geography, topography, Geo-Positioning, and transport domains. The search also showed that the Open Geospatial Consortium (OGC) protocols for web services remain less integrated in the apps. Conclusion: The features of mobile GIS were discussed and a set of recommendations to improve the development of mobile GIS applications was proposed.
Consequence operators, interior operators and fuzzy relations

In this article the relationship between fuzzy consequence and fuzzy interior operators with fuzzy relations is analyzed. While the situation has been studied in depth if the fuzzy relation is a ⁎-preorder it is not the case for arbitrary fuzzy relations. If R is an arbitrary fuzzy relation, a fuzzy consequence γR and a fuzzy interior δR operators can be derived from R. Using the Representation Theorem for ⁎-preorders, from the columns and rows of R two ⁎-preorders, Rcol and Rrow, can be generated that coincide with the ⁎-preorders generated by γR and δR respectively. A duality result between them and the ⁎-coherence of γR are also shown.
Use of Augmented Reality Technologies in Modern Media Libraries

The purpose of the study is to review the possible application of augmented reality technologies in modern media libraries and to argue the feasibility of using innovative developments in creating an updated information space for libraries. The research methodology is based on analysing existing innovative technologies used in libraries to modernize the media space, particularly the ways of applying augmented reality during the transformation of information centres in the media library. Scientific novelty. The paper presents an overview of existing ways of applying augmented reality technology in the information space organisation of modern media libraries, as well as offers new technological ways of library development and gives examples of AR use not only to promote reading or reference. Conclusions. The use of augmented reality technology in the development of media libraries is not a completely new trend. Everyone has long been familiar with augmented reality books, fairy tales, and educational materials. Nor is it an absolute innovation to use AR in research. Accepting today’s challenges, the library should not remain for all a common book depository, the only innovation where there is the use of modern digital technologies to access the resources of the collection. Due to the progress of the information society, the transformation of the library into a media library involves the creation of a radically new information space, which will provide a variety of information needs of users: from education to travel planning. This approach inevitably requires the use of digital technologies, including virtual and augmented reality technologies. The article considers various ways of using AR technologies, as well as the results of a student survey to create an innovative library space.
Solving systems of nonlinear equations with real world problems using an advanced hybrid algorithm

Systems of nonlinear equations (SNLEs) existed in various disciplines like engineering and applied mathematics. Finding solutions to SNLES is one of the most challenging problems. An advanced hybrid algorithm (haDEPSO) is proposed in this paper for finding the solution of SNLEs and real world problems, based on multi-population approach. Suggested advanced differential evolution (aDE) and particle swarm optimisation (aPSO) integrated with haDEPSO where in aDE a novel mutation strategy, crossover probability and slightly changed selection scheme is introduced (to avoid premature convergence) and novel gradually varying parameters familiarised in aPSO (to escape stagnation). Also in haDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. Performance of proposed haDEPSO as well as its integrating component aDE and aPSO are used to solve three SNLEs and three complex real world problems. Comparative analysis confirms superiority of the proposed algorithms.
Peningkatan Motivasi Dan Hasil Belajar Materi Lembaga Pemerintahan Desa Dan Kecamatan Melalui Penerapan Model Pembelajaran Word Square Pada Siswa Kelas IV SD 2 Getassrabi

Based on the results of the observations on the initial conditions in the 4th grade of SD 2 Getassrabi, the motivation and student learning outcomes of the village and sub-district governments were still very low. Student learning motivation is only 30% with completeness of learning outcomes only 40%. Therefore to improve this, research was carried out by applying the Word square learning model. And the results obtained after carrying out research for two cycles were student learning motivation increased to 80%, and learning completeness by 90%.
Magnetic Binary Encoding System Based on 3d Printing and Gmi Detection Prototype

In this work, the feasibility of a magnetic binary encoding system using 3D printing technology is analyzed. The study has a double interest, that is, the possibility of printing a 3D piece that contains the codified information and the development of a system for its decoding. For this purpose, magnetic nanoparticles (magnetite Fe 3 O 4 ) were embedded in a polymeric matrix of Polylactic Acid (PLA) and Poly-ε-caprolactone (PCL). Similar to a conventional barcode, a rectangular piece with an alternating pattern of strips with absence (only polymer) and a 5 wt % of embedded magnetic nanoparticles was 3D printed employing the Fused Deposition Modelling technique (FDM). The information was decoded by means of a Giant Magnetoimpedance (GMI) sensor-based prototype, by scanning the surface of the piece and measuring the changes in the magnetic field. As sensor nucleus, an amorphous soft magnetic wire of nominal composition (Co 0.94 Fe 0.06 ) 72.5 Si 12.5 B 15 was employed. The decoding prototype incorporates a homemade electronic sensor interface that permits, at the time, the GMI sensor excitation and the subsequent signal conditioning to optimize its response. The output signal enables the detection of the magnetite nanoparticles and the magnetic decoding of the encoded information (“1” and “0”, presence or absence of the magnetic nanoparticles, respectively).
Supervised Segmentation of Rti Appearance Attributes for Change Detection

This paper proposes a supervised segmentation method for detecting surface changes based on appearance attributes. Using the RTI method, both the PTM and HSH appearance coefficients are calculated for the sequence of two or more acquisitions representing different stages of the analysed surface. Additional normalisation is introduced, allowing consecutive acquisitions with different RTI setup and imaging parameters. The proposed method requires expert labeling on groups of pixels representing individual classes. Afterwards, the surface appearance is identified over time based on the calculated discriminant model. After segmentation, each detected category is assigned to a single colour to present the results with a user-friendly colourmap visualisation. The results were evaluated based on human expertise in the conservation-restoration field and are considered ground truth in this work. A case study with visibly segmentable characteristics was used to prove the concept and evaluate the invariance of the proposed method. Comparison with the segmentation of the visible characteristics shows very accurate segmentation for HSH (99%) and lower for PTM (80%), which is influenced by surface rotation. The method was applied in different case studies on metal surfaces and has shown promising results for condition assessment and monitoring corrosion or conservation-restoration treatments. Equally good is the possibility of qualitative quantifying the degree of change by counting the change of a selected class of pixels. PTM and HSH results are comparable in cases of mat surfaces; however, in high specular surfaces, HSH seems to provide more detailed information and, therefore, can better depict the surface characteristics. The method is user-dependent; the labelling of the pixels must be accurately defined based on the research question. Limitations of the application are related to the possibilities of identifying surface characteristics that do not exhibit topographic changes or significant reflectance differentiation.