Reviewerly/exHarmony huggingface dataset:
Z-Cognitive Map: An Integrated Cognitive Maps and Z-Numbers Approach under Cognitive Information

Usually, in real-world engineering problems, there are different types of uncertainties about the studied variables, which can be due to the specific variables under investigation or interaction between them. Fuzzy cognitive maps, which addresses the cause-effect relation between variables, is one of the most common models for better understanding of the problems, especially when the quantitative data are not available or the nature of the problem is qualitative. One of the significant issues of automatic construction of fuzzy cognitive maps is that it does not consider the experts' uncertainties because of their cognitive attitudes such as age, experience, etc., which affects the quality and validity of modeling of the complex problems. Therefore, in this paper, a Z-Cognitive maps approach is proposed based on the distance-based automatic construction approach and Z-numbers to capture uncertainty and ambiguity of experts' opinions on the variables and causality relationships. The suggested approach can act as decision support for cognitive maps problems considering the experts' cognitive information, which is tested by a numerical example.
Blind Polynomial Evaluation and Data Trading

Data trading is an emerging business, in which data sellers provide buyers with, for example, their private datasets and get paid from buyers. In many scenarios, sellers prefer to sell pieces of data, such as statistical results derived from the dataset, rather than the entire dataset. Meanwhile, buyers wish to hide the results they retrieve. Since it is not preferable to rely on a trusted third party (TTP), we are wondering, in the absence of TTP, whether there exists a practical mechanism satisfying the following requirements: the seller Sarah receives the payment if and only if she obliviously returns the buyer Bob the correct evaluation result of a function delegated by Bob on her dataset, and Bob can only derive the result for which he pays. Despite a lot of attention data trading has received, a desirable mechanism for this scenario is still missing. This is due to the fact that general solutions are inefficient when the size of datasets is considerable or the evaluated function is complicated, and that existing efficient cryptographic techniques cannot fully capture the features of our scenario or can only address very limited computing tasks.In this paper, we propose the first desirable mechanism that is practical and supports a wide variety of computing tasks—evaluation of arbitrary functions that can be represented as polynomials. We introduce a new cryptographic notion called blind polynomial evaluation and instantiate it with an explicit protocol. We further combine this notion with the blockchain paradigm to provide a practical framework that can satisfy the requirements mentioned above.
A New Blind Watermark Embedding Model: Spiral Updated Rider Optimization Algorithm

Abstract In this era, the exchanging of multimedia content has become much comfortable. Since the distribution and the illegal copying of digital multimedia content have become much easier, an author’s intellectual property copyrights have suffered from the violations that led to many damage to their benefits in many applications. This paper intends to propose a novel digital watermarking model for multimedia copyright protection that is based on 3D wavelet transformation. Initially, histogram-based motion frame extraction is conducted on the color video for which keyframes are extracted based on the extracted motion frames. To embed the watermark in the keyframes, 3D wavelet coefficients are selected followed by applying the spread spectrum technique. To conduct precise and effective embedding, it is planned to introduce fine-tuning aspects in the embedding process. As the intensity factor plays a major role in the embedding process, this paper tunes it rather than selecting randomly. Moreover, the Mersenne-Twister algorithm is known for spreading the power spectrum of watermark data to keep invisible. Hence, it is also intended to tune the seed factor of the respective algorithm to further effectuate the spreading process. These tuning processes can be solved as an optimization problem for which this paper proposes a new hybrid optimization algorithm named Spiral Updated Rider Optimization Algorithm (SU-ROA). SU-ROA is a hybrid variant of the Rider Optimization Algorithm (ROA) and Whale Optimization Algorithm (WOA). The performance of the proposed work is compared and proved over the state-of-the-art models in terms of both embedding and extracting effectiveness.
Eye Gaze-Based Human Error Prevention System: Experts vs. Non-Experts.

As over 95% of IT security breaches result from human error, there is a need to design a system to detect and prevent it. In this paper, we propose a human error prevention system that examines the users’ eye gaze behavior and determines if a human error is likely to occur. 13 expert network administrators and 18 non-experts were selected for this study. The results confirm the ability to detect human error using eye gaze data with 99.74%, 99.47%, 99.23% accuracy using KStar, Random Forest and J48 classifiers.
Angular Luminance for Material Segmentation

Moving cameras provide multiple intensity measurements per pixel, yet often semantic segmentation, material recognition, and object recognition do not utilize this information. With basic alignment over several frames of a moving camera sequence, a distribution of intensities over multiple angles is obtained. It is well known from prior work that luminance histograms and the statistics of natural images provide a strong material recognition cue. We utilize per-pixel angular luminance distributions as a key feature in discriminating the material of the surface. The angle-space sampling in a multiview satellite image sequence is an unstructured sampling of the underlying reflectance function of the material. For real-world materials there is significant intra-class variation that can be managed by building a angular luminance network (AngLNet). This network combines angular reflectance cues from multiple images with spatial cues as input to fully convolutional networks for material segmentation. We demonstrate the increased performance of AngLNet over prior state-of-the-art in material segmentation from satellite imagery.
Detailed analysis of implementation of aviation NMT system and the effects of aviation post-processing tools on TDIL tourism corpus

Capable MT systems implemented using SMT and NMT for languages such as Bengali and other Indian languages are used regularly. The performance of MT systems is regulated by the domain knowledge which is directly derived from the parallel corpus that provides the guidelines used to train the model. In the last few years, spectacular results have been achieved by systems using various NMT models. Organizations like Google and Microsoft have shifted from SMT to NMT models. In this paper, we compare the implementation of the unexplored aviation domain with standard domains whose corpuses are downloaded from TDIL (https://tdil.meity.gov.in/) and also have a look at the impact of the post-processing tools on the Tourism corpus of TDIL. The implementation was accomplished using OpenNMT. English to Bengali Aviation parallel corpus has been developed and implemented along with multiple post-processing and pre-processing tools to get the desired results. The developed aviation post-processing tools have been later implemented upon TDIL Tourism corpus to test the effectiveness of the tools on non-aviation but similar corpus. The result analysis involve comparing the BLEU scores of the aviation domain and the BLEU scores of the Tourism Domain before and after the applications of the Pre and Post-processing tools.
Global Data and Digital Public Health Leadership for Current and Future Pandemic Responses

OPINION article Front. Digit. Health, 28 January 2021 | https://doi.org/10.3389/fdgth.2021.632568
Hybrid Reality based Education Expansion System for Intelligent Learning

Most educators use traditional teaching method to conduct the teaching and learning activities in the classroom by the face-to-face method via verbal communication. However, the teaching and learning environment has grown beyond the classroom. The integration of technology in the teaching and learning process is the new trend in education, with a positive outcome. Technologies provide an environment for learning activities to occur anytime and anywhere that benefits educators and learners across the globe. One of the skills that have been showing possibilities of the appliance in educational environments is the Hybrid Reality (HR), including both Virtual Reality (VR) and Augmented Reality (AR). This work tries to build upon the current state of hybrid reality and its application in education. The first segment describes the basic structure of hybrid reality and its different parts. Following segments give a definitive structure of some innovative applications that developed for the hybrid reality. Lastly, the paper shows the benefits of those applications over the traditional teaching methods and the essential user reactions to them.
Text Extraction From Image Using MATLAB

The goal of this paper is for extricating the text present in image and identify the text written using MATLAB programming language. Text Extrication from image and recognition as a whole have enormous relevance from the point of view of application, like content-based image restoration, document indexing and identification. Moreover, this triggers vast advanced systems. This work is focusing on detecting text in an image. To find a special sign in a natural image is sometimes frustrating. By using this method, it is easy to identify this type of object in an image like traffic sign, using Optical Character Recognition (OCR). Automatic task detection algorithms help to identify such situations in an image and highly efficient. It also uses Canny Edge Detection to discover the boundaries of the text present in the image from the Maximally Stable Extremal Regions (MSER).
A Heteroscedastic Likelihood Model for Two-Frame Optical Flow

Machine vision is an important sensing technology used in mobile robotic systems. Advancing the autonomy of such systems requires accurate characterisation of sensor uncertainty. Vision includes intrinsic uncertainty due to the camera sensor and extrinsic uncertainty due to environmental lighting and texture, which propagate through the image processing algorithms used to produce visual measurements. To faithfully characterise visual measurements, we must take into account these uncertainties. In this letter, we propose a new class of likelihood functions that characterises the uncertainty of the error distribution of two-frame optical flow that enables a heteroscedastic dependence on texture. We employ the proposed class to characterise the Farnebäck and Lucas Kanade optical flow algorithms and achieve close agreement with their respective empirical error distributions over a wide range of texture in a simulated environment. The utility of the proposed likelihood model is demonstrated in a visual odometry ego-motion study, which results in performance competitive with contemporary methods. The development of an empirically congruent likelihood model advances the requisite tool-set for vision-based Bayesian inference and enables sensor data fusion with GPS, LiDAR and IMU to advance robust autonomous navigation.
Thai Word Segmentation Based on Sequence-to-Sequence Model

Abstract Thai as a low-resource language has a large word segmentation performance improvement space. In this paper, we investigate a sequence-to-sequence model for Thai word segmentation with two different recurrent neural networks, which could transform one input sequence into another output sequence. Furthermore, we evaluate datasets in four different fields compared then with other multiple word segmentation models, and the F1 value in the encyclopedia dataset reaches 97.15%. The results show that the proposed model has superior performance and is more effective, it is worth mentioning that the expected results can be achieved even with limited data resources.
Empirical Evaluation of Bug Proneness Index Algorithm

Researchers have devised and implemented different bug prediction approaches that use different metrics to predict bugs in software modules. However, the focus of research has been on proposing new approaches/models to predict bugs rather than on validating performance of existing approaches. In this paper, the authors evaluate and validate the findings of an algorithm that predicts the bug proneness index (bug score) of the software classes/modules. The algorithm uses normalized marginal R square values of software metrics as weights to the normalized metrics to compute bug proneness index (bug score). The experiment was performed on Eclipse JDT Core and reports significant improvements in F-measure of their algorithm as compared to the multiple linear regression. The authors found that there was no improvement in F-measure of evaluated algorithm compared to multiple linear regression. The use of marginal R square values as weights to the metrics in linear functions in the evaluated model instead of regression coefficients had no performance boost compared to the multiple linear regression.
Awjedni: A Reverse-Image-Search Application

The abundance of photos on the internet, along with smartphones that could implement computer vision technologies allow for a unique way to browse the web. These technologies have potential used in many widely accessible and globally available reverse-image search applications. One of these applications is the use of reverse-image search to help people finding items which they're interested in, but they can’t name it. This is where Awjedni was born. Awjedni is a reverse-image search application compatible with iOS and Android smartphones built to provide an efficient way to search millions of products on the internet using images only. Awjedni utilizes a computer vision technology through implementing multiple libraries and frameworks to process images, recognize objects, and crawl the web. Users simply upload/take a photo of a desired item and the application returns visually similar items and a direct link to the websites that sell them.
Facial Expression Recognition based on Graph Convolutional Networks with Phase Congruency

Phase congruency extracts features based on phase information. This paper proposes a facial expression recognition method based on phase congruency, which combines the features obtained from the graph convolutional neural network with the phase congruency to achieve high-precision recognition of facial expression images. Experimental result show that the method proposed performs better than the traditional graph convolutional neural network on both CK+ and JAFFE datasets.
Quantum Algorithms

In the last two decades, the interest in quantum computation has increased significantly among research communities. Quantum computing is the field that investigates the computational power and other properties of computers on the basis of the underlying quantum-mechanical principles. The main purpose is to find quantum algorithms that are significantly faster than any existing classical algorithms solving the same problem. While the quantum computers currently freely available to wider public count no more than two dozens of qubits, and most recently developed quantum devices offer some 50-60 qubits, quantum computer hardware is expected to grow in terms of qubit counts, fault tolerance, and resistance to decoherence. The main objective of this chapter is to present an introduction to the core quantum computing algorithms developed thus far for the field of cryptography.
Knowledge Building Processes Between Interaction and Collaboration

Knowledge building processes are involved in epochal changes that affect society. It was deemed necessary to start a study on the effects of these changes and processes on training practices and their models, also considering the consequences, not always explicit, that both of them have towards behaviors and values. The work also starts from the consideration, which many researches have highlighted, of how communication technologies and society are factors of great influence on learning-teaching processes. The learning-teaching processes are often integrated by blended methodologies and technologies certainly increase the factors of connection and interaction between the actors of the formal, non-formal and informal training processes. A balanced and preparatory analysis of effective practices is based on knowledge of learning processes and cognitive practices. The work describes the first phase of the research, an exploratory research on teachers’ perception on the relationship between cognitive styles, learning and technologies integrated in the learning-teaching processes. The first part of the research started from the investigation of the teachers’ thoughts, from their “feeling” of how the world of technologies and their pervasive use influenced cognitive styles and, in any case, students with school skills and attitudes secondary, regarding learning processes. From the result obtained, the second part of the research was developed which investigates the processes of building knowledge and which the situation of Pandemic has prevented from carrying out in the expected times. For this reason, the first phase of the research and the theoretical structure of the next phase are presented, in terms of methods and objectives.
Resource-Aware Pareto-Optimal Automated Machine Learning Platform

In this study, we introduce a novel platform Resource-Aware AutoML (RA-AutoML) which enables flexible and generalized algorithms to build machine learning models subjected to multiple objectives, as well as resource and hardware constraints. RA-AutoML intelligently conducts Hyper-Parameter Search (HPS) as well as Neural Architecture Search (NAS) to build models optimizing predefined objectives. RA-AutoML is a versatile framework that allows user to prescribe many resource/hardware constraints along with objectives demanded by the problem or even business requirements. At its core, RA-AutoML relies on our in-house search-engine algorithm, MOBOGA, which combines a modified constraint-aware Bayesian Osptimization and Genetic Algorithm to construct Pareto optimal candidates. Our experiments on CIFAR-10 dataset shows very good accuracy compared to results obtained by state-of-art neural network models, while subjected to resource constraints in the form of model size.
Visibility Enhancement in Low Light Images with Weighted Fusion of Robust Retinex Model and Dark Channel Prior

Images captured under poor illumination or at night time doesn't have significant details as compared to images captured under proper lighting conditions. These images, when used for computer vision applications might be the reason for undesirable output. So, these kinds of images are not suitable for observation and analysis is case of any computer vision application. To solve this problem, visibility enhancement in low light images with weighted fusion of robust retinex model and dark channel prior based enhancement method have been proposed in the literature. The paper proposes visibility enhancement in low light images with weighted fusion of robust retinex model and dark channel prior based enhancement. The validation of proposed method is judged based on entropy. The performance based on the entropy as measure, is evaluated for proposed system and compared with the other existing popular low light image enhancement techniques. For rigorous validation, different weights combinations are explored in the proposed fusion based image enhancement method.
Compliance with journalistic standards in the headlines of print and online publications

The headline is the most important part of the news. The headline should be readable, attractive, informative and concise. In this study theoretical methods of scientific knowledge were used. In the process of work methods of analysis and synthesis of information, as well as a method of classification, were also used. The aim of this work is to define standards for titles of print and online publications. Also, the main goal of this study is to determine the level of adherence to journalistic standards in the most popular Russian print and Internet publications. This paper provides a general description of journalistic standards, as well as standards for article titles. The study presents statistics on the popularity of Russian mass media, characteristics and examples of the main mistakes in article titles. This paper presents a detailed analysis of errors for some articles of popular Russian publications. The information presented in the results of this work can be used in the process of studying the standards for the titles of articles, as well as for the analysis of typical mistakes using the example of Russian publications.
Bridging Software Engineering Research and Industrial Practice

There is a long-standing challenge to narrow the gap between software engineering research and industrial practice. This gap is reinforced by a number of challenges, including differing timelines, metrics, expectations, and perceptions of these two communities. We believe that these and other related challenges need be analyzed and discussed, to discover synergies and strengthen collaborations between researchers and practitioners in software engineering. In this report, we present insights from the 7th International Workshop on Software Engineering Research and Industrial Practice held virtually at the International Conference on Software Engineering 2020.
<i>Continuous</i> discrete cubature quadrature Kalman filter

Abstract In this paper, cubature quadrature Kalman filter algorithm has been reformulated for nonlinear state space model of continuous‐discrete nature. The developed method is named as continuous‐discrete cubature quadrature Kalman filter (CD‐CQKF). The formulated algorithm has been applied to track the trajectory of an aircraft. The process is described with coordinated turn model. From the simulation result, it has been observed that the developed CD‐CQKF provides better estimation compared with continuous‐discrete cubature Kalman filter.
A new technology on translating Indonesian spoken language into Indonesian sign language system

People with hearing disabilities are those who are unable to hear, resulted in their disability to communicate using spoken language. The solution offered in this research is by creating a one way translation technology to interpret spoken language to Indonesian sign language system (SIBI). The mechanism applied here is by catching the sentences (audio) spoken by common society to be converted to texts, by using speech recognition. The texts are then processed in text processing to select the input texts. The next stage is stemming the texts into prefixes, basic words, and suffixes. Each words are then being indexed and matched to SIBI. Afterwards, the system will arrange the words into SIBI sentences based on the original sentences, so that the people with hearing disabilities can get the information contained within the spoken language. This technology success rate were tested using Confusion Matrix, which resulted in precision value of 76%, accuracy value of 78%, and recall value of 79%. This technology has been tested in SMP-LB Karya Mulya on the 7th grader students with the total of 9 students. From the test, it is obtained that 86% of students stated that this technology runs very well.