Reviewerly/exHarmony huggingface dataset:
Face 2D to 3D Reconstruction Network Based on Head Pose and 3D Facial Landmarks

Although most existing methods based on 3D mor-phable model (3DMM) need annotated parameters for training as ground truth, only a few datasets contain them. Moreover, it is difficult to acquire accurate 3D face models aligned with the input images due to the gap in dimensions. In this paper, we propose a face 2D to 3D reconstruction network based on head pose and 3D facial landmarks. We build a head pose guided face reconstruction network to regress an accurate 3D face model with the help of 3D facial landmarks. Different from 3DMM parameters, head pose and 3D facial landmarks are successfully estimated even in the wild images. Experiments on 300W-LP, AFLW2000-3D and CelebA HQ datasets show that the proposed method successfully reconstructs 3D face model from a single RGB image thanks to 3D facial landmarks as well as achieves state-of-the-art performance in terms of the normalized mean error (NME).
Breast Cancer Classification in IoT Framework

In the last decade, Breast Cancer (BC) is one of the widespreading diseases for women and causing of the death in the world wide. Early detection of BC can enhance the changes of survival. Now there are many techniques and methodologies for detecting BC in an appropriate time. Machine learning (ML) technique can be used to observe and classify the abnormalities that happened inside the breast such as micro-calcifications, masses, and asymmetry. With the help of ML techniques, it has become easier to distinguish tumor from the affected breast and diagnose BC. This paper aims to classify breast tumor (benign or malignant) in Internet of Things (IoT) health environment framework. Both Support Vector Machine (SVM) and back-Propagation classifiers are used for classification of breast tumor. In this paper, three performance evaluation metrics are used to evaluate, which are; classification accuracy, sensitivity, specificity.
Face Shape Transfer Via Semantic Warping

Face reshaping aims to adjust the shape of a face in a portrait image to make the face aesthetically beautiful, which has many potential applications. Existing methods 1) operate on the pre-defined facial landmarks, leading the results affected by artifacts or distortions due to the limited number of landmarks, 2) synthesize new faces based on segmentation masks or sketches, causing generated faces to look dissatisfied due to the losses of skin details and difficulties in dealing with hair and background blurring, or 3) project the positions of the deformed feature points from the 3D face model to the 2D image, making the results unrealistic because of the misalignment between feature points. In addition, the adjusted results of these methods depend on the preference of the user, which is very subjective and difficult to evaluate. In this paper, we propose a novel method named face shape transfer (FST) via semantic warping, which is capable of transferring both the overall face and individual components (e.g., eyes, nose, mouth) of a reference image to the source image while preserving the facial structure and personal identity. To facilitate extensive evaluation, we construct a large-scale high-resolution face dataset, which contains 14,000 images with the resolution of 1024 â‡¥ 1024. The superior performance of our method is demonstrated by qualitative and quantitative experiments on the benchmark dataset.
Neural Network Model Extraction Based on Adversarial Examples

The neural network model has been applied to all walks of life. By detecting the internal information of a black-box model, the attacker can obtain potential commercial value of the model. At the same time, understanding the model structure helps the attacker customize the strategy to attack the model. We have improved a model detection method based on input and output pairs to detect the internal information of the trained neural network black-box model. On the one hand, our work proved that adversarial examples are very likely to carry architecture information of the neural network model. On the other hand, we added adversarial examples to the model pre-detection module, and verified the positive effects of adversarial examples on model detection through experiments, which improved the accuracy of the meta-model and reduced the cost of model detection.
Determining the Priority Criteria for Personnel Selection With Fuzzy Dematel and Grey-Based Dematel Approaches

Although robots and other technologies replace manpower in today's business world with digitalization, the human factor is still one of the most important productivity and efficiency factors for many businesses. Managers have to consider many factors and constraints while selecting the human resources to be employed in their companies. In this study, the DEMATEL method was applied to determine the important criteria for the personnel to be recruited into an enterprise. It was combined as Fuzzy DEMATEL and Grey DEMATEL in order to remove the ambiguity in the linguistic expressions of the decision-makers while indicating the degree of importance between the criteria.​However, the linguistic expressions taken from experts while conducting the research may be in a range other than being expressed in clear numbers, the system becomes more reliable, and expert evaluations are obtained with grey numbers instead of triangular fuzzy numbers used in fuzzy logic, and this increases the reliability of the evaluations. Therefore, the problem applied the Fuzzy DEMATEL method was solved with Grey DEMATEL, in other words, the flexible structure of Grey DEMATEL was adapted to the problem. At the end of both methods, the criteria were ranked according to their weights and the results were compared.
A New History of Logic: The Laborious Birth of a Formal Pluralism

The paper starts by remarking that the ancient Greek word for "truth" was "alétheia" (unveiling), which is a double negation.But, after Plato the affirmative meaning of the idea of truth has prevailed.The same meaning was reiterated by Romans's word for truth, veritas.Not before the year 1968 the double negation law was re-evaluated, since its failure was recognized as representing, more appropriately than the failure of the excluded middle law, the borderline between classical logic and almost all non-classical kinds of logic.Moreover, its failure is easily recognized within a (scientific) text; which therefore can be analyzed in a new logical way.As an example, the analysis of Kolmogorov's 1932 paper about the foundations of the intuitionist logic shows many interesting results, in particular his reasoning through arguments pertaining to the same intuitionist logic.In addition, previous papers have suggested-through a comparative analysis of all scientific theories which are based, like the previous one, on a general problem-that there exists a new model of organization of a theory which is alternative to the deductive-axiomatic model and it is governed by intuitionist logic.Some important logical events pertaining to non-classical logic are recognized by inspecting through both the double negations and this new model of organizing the original texts of past theories; so that the entire history of logic appears as a development along two main lines, one line representing classical logic dominated all others for a very long period of time, although at the origins the logical arguing had pertained to the alternative line.This history of logic confirms both Kuhn's category of a paradigm and Koyré's categories, represented by him through a couple of "characteristic propositions" in mutual conflict; analogous couples of propositions are suggested as representing the categories for adequately interpreting the entire history of logic.
WORLDWIDE APPLICATION TO CONTROL COVID-19 BY REMOTE MANAGEMENT OF PATIENT RECORDS ACCESSED BY AN INTERNET OF THINGS

Medical professionals need new technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) to combat COVID-19. A major aspect of this study is to examine how IoT-based remote monitoring technology can be used to record the medical history of patients, trace, monitor, analyze, and prevent COVID-19 and other health outbreaks. We have designed an IoT based patient's health record (PHR) that includes actual and prospective information about the patient, including their contacts with healthcare providers, medications, and a history of operations and hospitalizations. By making medical records available to patients, they can become more involved in their own health care, and this will help reduce direct contact with health care providers. The risk of a virus or infection spreading among medical health care providers and patients can be minimized through reduced contact. The system stores patient's personal information and medical records. Information regarding the next appointment, medicine intake, and prescriptions is sent to the patients on their email address. Additionally, we have built an Ambulance Kit using Arduino Nano, which is intended for use during medical emergencies.
A Modified Recursive Newton-Euler Algorithm Embedding a Collision Avoidance Module

Planning collision-free trajectories for robotic manipulators is a quite old problem that has recently re-gained importance thanks to collaborative robotics. In scenarios where a robot is moving close to an operator, it is of paramount importance to detect obstacles (e.g. human’ arms) that must be avoided. When the environment is dynamic this problem is still challenging. In this paper we integrate a recent collision avoidance algorithm based on the efficient computation of distances between capsules and the environment into the well-known recursive Newton-Euler algorithm for computing the inverse dynamics of serial-link manipulators. This approach allows to compute repulsive torques at the joint level that guarantee collision-free motion at run-time. The proposed approach has been validated on a simulated environment using a six degrees of freedom UR5 robot.
Exploring the Need For an Updated Mixed File Research Data Set

Mixed file data sets are used in a variety of research areas, including Digital Forensics, Malware analysis and Ransomware detection. Researchers recently seem to either have to create their own custom data sets or well-known data sets are used, but additional file types have been added. In the majority of research, these data sets are never published. This paper outlines ongoing research around currently available mixed file data sets to determine if a need exists for a new modern cybersecurity mixed file data set, and how this could be created and maintained. Part of the investigation would include identifying common files types currently needed for research, and an outline methodology into data set creation is also presented. It was found when reviewing ransomware detection research literature that almost no proposal provided enough detail on how the test data set was created, or sufficient description of its actual content, to allow it to be recreated by other researchers interested in reconstructing their environment and validating the research results.
Implementing Dampster-Shafer Theory for Property Similarity in Conceptual Spaces Modeling

Previous work has shown that the Complex Conceptual Spaces - Single Observation mathematical framework is a useful tool for event characterization. This mathematical framework is developed on the basis of Conceptual Spaces and uses integer linear programming to find the needed similarity values. The work of this paper is focused primarily on space event characterization. In particular, the focus is on the ranking of threats for malicious space events such as a kinetic kill. To make the Conceptual Spaces framework work, the similarity values between the contents of observations on the one hand and the properties of the entities observed on the other needs to be found. This paper shows how to exploit Dempster-Shafer theory to implement a statistical approach for finding these similarities values. This approach will allow a user to identify the uncertainty involved in similarity value data, which can later be propagated through the developed mathematical model in order for the user to know the overall uncertainty in the observation-to-concept mappings needed for space event characterization.
Image To Image Translation in Video Call Using Residual Based StyleGAN Encoder

Conditional adversarial networks can be used as a solution to non labeled image-to-image translation problems, where the image is not identified as objects. These networks use a loss function to learn the mapping from input image to output image. This makes it possible to apply the same generic approach to problems that traditionally would require very difficult loss formulations and large image datasets. This paper demonstrates that our approach of using conditional adversarial networks to effectively synthesize images from label maps and reconstructing objects to latent space maps. It's applicability and ease of adoption without the need for parameter tuning into a video output by producing it frame by frame. This paper also contributes with reasonable results can be achieved without hand-engineering of loss functions in an adversarial network manually, with less latency and high throughput.
Amharic Text Corpus based on Parts of Speech tagging and headwords

Corpus is a milestone to study natural languages and to develop various tools for the processing of human languages. Since, few studies are carried out on the development of Amharic language corpus development, the existing corpora are very small in size and not well accessible for academicians as well as commercial and non-commercial organizations. This paper presents Amharic text corpus developed by applying the processes of annotating each word with its part of speech tag and reducing each orthographic word to its headword using either derivational or inflectional process. We extracted 12,720 sentences from various text documents collected in the domain of proclamations. Ethiopian 1987 E.C constitution and a few policies of Amhara regional state, Ethiopia and federal government of Ethiopia are some of those documents. We found 331,728 tokens from those sentences. 66 tag sets are compiled from base part of speech tag set classes and compound part of speech tag set classes based on different factors and representation of orthographic words. To help the manual annotation of each orthographic word, we developed a semi-automatic Amharic text tagger. The outputs of the research project are pre-processed Amharic text stored in plain text format and tagged Amharic text corpus encoded with extensible markup language format. The tag sets of annotated text corpora are represented in both Ge'ez script and English characters. We plan to increase the number of tag sets and size of text corpus in the near future. Moreover, we are working towards converting the semi-automatic Amharic text tagger to full automation.
Fusing the Degree of Nodes in the Session Graph for Session-Based Recommendation

With the increasing number of data appears in the form of session, it shows great importance to predict the future items based on the present ones in the session. By now, great progress has been made in the Graph Neural Network to build the session-based recommendation system. Nevertheless, the existing method of session-data modeling through the graph neural network ignores the degree of nodes which to some extent reflects the importance of the nodes in the graph. Intuitively, the possibility of the item to be clicked increases along with the degree of the node represents this item. Inspired by the aforementioned observation, we analyze the session data and propose to use the degree information of the nodes in the session graph to improve the effect of session recommendation. The experiments show that the proposed method outperforms the current mainstream approaches on a number of real-world data sets, such as Tmall and Diginetica.
Outlier Detection and Abnormal Subspace Search Based on Autoencoder and Genetic Algorithm for High-Dimensional Data

The curse of dimensionality in high-dimensional data makes it difficult to detect outliers in full data space. In this paper, we propose an outlier detection and abnormal subspace search method based on autoencoder and genetic algorithm for high-dimensional data to alleviate this problem. This work uses neural networks to build an autoencoder that forces the high-dimensional data to be mapped into a low-dimensional space. The compressed data is decoded and reconstructed, and the error between the original data and the reconstructed data is compared with a threshold value to achieve unsupervised outlier detection. Then our method searches the subspaces of high-dimensional outliers by genetic algorithm. It encodes subspaces of the outliers and uses the redefined fitness function to evaluate the degree of outlier of the detected subspace. The subspace with the highest degree of outlier is output as the abnormal subspace of the detected point. The abnormal subspace features can be used as a basis for analyzing the causes of outliers. Experimental results reveal that the proposed method, when compared with other competitive methods, can effectively detect outliers in high-dimensional data and obtain the accurate abnormal subspace of outliers.
RTStitch: Real-time Stitching of High-resolution PCB Images

In the Automatic Optical Inspection (AOI) system, it is necessary to stitch high-resolution Printed Circuit Board (PCB) partial images in real time to obtain a panoramic image. However, the proportion of the overlapping area of these images is usually relatively small, and there are few feature points in the overlap area of some images. Traditional image stitching algorithms are not effective in processing such images, and it is difficult to meet the requirements of real-time stitching. Therefore, we propose RTStitch, a method specially used for real-time stitching of high-resolution PCB images in AOI systems. We divide the images into two categories. An image with a large change in the pixel gray value of the first column in the overlapping area is the first type of image, otherwise, it is the second type of image. We use different methods to obtain a relatively accurate maximum overlap position for these two types of images. Besides, we propose a new method of selecting template images, then perform template matching to obtain horizontal and vertical errors. We focus on the areas where the gray level changes drastically in the overlapping area to improve the accuracy of the registration. Our experimental results show that for the first type of image, RTStitch improves the registration accuracy by more than 2%, while for the second type of image, the registration accuracy is improved by more than 10%. And more than doubled the stitching speed. Moreover, RTStitch can meet the high-precision and real-time requirements of the AOI system.
BLENDED LEARNING MODEL FOR THE DEVELOPMENT OF EFL LEARNERS’ COMMUNICATIVE COMPETENCE (CASE OF SOUTH URAL STATE UNIVERSITY: HIGHER SCHOOL OF ELECTRONIC ENGINEERING AND COMPUTER SCIENCE)

The world is developing dynamically, and the changes, that occur every day, require instant decision-making. The examples of this are the latest events in the world: lockdown and distance learning. The 21st century has tested humanity for sustainability introducing COVID-19 and many business sectors as well as the education system at all levels had to switch to a remote format. However, the South Ural State University has managed to build a full-fledged online learning system, which provides opportunities for both teaching staff and students. The article reveals the key theoretical and practical aspects of the system of blended learning at the South Ural State University, as a new design for the teaching and learning cooperation: individual education routes depending on the initial level of language proficiency; placement tests to form groups by levels with online support for them; appropriate score-rating system for monitoring students' progress at each level. Moreover, the presented level-based model of education contributes to more effective mastery of communication skills (speaking, listening, writing and reading). The pedagogical model contains three blocks: methodological block, group processes’ block and assessment block. The model was piloted in an online digital course with the students of Higher School of Electronic Engineering and Computer Science. After piloting the model, the survey was conducted among the students that participated in a blended level education program. The aim of our research was to study the satisfaction level of students with the quality of education and to analyze the implementation process of the blended learning technology. During the research the following tasks were solved: the experience of using blended learning in educational process was analyzed, the level of students’ satisfaction with the quality of education system was evaluated. This research shows that the implementation of blended learning technology in educational process had a positive impact on students' learning and the level of students` satisfaction with the education process increased.
Multi-Modal Sarcasm Detection in Weibo

Irony is a writing technique with a sense of irony, and the original meaning it expresses is just the opposite of what can be understood literally. However, the existing sarcasm detection is mostly based on text analysis and seldom pays attention to the influence of emoji pictures on the expression of text emotion. This article focuses on multi-modal satire detection for tweets composed of text and emoticons on Weibo. Firstly, image features and expression features are extracted, and then expression features and Bi-LSTM network are used to extract text features. Finally, the three modal features are fused for prediction. We created a Weibo-based multi-modal irony detection data set, and the evaluation results on the data set proved the effectiveness of our proposed model.
Privacy Information Leakage Prevention in Cognitive Social Mining Applications

Online social networks, such as Facebook are increasingly used by many users and these networks allow people to publish and share their data to their friends. The problem is user privacy information can be inferred via social relations. This chapter makes a study and performs research on managing those confidential information leakages which is a challenging issue in social networks. It is possible to use learning methods on user released data to predict private information. Since the main goal is to distribute social network data while preventing sensitive data disclosure, it can be achieved through sanitization techniques. Then the effectiveness of those techniques is explored, and the methods of collective inference are used to discover sensitive attributes of the user profile data set. Hence, sanitization methods can be used efficiently to decrease the accuracy of both local and relational classifiers and allow secure information sharing by maintaining user privacy.
Improving Accuracy and Precision through Machine Learning Fusion using Two-Line Element Sets

The machine learning (ML) approach can improve both the orbit prediction accuracy and precision with a properly designed fusion strategy. ML models are trained based on historical TLE sets and then can generate ML-corrections to the future state and uncertainty predictions. The ML-correction will be first given a pseudo-physical interpretation as pseudo-measurements of the true orbit prediction error and then fused with the conventional orbital state and uncertainty propagation results. The regularized particle filter (PF) with progressive correction and systematic sampling methods is used to accomplish the uncertainty propagation task using the SGP4 model. Comprehensive experiments on different resident space objects (RSOs) with TLE sets in different orbit types are carried out and analyzed. The results reveal that the fusion strategy enables the ML approach to work with the advanced PF prediction method, which can enhance both the prediction accuracy and precision of PF.