Reviewerly/exHarmony huggingface dataset:
Anthropomorphic information networks and converging technologies: challenge to humanity vs step forward?

The impact of convergening technologies on the social processes of the information society is investigated. It is grounded that at the beginning of the 21st century, the globalization of society is realized by means of convergening technologies. They are based on innovative communication technologies, in particular, technologies of artificial intelligence. The converging technologies of social structure expansion transform it into a network architect, where sustainability of social relations is the most important ergument. The recognition of the lack of administrative influence to achieve socially important goals leads to the fact that convergence of technologies is an irreversible part of the new technological way. The strengthening of the informational pressure that accompanies the formation of the network society further removes the person from realization of its potential, replacing critical, rational thinking with stereotyped, formalized information and communication exchange. As a result, the more dispersed (convergent) are social institutions and their networks, the less capable and willing to control them. Humanity was first faced with the challenge of its intellect, ability to create, control, recognize. Is society ready to give part of the management of the virtual network and artificial intelligence to the face of global problems? This question is before humanity. That is why the author underlies the idea that social modernization requires interpretation of convergence as a strategic innovation. Lack of public control over the convergence of networks leads to reverse – diverging processes. It is shown that the latter produce different forms of social alienation. And the emergence of anthropomorphic networks, based on the technologies of big data processing, machine learning and artificial intelligence, sharply pose questions of the future before every inhabitant of the planet.
Search for identical points in the inter-pixel space of video images

One of the ways to describe objects in images is to identify some of their characteristic points or points of attention. Areas surrounding attention points are described by descriptors (a set of features) in such a way that they can be identified and compared. On these features the search for identical points on other images is carried out by scanning them with a sliding window. The most famous descriptors and methods for finding identical points are: SIFT, SURF, GLOH, BRIEF and others. This group of methods is characterized by the fact that the displacement of identical points in video images can be arbitrary, but the accuracy of calculating their coordinates depends on the bit grid of video images and, in the best case, is equal to the interpixel distance. Another group of methods that can be used to track identical points of video images are methods built on the basis of optical flow calculation. One of the popular methods of tracking points based on optical flow calculation is the Lucas-Kanade method. It allows you to calculate the displacement of points in the interpixel space due to the solution of differential equations. To date, the Lucas-Kanade method has several modifications. A limitation of these methods is that the neighborhoods of the shifted points must overlap to a large extent. The article investigates and proposes the complex application of methods of scanning video images with a sliding window and differential calculation of optical flow, which allows to increase the accuracy and speed of calculating the coordinates of identical points in the images in relation to the search for these points only by scanning. A more accurate calculation of the coordinates of the characteristic points of the object in the interpixel space of video images will lead to a more accurate determination of the position and orientation of these objects in 3D space. The simulation was carried out using the method of rough search for identical points of video images described by invariant moments and specifying their coordinates using the Lucas-Kanade point tracking method. The simulation results indicate an increase in speed by almost an order of magnitude and, according to indirect estimates, the accuracy of calculations.
Work in Progress: Foundation of the need for the development of digital skills in Engineering professors

This research aims to support the need for the development of the digital skills of Continental University teachers, based on a series of needs identified in the context of the Fourth Industrial Revolution, in which as a higher education institution it aspires to be a key player. The method presents the results of a knowledge and procedural test on digital topics and tools that the teacher has been using and could use; the structure and questions of this test have been framed in the dimensions understood by the DigCompEdu Framework, finally describing the conclusions and future work that originate from this study.
Toward Basing Cryptography on the Hardness of EXP

Let Kt ( x ) denote the Levin-Kolmogorov Complexity of the string x , and let MKtP denote the language of pairs ( x , k ) having the property that Kt ( x ) ≤ k. We demonstrate that: • MKtP ∉ Heur neg BPP (i.e., MKtP is two-sided error mildly average-case hard) iff infinitely-often OWFs exist. • MKtP ∉ Avg neg BPP (i.e., MKtP is errorless mildly average-case hard) iff EXP ≠ BPP. Taken together, these results show that the only "gap" toward getting (infinitely-often) OWFs from the assumption that EXP ≠ BPP is the seemingly "minor" technical gap between two-sided error and errorless average-case hardness of the MKtP problem.
Research on Bank Card Number Recognition Method Based on Template Matching

In order to improve the effectiveness of mobile payment, automatic identification technology of bank card is proposed. Firstly, a digital template image is created for the bank card, and the image is converted into gray scale, binary conversion, contour extraction, etc. On the basis of the preprocessing, image morphology processing, contour extraction and digital segmentation of the digital image of the bank card, matching the number of the bank card with the template image through the OpenCV computer vision library technology, and the bank card data is recognized by matching method. Finally, through the test of the collected bank card data, the test results show that the correct rate of bank card recognition is up to 95.6%, which meets the requirements of mobile payment bank card binding and improves the user experience of mobile payment.
Blockchain Applications in Digital Construction Supply Chains

This article takes the relevant construction supply chain (CSC) literature of the Web of Science Core Collection Database in the past 20 years and its construction supply chain - blockchain as the object, and then aims to identify and analyze the articles related to the application of blockchain technology in the CSC, and analyze the related problems and potential of the CSC in the application of blockchain technology. An analysis and exploration of a total of 395 articles was conducted. Through VOS viewer software, the author in collaboration network, key term co-occurrence network, and theme classification visualization network of the research field are extracted. The research status of blockchain technology in the CSC is analyzed, and the two key issues of the CSC are focused on through literature reading, so as to explore the application prospects of blockchain technology, and make relevant discussions and some application prospects for the application of blockchain technology in the future.
Transactional Panorama: A Conceptual Framework for User Perception in Analytical Visual Interfaces

Many tools empower analysts and data scientists to consume analysis results in a visual interface. When the underlying data changes, these results need to be updated, but this update can take a long time---all while the user continues to explore the results. Tools can either (i) hide away results that haven't been updated, hindering exploration; (ii) make the updated results immediately available to the user (on the same screen as old results), leading to confusion and incorrect insights; or (iii) present old---and therefore stale---results to the user during the update. To help users reason about these options and others, and make appropriate trade-offs, we introduce Transactional Panorama, a formal framework that adopts transactions to jointly model the system refreshing the analysis results and the user interacting with them. We introduce three key properties that are important for user perception in this context: visibility (allowing users to continuously explore results), consistency (ensuring that results presented are from the same version of the data), and monotonicity (making sure that results don't "go back in time"). Within transactional panorama, we characterize all feasible property combinations, design new mechanisms (that we call lenses) for presenting analysis results to the user while preserving a given property combination, formally prove their relative orderings for various performance criteria, and discuss their use cases. We propose novel algorithms to preserve each property combination and efficiently present fresh analysis results. We implement our framework into a popular, open-source BI tool, illustrate the relative performance implications of different lenses, and demonstrate the benefits of the novel lenses and our optimizations.
A new desorption method of polyurethane foam for the determination of gold and a comparative study on four desorption methods based on meta-analysis

Accurate determination of gold in geological samples is an important prerequisite and guarantee for studying geological problems. There are many methods for digestion and enrichment of gold among which polyurethane foam (PUF) enrichment after aqua regia digestion is the most commonly used in the experiments. A new method to help the relief of gold from the PUF was put forward in this study, and it was applied to four certified reference materials (CRMs) together with three previously used methods, and the optimal extraction and enrichment conditions were determined through experiments. The four methods were compared by meta-analysis, and the thiourea liberation method was superior to the other three methods because of its simple operation and high accuracy. Out of consideration for the incomplete adsorption of gold in the solution by only one piece of PUF, repetitive adsorption of gold with a second and a third piece of PUF in the solution was proposed in this study. Results show that the gold content obtained by secondary and tertiary adsorption accounts for 11.03% of the total content, and the highest can reach 20.74%. When the third adsorption was carried out, the gold content in several samples was below the detection limit. Therefore, repeated adsorption of gold in the solution is necessary, and three times of adsorption is necessary.
Instructional Usability and Learner-User eXperience Assessment in a Virtual Reality Educational Milieu: A Deductive Tech-Instructionality Model from EdTech

Educational Technology (EdTech) lacks a foundational, formal, scientific, epistemic theory. Therefore, it lacks native constructs/variables and an epistemological object of study for scientifically deploying its work. This study determines the existence (ontology) of the theorized constructs Instructional Usability (UsI) and Learner-User eXperience (LUX) and defines their characterization (epistemology). Both constructs were modeled and instrumented. Furthermore, a Tech-Instructionality Model (TIM) was theorized and developed in this paper, both analytically and empirically. The model integrates UsI and LUX as two pairs of constructs linked with two EdTech epistemological objects of study, the instructional interface and the instructional interaction in two assessment modalities, testing mode (user-learner view) and inspection mode (expert/designer view). Two instruments were developed and validated in this study for testing mode, the Instructional Usability Scale (SUsI) and the Learner-User eXperience Questionnaire (QLUX). Both instruments were tested in a non-immersive virtual reality educational milieu during the academic lockdown of the Covid19 pandemic. The results show that both SUsI and QLUX consistently measured UsI and LUX, thus, providing a valid assessment for tech-instructionality and a foundation for constructing a scientific theory of EdTech.
A Machine Learning-Based Approach to Analyze Public Sentiment on Indian New Education Policy 2020

Sentiment analysis is a circular process that helps to understand the evaluation of a text. People are interested in sharing what they know about an event and reading what others say about it in reviews posted to online media outlets. Sentiment analysis (SA) can be used for this sorting purpose. From all the reviews provided by different people, SA pulls out the structured-less text reviews relating to an item survey, an event, and so on and then classifies them as either positive, negative, or neutral. Polarity categorization is another term for this. This research examines and contrasts several machine-learning approaches to SA on the Twitter dataset. Using the NEP2020 Twitter dataset, the results are compared. Results are evaluated using several criteria: accuracy, precision, and recall. Results show that logistic regression outperforms competing machine learning methods.
Boosting Low-Resource Speech Recognition in Air Traffic Communication via Pretrained Feature Aggregation and Multi-Task Learning

Developing a robust Automatic Speech Recognition (ASR) system usually requires a large amount of well-annotated samples which is extremely hard to build in the Air Traffic Control (ATC) due to domain-specific knowledge. In this brief, we present a novel approach to improve ASR performance in the ATC domain by integrating self-supervised learning and multi-task learning into a unified framework. Specifically, the proposed framework follows a two-stage training paradigm, i.e., (a) learning universal acoustic representations by employing the wav2vec 2.0 model and (b) jointly finetuning the model by the ASR, speaker role identification, and language identification tasks. To capture the task-specific representations, an attention-guided feature aggregation module is dedicatedly designed to disentangle the discriminative representations from the pretrained features. In addition, the uncertainty-based loss combination strategy is employed to balance the loss weights for each task in a learnable manner. Finally, we conduct experiments to validate the technical improvements in a real-world ATC dataset. Experimental results demonstrated that the proposed framework outperforms competitive baselines among all tasks.
Applying Albedo Estimation and Implicit Neural Representations to Well-Posed Shape From Shading

We present a method that improves the accuracy of depth maps by combining albedo estimation and implicit neural representations to the well-posed shape from shading. Because the estimation of depth information from a single image is an under-constrained problem, we apply certain physical constrains to convert the ill-posed shape from shading problem to a well-posed problem. Subsequently, we construct an image irradiance equation wherein the surface parameter representing albedo is estimated using a learning-based encoder-decoder network. By solving the equation using implicit neural representations, we can obtain a depth map of the original image. The proposed method achieves an accuracy of depth estimation from a single image with the mean absolute error (MAE) of 0.1510 and root mean square error (RMSE) of 0.1768, indicating superior performance to that of existing methods. Both simulation and real experiments have been carried out to verify the effectiveness of the proposed method.
Autonomous 3D Position Control for a Safe Single Motor Micro Aerial Vehicle

We present the Maneuverable Piccolissimo 2 (MP2), an autonomous, controllable, single motor micro aerial vehicle (MAV). The small size of MP2 makes it safe to operate in the presence of humans, and its simple design facilitates the creation of large swarms of capable MAVs. MP2 is equipped with on-board sensing capabilities and uses active environmental beacons to compute its three-dimensional position and yaw orientation. Its novel design enables autonomous takeoff, flight, and landing while maintaining a small, simple form factor. We describe a feedback controller and demonstrate its feasibility in a series of flight tests that display position holding, step response, and path following capabilities. The results indicate that MP2 is capable of controlled autonomous 3D flight with only one actuator.
FCD-Net: Learning to Detect Multiple Types of Homologous Deepfake Face Images

With the rapid development of artificial intelligence technology, a variety of GAN generated deepfake face images/videos have emerged endlessly. The abuse of deepfake has brought serious negative effects to many industries. Therefore, there is an urgent need to develop advanced methods to combat the abuse of deepfake. As far as we know, there are almost no techniques that can distinguish multiple types of homologous deepfake face images. In this study, we propose a method based on the multi-classification task to address this issue. The proposed method relies on a novel network framework named FCD-Net that consists of the facial synaptic saliency module (FSS), the contour detail feature extraction module (CDFE), and the distinguishing feature fusion module (DFF). Utilizing this method, the imperceptible features introduced by deepfake can be exposed, and the differences caused by different types of deepfake can be distinguished, even if deepfake images are homologous. To test the proposed method and compare it with other SOTA methods, we establish a new homologous dataset named HDFD that contains real face images, entire face synthesis images, face swap images, and facial attribute manipulation images. Among them, the three types of deepfake images are all generated from the same real face images through different deepfake techniques. Abundant experiment results demonstrate that the proposed method has a high-level detection accuracy and relatively strong robustness against content-preserving manipulations. Moreover, the generalization of our method is superior to other SOTA methods.
Practical Product Development Challenges

Despite the many product development techniques available today, manufacturers under pressure to reduce time to market while keeping up with stricter regulatory demands are struggling more than ever with their product development processes.Here, I list a selection of problems and challenges frequently encountered in my consulting practice and checked with literature, as well as practical recommendations for improvement.While in specific situations some project management techniques prove better suited than others, overall product development success seems more dependent on the organization's willingness and ability to learn and adapt rather than on the specific technique chosen.
Graph Convolutional Networks for Designing Collaborative Filtering-Based Health Recommender Systems

Recommender systems provide useful item suggestions (products or services) to users as part of their decision-making processes. The effectiveness of recommender systems is now clearly confirmed in various fields of application (e.g., YouTube, Amazon, Facebook, ResearchGate). In the literature, many research works have addressed the application of recommendations in the field of health in what are called health recommender systems (HRS). HRS is an innovative alternative when it comes to providing information to help doctors in the diagnosis/treatment of diseases, as well as helping patients with recommendations on how to maintain their well-being. However, the proposed development approaches in this field are limited to traditional models that lack the accuracy and effectiveness, which are vital in healthcare. This paper presents a design model for collaborative filtering-based health recommender systems using graph neural networks (GNN) via its promising Graph Convolutional Network (GCN) architecture. In this model, the convolution layer works with a simplified and efficient GCN algorithm named LightGCN. GCN-based methods are among the new cutting-edge approaches in recommender systems, and LightGCN has proven its superiority in recommendation accuracy.
A Survey on Self-adaptation Planning Optimization Techniques

Self-adaptation planning is a challenging task being time and resource consuming. It can be affected by multiple sources of uncertainty as it deals with frequently changing contexts. In addition, it requires real-time information, which is unpredictable at design time, to offer high quality adaptation solution. To deal with these issues, a wide range of studies proposed techniques to optimize the adaptation planning process regarding two aspects namely: planning timeliness and the quality of the provided adaptation solution. However, these two criteria are conflicting in nature i.e. improving the performance of the planning in terms of response time deteriorates the quality of the adaptation solution and vice-versa. Therefore, the adaptation research community witnesses the emergence of multiple studies of which the ultimate goal is to obtain a tradeoff between the two aspects. In this paper, we aim to highlight the key design objectives that affect the planning design and implementation. Moreover, we present the planning optimization techniques proposed in the literature and categorize them to give an understandable view of this specific area of Self-Adaptive Systems (SASs).
New hybrid Particle Swarm and Dragonfly Algorithm for features selection

We are nowadays obligated to deal with rich datasets with exceptionally high dimensions due to big data and IoT. Therefore, a technique known as feature selection (FS) is employed to carry out any machine learning activity or get insights from such dimensions data. One of the most fundamental issues in the analysis of high-dimensional data is feature selection. In this work, we suggest a new approach to the problem of feature selection, which involves selecting a subset of pertinent features for the research topic from a wide number of attributes. To tackle the FS problem, a new bio-inspired algorithm called PSODA is developed in this study, and a novel approach is suggested to maintain a balance between the capacities for exploration and exploitation. The Dragonfly Technique (DA) and the particle swarm optimization (PSO) approach were combined to create the suggested algorithm. Over the most popular datasets in literature, the proposed approach was adequately compared to other algorithms. The outcomes show how PSODA outperforms all other algorithms.
Statistical Tool for Arabic Text

We present here a statistical tool dedicated to the Arabic language. This statistical tool uses encoding from the Unicode standard; the tool was programmed in the MATLAB environment. The statistical processing of the Arabic language constitutes a fundamental step for the realization and analysis of Arabic language corpora dedicated to various fields of application such as: the field of speech synthesis, speech recognition field, and the field of natural language processing. Our system which generates the statistical results related to the Arabic text is essentially based as input on a sequence of the diacritized Arabic text. The latter is transformed into data coded according to the Unicode standard so that the statistical rules base that we have developed can process it. The statistical tool developed provides useful information related to the treated Arabic text such as: number of words, occurrence frequency of each grapheme, and occurrence frequency of syllables "CV/CVV/CVC".