Reviewerly/exHarmony huggingface dataset:
Topic Mining Based Literature Analysis for Face Recognition

Face recognition is a well-known and interesting research topic. Thousands of research articles that focus on face recognition have been published. However, it is time-consuming and labor-intensive to grasp useful information among face recognition-related existing articles. Moreover, most of the existing survey literature provides information about face recognition, focusing only on the recognition technology perspective. This work attempts to use the text mining technique to extract useful information about face recognition from published articles in a life science and medicine database. Specifically, firstly, we collected a large face recognition-related abstract (FRA) dataset in the PubMed database. Secondly, two topic modeling methods, the Latent Dirichlet Allocation (LDA) method and the Non-negative Matrix Factorization (NMF) method, were used to mine useful information from the collected FRA dataset. Finally, the mined results were visualized and analyzed to provide a systematic overview of face recognition literature.
CVR: An Automated CV Recommender System Using Machine Learning Techniques

In the recruitment process, hand-picking the right candidate out of a pool of resumes in the allotted time can be very challenging. Furthermore, as there is no standard pattern for producing CVs, there is always the risk of overlooking crucial information. This research focuses on extracting important information from CVs and job descriptions of various formats, using several machine learning techniques. The extracted data from CVs and job descriptions is then compared to find the best-matched CVs using a variety of metrics such as cosine similarity, soft cosine similarity, Jaccard similarity, dice similarity coefficient, overlap coefficient, and conditional probability. Conditional probability outperforms other metrics and hence is chosen as the metric to assess similarity. Following this, the content-based recommendation technique is used to recommend candidates based on skillset. Currently, our work is limited to computer science, but we plan to extend our work into other sectors in the future.
Detection of Sparsity in Multidimensional Data Using Network Degree Distribution and Improved Supervised Learning with Correction of Data Weighting

Multidimensional data are representatives in a wide range of applications, from those in the latest state-of-the-art science and technology to specific social issues. And they have been subject to analysis using methods such as regression analysis and machine learning. However, they are rarely obtained as complete data and contain more or less biases and deficiencies. In this study, we form a network from a multidimensional dataset and use its degree distribution to detect data sparsity. Although model analysis based on the degree distribution has been conducted for many years, sparsity detection has not been a target of the degree distribution analysis. Furthermore, we attempt to increase the accuracy and precision of supervised learning by applying regressive weighting according to node grouping in the degree distribution spectrum. By making use of this algorithm, we can expand the range of utilization of incomplete data together with other promising progresses in complex networks.
Computational Contextual Framework for Dynamic Tracking of Numerous Mobile Object

The implication of object detection has been witnessing an evolutionary performance improvement since last decade in multimedia processing; however, it is still inflicted with various loopholes due to uncontrolled attributes. Existing literatures shows adoption of quite sophisticated schemes to find successful accomplishment of higher accuracy while computational cost efficiency is less to be reported. Hence, in order to retain this balance, the proposed manuscript introduces a computational framework capable of identifying numerous mobile objects for a dynamic scene. The study model contributes towards a new concept of line of movement, a directionality attribute that contextually connects to every identified moving objects. Further, an optimal estimator is used towards motion tracking to shows better identification performance and cost effectiveness in contrast to existing schemes.
Lev Landau’s Marital Advice Explained

Nobelist physicist Lev Landau was known for applying mathematical and physical reasoning to human relations. His advices may have been somewhat controversial, but they were usually well motivated. However, there was one advice for which no explanation remains—that a person should not marry his/her first and second true loves, and only start thinking about marriage starting with the third true love. In this paper, we provide a possible Landau-style motivation for this advice.
HOW THE NEGATIVE AFFECTIVE RELATION BETWEEN THE SCHOOL COMMUNITY HARMS THE LEARNING OF STUDENTS AT THE STATE SCHOOL PROFESSOR DONIZETTI APARECIDO LEITE

This article addresses the topic: Affectivity and Learning and has as an investigation problem: how can negative affectivity affect student learning? The following research questions are: how does the affective relationship influence learning? How does the negative affectivity with the school community occur? What are the main problems of this school community in relation to affectivity? As a central assumption: negative affectivity impairs students’ learning. The research objectives are: to analyze whether the relationships between the school community are affective. To verify how the negative affective relationship between the school community influences the low levels of student learning. The qualitative and quantitative research was selected in the execution of this research and the case study as an investigation method with the application of a poll with the participation of professors, employees and students.
Cover Trees Revisited: Exploiting Unused Distance and Direction Information

The cover tree (CT) and its improved version are hierarchical data structures that simplified navigating nets while maintaining good runtime guarantees. They can perform nearest neighbor search in logarithmic time and provide efficient computation in practice. In this article, we revisit cover trees for nearest neighbor search, and propose a more competitive method. The central idea of our method is to fully exploit the unused distance and direction information. More specially, our method introduces three novel concepts/techniques: (i) range list, (ii) quadrant information, and (iii) vectorial angle cosine. These techniques are seamlessly integrated into our suggested data structure and search algorithms. As an extra bonus, we explore approximate nearest neighbor and <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> nearest neighbor based on the proposed techniques, and present algorithms for handling updates. Extensive experimental results, based on both real and synthetic datasets, consistently demonstrate that our method is attractive and competitive, compared against existing cover tree structures for nearest neighbor search and its variants.
Validation of the motion sickness severity scale: Secondary analysis of a randomized, double-blind, placebo-controlled study of a treatment for motion sickness

Motion sickness is characterized by nausea and vomiting among a constellation of symptoms. Symptom severity is dynamic and distressing. Most validated motion sickness scales are time-intensive and effortful, with alternative scales having uncertain performance or non-specific measures. A validated instrument allowing for facile, rapid assessment of core motion sickness symptom severity would therefore be valuable. We assessed the performance of the Motion Sickness Severity Scale (MSSS), a six-item questionnaire designed to measure real-time motion sickness symptoms.MSSS construct validity was assessed as a secondary analysis of data from 63 healthy participants without antiemetic treatment in a clinical trial (Unique Identifier = NCT03772340) conducted to evaluate the safety and efficacy of Tradipitant-a novel neurokinin-1 receptor antagonist-in the treatment of motion sickness. Clinical outcome assessments included the MSSS, the Patient Global Impression of Severity (PGI-S), and the Motion Sickness Assessment Questionnaire (MSAQ). The performance of the MSSS through Pearson correlation coefficients, within-group analysis of variance, empirical cumulative distribution functions, and Kolmogorov-Smirnov tests.The MSSS correlated very highly with the PGI-S (r = 0.93, p-value<0.0001) and highly with the MSAQ (r = 0.83, p-value<0.0001). Mean MSSS scores between increasing PGI-S severity levels increased significantly in all four increments (None-to-Mild: p-value = 0.006, Mild-to-Moderate: p-value<0.0001, Moderate-to-Severe: p-value = 0.006, Severe-to-Very-Severe: p-value = 0.002). There were statistically significant differences in MSSS score distributions stratified by PGI-S severity level, with higher MSSS scores associated with higher PGI-S severity levels and lower MSSS scores associated with lower PGI-S severity levels.The MSSS is a valid instrument for the assessment of the core motion sickness symptoms and is reflective of global disease severity. Implementation of the MSSS and comparable simplified, short questionnaires in motion sickness research will provide rapid and accurate measures of disease severity. These measures will enable further elucidation of motion sickness as an illness and inform the development and evaluation of motion sickness therapies.
LMS cluster data analysis for correcting students' learning

This article deals with the analysis of data obtained from databases of learning management systems to organize student-centred learning with the application of course adaptation methods by changing the set of asynchronous and synchronous learning tools. The relevance of the research in this area is due to the current teaching paradigm as well as the increasing volume of digital data available to universities. The aim of the study is to investigate the possibility of effectively influencing the educational process based on the results of data aggregated from learning management systems (LMS) using cluster analysis. The results of the study confirm the possibility of dividing students into clusters based on their success in perceiving synchronous and asynchronous educational methods, and the effectiveness of corrective impact on the respective clusters of students to improve academic performance. As a conclusion the author made a statement about the effectiveness of practical everyday application of learning analytics aimed at person-centered learning.
Development of knowledge control software in graph theory

The manuscript deals with the features of the development of control programs, working with which is similar to performing laboratory work. The development of such programs is based on computer visualization of graphs. To depict the work of algorithms on graphs, different color schemes and different designations are used to convey the processes of manipulation with graph elements. The developed programs provide interactive work with the material and implement learning by action. The article deals with the algorithms "Search in Breadth" and "Search in Depth", the Ford-Bellman shortest path search algorithm, the Terry algorithm for finding a route between two different vertices. When writing programs, object-oriented programming technology was used. Various classes have been created to model the structure of the graph and its various states
Analysis tools for smart contract security

Currently, one of the most pressing issues in the deployment of smart contracts is security. Concerns about inefficiency, security, and dishonest behavior are well-founded, so ignoring them when using the blockchain network to create smart contracts can lead to extremely high additional costs. The article deals with the most common tools for smart contracts auditing. The purpose of this article is to compare the most popular software tools for smart contract security analysis. Smart contracts were used for this purpose that were recognized as secure with a high degree of probability (hereinafter, for simplicity, known as secure or secure smart contracts), as well as vulnerable or simply vulnerable smart contracts. In total, 20 DeFi smart contracts from the Ethereum blockchain network written in the Solidity programming language were used for analysis.
The concept of a single digital educational platform.

A new conceptual approach to the creation of educational information systems is proposed. The relevance is due to a sharp increase in the number of students in IT specialties and an increased workload on teachers. The purpose of the development is to improve the quality of education for IT specialists while preservation an individual approach and attention of the teacher to each student. The developed cross-platform information system is packaged in a web-solution. The system contains the main website and three microservices: a plagiarism checker, a code tester, and an analytics module. The code testing module is a multi-threaded hierarchical system. The uniqueness check module searches for similar solutions to a specific task within the system. The analytical module processes data, including information about the quality of task solving to form a profile of each student. The Django framework was used to develop the server part of the software product. А traditional bundle of HTML + CSS + JS is used for implement the user interface. As a DBMS, PostgreSQL is used, which is implemented in Mospolyteсh, exists in the form of a beta version and corresponds to the modern practice of teaching IT at Russian universities
Virtual Reality Image Dataset vCAT Helps Research on Semantic Segmentation Algorithms

It is difficult to create sample datasets for semantic identification for animals with quick behavior traits, such as cats, and the cost of dataset gathering is greatly increased by high-speed motion capture cameras. This paper takes the common Chinese dragon-li as an example, carries on the solid modeling and the gait movement design in the digital 3D modeling software, then constructs the virtual image dataset vCAT through the image rendering technology. To account for changing elements like the body shape, and scene and camera perspective, the vCAT dataset has numerous data sub-categories. In addition, we accomplished the pixel-level segmentation test of synthetic images and actual scene images using deep learning on the vCAT dataset based on the attention mechanism. During the basis of training, the recognition accuracy rate can reach 84% when only 726 images with real scene modeling were used. In addition to providing a fully automated technical method for creating the original image of the virtual image dataset with its corresponding labels, we also thoroughly test and discuss elements like scenes, perspectives, textures, and lighting in the virtual dataset to serve as a foundation for its practical use in deep learning training.
Data4Good: An Established Framework for supporting Civil Society Organizations

Civil society organisations (CSOs) are critical to the advancement of human well-being, particularly in the Global South and in Small Island Developing States (SIDS). In these developing spaces, civil society's impact often faces barriers due to resource constraints ranging from human capacity to finance. It is well established that data science and Information and Communications Technology (ICT) play a key role in the digital age in improving the efficiency and productivity of for-profit companies. However, such benefits have yet to be accrued in the work of civil society in small developing states as they often lack the resources to use tools like Artificial Intelligence (AI) to improve their operations. Simply providing funding or ICT resources may not solve the problem. We propose a framework in which Data Science resources can be provided to CSOs in such a way that all parties benefit, through an adaptation of the technology stewardship model. The proposed model describes a distributed framework for technology stewardship that comprises a coordinating agency, client and beneficiary civil society organisations, students and expert mentors, that all work together to deliver value to the beneficiary organisations, as well as to each other. This Framework has been trialled with 10 beneficiary civil society organisations in Trinidad and Tobago with exceptional results. We provide details of this framework as well as the outcomes of its first deployment.
Generative adversarial feature learning for glomerulopathy histological classification

Early diagnosis of glomerulopathy is significant for improving disease control and prognosis. Glomerulopathy is diagnosed by pathological images obtained from a renal biopsy, which has many complications and contraindications. In this work, we propose to utilize noninvasive renal ultrasound images to predict the pathological type of glomerulopathy. Due to the difficulty of pathological type recognition on renal ultrasound images and small amounts of annotated data, effective feature learning is more important. Therefore, we employ an adversarial learning approach to generate feature embeddings rather than images. AC-GAN, using conditional image generation and auxiliary classifier, is deployed to synthesize class-related image features. A basic residual network is applied to extract real renal features. After adversarial feature learning, an additional classifier is used for further optimizing and updating the feature extraction network. Through adversarial learning, the feature embeddings extracted by feature extraction network become more informative and discriminative. The t-SNE visualization of extracted features demonstrates that learned features by adversarial feature learning are more discriminative with image categories than others, and this adversarial feature learning architecture achieves 85% accuracy and 0.8542 AUC value, which is better than single classification network and image generation augmentation strategy.
Efficient design methodology for adaptive system based on direct bitstream evolution

An efficient design methodology for adaptive system based on direct configuration bitstream (CB) evolution has been proposed, which models the relationship between resources and CBs of the field programmable gate array (FPGA), and then generates CBs online according to the models built. The system framework and design methodology have been given and verified on Xilinx Virtex-5 FPGA. Results show that it can implement embedded online self-programming of FPGA’s logic and routing resources via modifying CBs directly; and has lower resources overhead, lesser time consumption, and stronger adaptability than the mainstream evolution approaches.
An investigation of the diachronic trend of dependency distance minimization in magazines and news

The principle of minimization of dependency distance (DD) can reduce the working memory burden of language speakers, that is, reduce the cognitive burden during the communication process. This investigation demonstrated the dependency distance (based on the dependency grammar) minimization principle from a diachronic perspective in two text types of magazines and news with inspection indicators of mean dependency distance (MDD) and normalized dependency distance (NDD). This research revealed a fluctuation tendency around a certain axis concerning diachronic dependency distance variation. This research also indicated that news text balances language complexity and communication efficiency better than magazine text.
Thermal Residue-Based Password Attacks and the Ways to Counteract the Same

This article covers possible password attacks, describing thermal residue-based post factum password attacks and the ways to counteract the same, with test results provided as to the possibility of intercepting a password with a thermal imager. In the process of work theoretical material was studied, the methodology of testing the possibility of intercepting passwords, PIN-codes and graphic keys by residual heat trace was developed. The task of testing was that on different models of keyboards, as well as on a smartphone, a set of different combinations of characters, PIN codes and graphic keys was produced, then, using a thermal imager of the UNI-T model UTi260B photographs of the residual thermal trace were taken. The pictures were taken at different distances from the keyboard and after different times of typing the combinations of characters. In the course of this experiment, it was found that it is possible to obtain a sufficiently clear image within one minute after entering the password. However, in order to most successfully reproduce the sequence of the typed characters, the image should be made as early as possible. If the image is taken within fifteen seconds after the password has been entered, success in reproducing the character sequence is achieved in most cases. After thirty seconds, accuracy drops, and after forty-five seconds or more, it becomes increasingly difficult to reproduce the text. In cases of decryption of a graphical key, a similar heat attack makes it possible to reproduce the correct key form even forty seconds after input, as long as the key lines are not superimposed. As a result of the research, methods for counteracting residual heat trace attacks on passwords have been identified and formulated.
Design and Development of an IoT-based Solar Powered Camouflaged Robot for Military Applications

Given Algeria's large area, estimated at 2,381,740 km, and its land borders, estimated at 6,385 km., which made unmanned border control mandatory to reduce the deployment of soldiers and protect the country. Robots play an important role in border protection, especially camouflaged robots, which are difficult for the enemy to detect. Camouflage becomes a survival skill for the robot and a defensive technology at the same time. In this paper, we designed a camouflaged robot in the form of the Fennec animal (Desert Fox), which is widely spread in the Algerian desert, based on the Internet of things and powered by solar energy, which is clean renewable energy available in the desert environment in which the robot will live. The features of the proposed model are that Camouflaged and mimics the funk in both its shape and movement, allowing it to move in this environment smoothly and detect intruders, bombs, mines and fires with its camera and sensors. Solar panels are used to provide power to the model with backup storage batteries, the IoT-based Fennec robot is connected by satellite to a military station. The robot sends information in real time and continuously and streaming video, and is equipped with a GPS sensor to determine its location.
Recommended Criteria for Qualitative Comparison of Fiducial Markers Performance

Fiducial marker (FM) systems have a wide range of applications, such as augmented reality, human-robot interaction, medical surgery, robot navigation, and swarm robotics. Moreover, FMs enable to calibrate optical sensors, which is a crucial step in most computer vision algorithms. A variety of considerations should be taken into account to decide which FM system is suitable for a particular application. This paper proposes a list of recommended criteria that allow to compare FM systems. The criteria support a FM ranking according to their properties and enable users to choose a set of FMs best suited for a specific task. A number of the recommended criteria efficiency and performance were validated in the Gazebo simulation and in a real world environment using Servosila Engineer mobile robot.